Applied AI Engineer Jobs
Applied AI Engineer jobs are open across technology, healthcare, finance, and defense, from new-grad to principal and staff level, with specializations in LLM fine-tuning, MLOps, and multimodal systems. Find a role that fits from the openings below and apply directly.
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Showing 5 of 575+ Applied AI Engineer jobs
About NewsBreak
Founded in 2015, NewsBreak is the Content Intelligence platform shaping the future content economy. With over 40 million monthly active users, our flagship platform delivers highly personalized local news and information powered by advanced AI, recommendