Remote Director of Artificial Intelligence Jobs
Remote Director of Artificial Intelligence jobs are in strong demand across the U.S., with remote-first firms and distributed technology, healthcare, and financial services teams actively hiring. Employers posting remote roles right now include DataAnnotation, University of San Diego, and MEM. Find a role that fits below and apply directly.
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Showing 5 of 44+ Remote Director of Artificial Intelligence jobs
Description
Southern New Hampshire University is a team of innovators. World changers. Individuals who believe in progress with purpose. Since 1932, our people-centered strategy has defined us — and helped us grow a team that now serves over 180,000 learners worldwide.
Our mission to transform lives is made possible by talented people who bring diverse industry experience, backgrounds and skills to the university. And today, we're ready to expand our reach. All we need is you.
Make an impact — from near or far
At SNHU, you'll have the option to work remotely in the following states: Alabama, Arizona, Arkansas, Delaware, Florida, Georgia, Hawaii, Idaho, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Mississippi, Missouri, Nebraska, New Hampshire, New Mexico, North Carolina, North Dakota, Ohio, Oklahoma, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, West Virginia, Wisconsin and Wyoming.
We ask that our remote employees have access to a reliable internet connection and a dedicated, properly equipped workspace that is free of distractions. Employees must reside in, and work from, one of the above approved states.
The opportunity
The Director of AI Solutions Partners will report directly to the Assistant Vice President of AI Engineering and will lead the discovery and solution definition function for SNHU's AI engineering capability. Reporting to the AVP, AI Engineering, the Director will own how SNHU identifies its highest-leverage AI problems and opportunities and turns them into production-grade solutions. The team collaborates with partner teams across the university, carrying a promising idea from the first conversation through to a clearly stated problem, a validated case for building, and a proof-of-concept level feature set an AI Product Pod can deliver.
This is a player-coach leadership role with people-management responsibility. The Director will build and lead the AI Solutions Partner function, the discovery and solution-shaping role in every pod, and work alongside engineering and delivery leadership within AI Engineering so that definition and delivery stay connected. This position will also lead pods directly, particularly the newest and most demanding ones, keeping their own practice current with the standard set for the team.
AI Product Pods are how SNHU builds with AI: small, forward-deployed teams that embed with a partner team, build alongside them on their real problems, and leave them measurably more AI-capable than they found them. A single pod drives one initiative, and the number of active pods is growing. The Director's accountability spans the discovery pipeline that feeds every pod and the practice that keeps problem definition rigorous as the portfolio grows.
The role involves:
- Discovery and solution definition: Own the path from a promising opportunity to a clearly defined problem, a validated case for building, and a solution shape a pod can deliver, and sequences that pipeline against available pod capacity.
- Building the discipline: Hire, develop, and lead the AI Solutions Partners who do this work inside pods, and set the standard for discovery, problem definition, requirements, and prototyping across the AI organization.
- Partnership across SNHU: Work with AI engineering and delivery leadership, Domain Product Owners, AI Operations and Governance, AI Portfolio Operations, and ITS to advance a build culture in which the teams closest to a problem can specify and prototype solutions themselves.
You will work 100% remotely. #LI-Remote
What You'll Do:
Discovery and solution definition:
- Own the initial stages of the pod lifecycle: opportunity identification, discovery interviews, workflow analysis, and opportunity assessment, through to a problem definition and solution shape an engineering team can act on. Go looking for SNHU's highest-leverage problems and opportunities rather than waiting for them to arrive.
- Define the standard for a complete problem definition: the problem, the outcome it serves, the evidence behind both, the data and access the solution depends on, a proof-of-concept level feature set with acceptance criteria, and what scaling would involve if the proof of concept succeeds.
- Run intake and triage with AI Engineering leadership and AI Portfolio Operations: what enters the pipeline, what receives a pod, what is deferred, what is discontinued, and in what order against available capacity.
- Own the opportunity portfolio and support its prioritization with evidence when competing demands exceed capacity.
- Advise clearly when AI is not the right tool, when an existing system or team already addresses the problem, or when a process change would serve better than a build, so that pods stay focused on work that genuinely requires AI.
- Partner with AI engineering leadership to define outcomes and evaluation criteria for each initiative, so that delivered work is measured against what it was intended to achieve.
Leading the AI Solutions Partner discipline:
- Hire, develop, and lead a team of AI Solutions Partners; own their performance, growth, and career development.
- Set the standard for discovery, problem definition, requirements, prototyping, and user validation, at a level of quality that allows pods to hand off cleanly and later teams to reuse what was learned.
- Build shared practice assets: discovery playbooks, interview and workflow-mapping guides, opportunity assessment and triage templates, requirements patterns, and practice documentation that lift consistency across the function.
- Ensure what pods learn reaches the shared AI Engineering platform and practice rather than staying with the partner who learned it, so that each pod starts from more than the last one did.
- Allocate AI Solutions Partners across pods and initiatives, balancing continuity for host teams against the concurrent load any one partner can carry well.
- Build durable internal capability: a function that is staffed, capable, and not dependent on any one individual, so that capacity grows with the portfolio.
- Mentor across the AI organization and the wider institution on problem definition, discovery, and outcome ownership.
Leading the work directly:
- Personally lead discovery and solution definition on the most complex and highest-stakes initiatives, including the first pod in any new domain.
- Build prototypes, agent workflows, and working demonstrations that make an idea tangible quickly and reduce risk before a full build is funded.
- Stay hands-on with current AI tooling and agentic frameworks and contribute technically rather than only oversee engage with code, reason about architecture, and shape agent and solution design with the engineers who own the build, informing the feasibility, model, cost, and latency trade-offs that determine what gets built.
- Pod operating model and collaboration
- Own the definition side of the pod model: what gets built and why, with engineering owning how it is built and operated, and joint accountability to the AVP, AI Engineering for whether the pod model delivers.
- Work with Domain Product Owners, Business Sponsors, and host-team leadership on what to build and how it fits the operational reality of the teams who own the problems.
- Ensure pods operate effectively alongside the host team, whatever its shape: account for the teams, platforms, and solutions already in place, and design for the host team to sustain what ships after the pod concludes.
- Contribute to checkpoint reviews across the pod lifecycle, ensuring that leadership decisions are informed, evidenced, and documented.
- Surface definition and scope risk early and make scoping and sequencing decisions promptly.
Pod operating model and collaboration
- Own the definition side of the pod model: what gets built and why, with engineering owning how it is built and operated, and joint accountability to the AVP, AI Engineering for whether the pod model delivers.
- Work with Domain Product Owners, Business Sponsors, and host-team leadership on what to build and how it fits the operational reality of the teams who own the problems.
- Ensure pods operate effectively alongside the host team, whatever its shape: account for the teams, platforms, and solutions already in place, and design for the host team to sustain what ships after the pod concludes.
- Contribute to checkpoint reviews across the pod lifecycle, ensuring that leadership decisions are informed, evidenced, and documented.
- Surface definition and scope risk early and make scoping and sequencing decisions promptly.
Partnering across SNHU:
- Partner with SNHU AI Operations and Governance on governance, safety, evaluation, and adoption as they relate to the opportunity portfolio. AI Operations own AI governance: the Director designs solutions that are safe, ethical, and human-focused.
- Coordinate with ITS and data leadership on the infrastructure, access, environments, and systems of record that discovery and proof-of-concept work depend on.
- Engage executive and academic stakeholders: translate broad institutional priorities into defined problems, and report progress in terms leadership can act on.
- Spread the craft beyond the AI organization: build the practices, playbooks, and shared judgment that let teams across SNHU apply AI engineering in their own work.
What We're Looking For:
- 7+ years in product management, product strategy, AI or digital strategy, experience design, or a related discipline, including taking ambiguous problems through to shipped, production software. We hire judgment and outcomes, not a specific domain.
- 2+ years leading, developing, or formally mentoring practitioners, including building or growing a team or function.
- Bachelor Degree in relevant field and will consider equivalent experience in lieu of degree.
- Demonstrated record of leading discovery end to end stakeholder interviews, workflow analysis, opportunity assessment, and the translation of all of it into requirements an engineering team can build from.
- Working command of applied AI, including LLMs, retrieval, agentic systems, and evaluation, with the technical depth to contribute alongside AI engineers rather than only evaluate them: able to engage with code (Python, JavaScript, or TypeScript), reason about architecture, and shape solution design.
- Applied experience with AI tools and agentic frameworks as a core part of how the work gets done, including building prototypes independently (for example Claude and Claude Code, Microsoft Copilot, Cursor, LangGraph, AWS Strands, or the Vercel AI SDK).
- Practical experience defining outcomes and the measures that demonstrate whether delivered work succeeded.
- Experience translating ambiguous domain problems into delivered solutions alongside non-technical stakeholders, including the judgment to scope tightly, assess build versus buy, and recognize when AI is not the right tool for the problem.
- Strong written and verbal communication: executive-level narratives, requirements that hold up through delivery, and direct, well-evidenced counsel.
Preferred Qualifications:
- Education technology or higher education systems including or personalized learning products.
- Experience standing up a discovery, strategy, or product function from the ground up.
- Prior work inside small, embedded or forward-deployed teams that crossed organizational lines, in a consulting, agency, or internal capability setting.
- Design or user experience practice alongside product judgment, to shape how a solution works for the people who will use it.
- AI evaluation treated as a product concern: defining the expected behavior and quality standard for an AI system and collaborating with engineers to instrument for it.
- Experience transferring capability to a host team through teaching, enablement, and documentation.
We believe real innovation comes from inclusion - where different experiences, perspectives and talents are celebrated. So if you're wondering whether SNHU is right for you, take the leap and apply. You might be just the person we're looking for.
If you have a disability and require a reasonable accommodation to fully engage in any part of the application or hiring process, please complete the Accommodation Request form or contact us at 603-626-9447. A member from the Employee Accommodation Support Center will be in contact with you within two business days to discuss your request.
Compensation
The annual pay range for this position is $96,954.00 - $155,153.00. Actual offer will be based on skills, qualifications, experience and internal equity, in addition to relevant business considerations. We expect this position to be hired in the following target hiring range $107,132.00 - $144,945.00.
Exceptional benefits (because you’re exceptional)
You’re the whole package. Your benefits should be, too. As a full-time employee at SNHU, you’ll get:
High-quality, low-deductible medical insurance
Low to no-cost dental and vision plans
5 weeks of paid time off (plus almost a dozen paid holidays)
Employer-funded retirement
Free tuition program
Parental leave
Mental health and wellbeing resources
Description
Southern New Hampshire University is a team of innovators. World changers. Individuals who believe in progress with purpose. Since 1932, our people-centered strategy has defined us — and helped us grow a team that now serves over 180,000 learners worldwide.
Our mission to transform lives is made possible by talented people who bring diverse industry experience, backgrounds and skills to the university. And today, we're ready to expand our reach. All we need is you.
Make an impact — from near or far
At SNHU, you'll have the option to work remotely in the following states: Alabama, Arizona, Arkansas, Delaware, Florida, Georgia, Hawaii, Idaho, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Mississippi, Missouri, Nebraska, New Hampshire, New Mexico, North Carolina, North Dakota, Ohio, Oklahoma, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, West Virginia, Wisconsin and Wyoming.
We ask that our remote employees have access to a reliable internet connection and a dedicated, properly equipped workspace that is free of distractions. Employees must reside in, and work from, one of the above approved states.
The opportunity
The Director of AI Solutions Partners will report directly to the Assistant Vice President of AI Engineering and will lead the discovery and solution definition function for SNHU's AI engineering capability. Reporting to the AVP, AI Engineering, the Director will own how SNHU identifies its highest-leverage AI problems and opportunities and turns them into production-grade solutions. The team collaborates with partner teams across the university, carrying a promising idea from the first conversation through to a clearly stated problem, a validated case for building, and a proof-of-concept level feature set an AI Product Pod can deliver.
This is a player-coach leadership role with people-management responsibility. The Director will build and lead the AI Solutions Partner function, the discovery and solution-shaping role in every pod, and work alongside engineering and delivery leadership within AI Engineering so that definition and delivery stay connected. This position will also lead pods directly, particularly the newest and most demanding ones, keeping their own practice current with the standard set for the team.
AI Product Pods are how SNHU builds with AI: small, forward-deployed teams that embed with a partner team, build alongside them on their real problems, and leave them measurably more AI-capable than they found them. A single pod drives one initiative, and the number of active pods is growing. The Director's accountability spans the discovery pipeline that feeds every pod and the practice that keeps problem definition rigorous as the portfolio grows.
The role involves:
- Discovery and solution definition: Own the path from a promising opportunity to a clearly defined problem, a validated case for building, and a solution shape a pod can deliver, and sequences that pipeline against available pod capacity.
- Building the discipline: Hire, develop, and lead the AI Solutions Partners who do this work inside pods, and set the standard for discovery, problem definition, requirements, and prototyping across the AI organization.
- Partnership across SNHU: Work with AI engineering and delivery leadership, Domain Product Owners, AI Operations and Governance, AI Portfolio Operations, and ITS to advance a build culture in which the teams closest to a problem can specify and prototype solutions themselves.
You will work 100% remotely. #LI-Remote
What You'll Do:
Discovery and solution definition:
- Own the initial stages of the pod lifecycle: opportunity identification, discovery interviews, workflow analysis, and opportunity assessment, through to a problem definition and solution shape an engineering team can act on. Go looking for SNHU's highest-leverage problems and opportunities rather than waiting for them to arrive.
- Define the standard for a complete problem definition: the problem, the outcome it serves, the evidence behind both, the data and access the solution depends on, a proof-of-concept level feature set with acceptance criteria, and what scaling would involve if the proof of concept succeeds.
- Run intake and triage with AI Engineering leadership and AI Portfolio Operations: what enters the pipeline, what receives a pod, what is deferred, what is discontinued, and in what order against available capacity.
- Own the opportunity portfolio and support its prioritization with evidence when competing demands exceed capacity.
- Advise clearly when AI is not the right tool, when an existing system or team already addresses the problem, or when a process change would serve better than a build, so that pods stay focused on work that genuinely requires AI.
- Partner with AI engineering leadership to define outcomes and evaluation criteria for each initiative, so that delivered work is measured against what it was intended to achieve.
Leading the AI Solutions Partner discipline:
- Hire, develop, and lead a team of AI Solutions Partners; own their performance, growth, and career development.
- Set the standard for discovery, problem definition, requirements, prototyping, and user validation, at a level of quality that allows pods to hand off cleanly and later teams to reuse what was learned.
- Build shared practice assets: discovery playbooks, interview and workflow-mapping guides, opportunity assessment and triage templates, requirements patterns, and practice documentation that lift consistency across the function.
- Ensure what pods learn reaches the shared AI Engineering platform and practice rather than staying with the partner who learned it, so that each pod starts from more than the last one did.
- Allocate AI Solutions Partners across pods and initiatives, balancing continuity for host teams against the concurrent load any one partner can carry well.
- Build durable internal capability: a function that is staffed, capable, and not dependent on any one individual, so that capacity grows with the portfolio.
- Mentor across the AI organization and the wider institution on problem definition, discovery, and outcome ownership.
Leading the work directly:
- Personally lead discovery and solution definition on the most complex and highest-stakes initiatives, including the first pod in any new domain.
- Build prototypes, agent workflows, and working demonstrations that make an idea tangible quickly and reduce risk before a full build is funded.
- Stay hands-on with current AI tooling and agentic frameworks and contribute technically rather than only oversee engage with code, reason about architecture, and shape agent and solution design with the engineers who own the build, informing the feasibility, model, cost, and latency trade-offs that determine what gets built.
- Pod operating model and collaboration
- Own the definition side of the pod model: what gets built and why, with engineering owning how it is built and operated, and joint accountability to the AVP, AI Engineering for whether the pod model delivers.
- Work with Domain Product Owners, Business Sponsors, and host-team leadership on what to build and how it fits the operational reality of the teams who own the problems.
- Ensure pods operate effectively alongside the host team, whatever its shape: account for the teams, platforms, and solutions already in place, and design for the host team to sustain what ships after the pod concludes.
- Contribute to checkpoint reviews across the pod lifecycle, ensuring that leadership decisions are informed, evidenced, and documented.
- Surface definition and scope risk early and make scoping and sequencing decisions promptly.
Pod operating model and collaboration
- Own the definition side of the pod model: what gets built and why, with engineering owning how it is built and operated, and joint accountability to the AVP, AI Engineering for whether the pod model delivers.
- Work with Domain Product Owners, Business Sponsors, and host-team leadership on what to build and how it fits the operational reality of the teams who own the problems.
- Ensure pods operate effectively alongside the host team, whatever its shape: account for the teams, platforms, and solutions already in place, and design for the host team to sustain what ships after the pod concludes.
- Contribute to checkpoint reviews across the pod lifecycle, ensuring that leadership decisions are informed, evidenced, and documented.
- Surface definition and scope risk early and make scoping and sequencing decisions promptly.
Partnering across SNHU:
- Partner with SNHU AI Operations and Governance on governance, safety, evaluation, and adoption as they relate to the opportunity portfolio. AI Operations own AI governance: the Director designs solutions that are safe, ethical, and human-focused.
- Coordinate with ITS and data leadership on the infrastructure, access, environments, and systems of record that discovery and proof-of-concept work depend on.
- Engage executive and academic stakeholders: translate broad institutional priorities into defined problems, and report progress in terms leadership can act on.
- Spread the craft beyond the AI organization: build the practices, playbooks, and shared judgment that let teams across SNHU apply AI engineering in their own work.
What We're Looking For:
- 7+ years in product management, product strategy, AI or digital strategy, experience design, or a related discipline, including taking ambiguous problems through to shipped, production software. We hire judgment and outcomes, not a specific domain.
- 2+ years leading, developing, or formally mentoring practitioners, including building or growing a team or function.
- Bachelor Degree in relevant field and will consider equivalent experience in lieu of degree.
- Demonstrated record of leading discovery end to end stakeholder interviews, workflow analysis, opportunity assessment, and the translation of all of it into requirements an engineering team can build from.
- Working command of applied AI, including LLMs, retrieval, agentic systems, and evaluation, with the technical depth to contribute alongside AI engineers rather than only evaluate them: able to engage with code (Python, JavaScript, or TypeScript), reason about architecture, and shape solution design.
- Applied experience with AI tools and agentic frameworks as a core part of how the work gets done, including building prototypes independently (for example Claude and Claude Code, Microsoft Copilot, Cursor, LangGraph, AWS Strands, or the Vercel AI SDK).
- Practical experience defining outcomes and the measures that demonstrate whether delivered work succeeded.
- Experience translating ambiguous domain problems into delivered solutions alongside non-technical stakeholders, including the judgment to scope tightly, assess build versus buy, and recognize when AI is not the right tool for the problem.
- Strong written and verbal communication: executive-level narratives, requirements that hold up through delivery, and direct, well-evidenced counsel.
Preferred Qualifications:
- Education technology or higher education systems including or personalized learning products.
- Experience standing up a discovery, strategy, or product function from the ground up.
- Prior work inside small, embedded or forward-deployed teams that crossed organizational lines, in a consulting, agency, or internal capability setting.
- Design or user experience practice alongside product judgment, to shape how a solution works for the people who will use it.
- AI evaluation treated as a product concern: defining the expected behavior and quality standard for an AI system and collaborating with engineers to instrument for it.
- Experience transferring capability to a host team through teaching, enablement, and documentation.
We believe real innovation comes from inclusion - where different experiences, perspectives and talents are celebrated. So if you're wondering whether SNHU is right for you, take the leap and apply. You might be just the person we're looking for.
If you have a disability and require a reasonable accommodation to fully engage in any part of the application or hiring process, please complete the Accommodation Request form or contact us at 603-626-9447. A member from the Employee Accommodation Support Center will be in contact with you within two business days to discuss your request.
Compensation
The annual pay range for this position is $96,954.00 - $155,153.00. Actual offer will be based on skills, qualifications, experience and internal equity, in addition to relevant business considerations. We expect this position to be hired in the following target hiring range $107,132.00 - $144,945.00.
Exceptional benefits (because you’re exceptional)
You’re the whole package. Your benefits should be, too. As a full-time employee at SNHU, you’ll get:
High-quality, low-deductible medical insurance
Low to no-cost dental and vision plans
5 weeks of paid time off (plus almost a dozen paid holidays)
Employer-funded retirement
Free tuition program
Parental leave
Mental health and wellbeing resources
See All 44 Remote Director of Artificial Intelligence Jobs
Find roles that match your experience and apply in just a few clicks.
Find JobsRemote Director of Artificial Intelligence Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software
- Education
- Insurance
- Automotive
What Employers Look For
The qualifications that appear most often in remote director of artificial intelligence jobs.
- 10 or more years of experience in machine learning or applied AI roles
- Proven leadership of cross-functional AI or data science teams
- Proficiency with frameworks such as TensorFlow, PyTorch, or equivalent
- Experience deploying large-scale ML models in production environments
- Master's or doctoral degree in computer science, AI, or a related field
- Familiarity with AI governance, model risk management, and responsible AI practices
Tips for Your Remote Director of Artificial Intelligence Job Search
Show async leadership in your application materials
Remote employers want proof you can lead without a conference room. Include written examples of AI strategy documents, decision memos, or recorded technical reviews you've produced. Candidates who demonstrate clear async communication habits get screened in faster than those who list soft skills without evidence.
Apply early to remote roles that fit
Migrate Mate lists remote director of artificial intelligence openings from across the U.S. in one place, so you can find roles that match your background and apply directly without sorting through location-filtered results on general boards.
Build a portfolio of production AI systems
Remote hiring managers for AI director roles want to see systems you have owned end-to-end, not slides about AI strategy. Document the architecture decisions, team structure, and business outcomes for two or three deployed AI products you have led, and make that documentation easy to share.
Prepare for remote-first technical interviews
Remote director interviews typically include a live system design session over a shared whiteboard tool and an async take-home assessing your written technical judgment. Practice articulating tradeoffs in MLOps architecture, model governance, and AI team scaling in writing, not just verbally.
Remote Director of Artificial Intelligence Jobs: Frequently Asked Questions
How do I get a remote director of artificial intelligence job?
Remote director of artificial intelligence roles go to candidates who can lead distributed teams, communicate strategy clearly in writing, and drive AI programs without in-person oversight. Employers screen for experience managing cross-functional remote teams, hands-on fluency with modern AI and ML frameworks, and a history of shipping production AI systems. A public portfolio of technical projects, strong async written communication, and demonstrated ability to align stakeholders across time zones give you a clear edge.
Which companies hire remote director of artificial intelligences?
Remote director of artificial intelligence roles are posted by DataAnnotation, University of San Diego, and MEM and others right now, based on current remote listings on Migrate Mate as of November 2026. Remote-first technology companies, distributed SaaS platforms, and enterprise software firms with global teams are among the most active hirers for this role.
Can you get a remote director of artificial intelligence job with no experience?
Yes, but remote director-level AI roles are rare for candidates without prior leadership experience, since remote work requires you to manage teams and deliver results independently from day one. Early-career candidates can build toward this by leading open-source AI projects, contributing to distributed research teams, or taking on AI lead roles at smaller remote-first startups where ownership is broader and progression is faster.
Do you need a degree for remote director of artificial intelligence jobs?
Not always. Many remote employers weigh a strong portfolio of shipped AI systems, measurable business impact, and demonstrated technical depth over formal credentials. That said, a graduate degree in computer science, machine learning, or a related field remains common at larger organizations. What consistently matters most remotely is evidence you can lead AI strategy, mentor technical teams, and deliver results without day-to-day supervision.
Which industries hire the most remote director of artificial intelligences?
The sectors hiring the most remote director of artificial intelligences are Technology & Software, Education, and Insurance, based on current remote listings on Migrate Mate as of November 2026. These sectors rely on distributed engineering and data teams, making remote AI leadership a practical and common organizational structure.
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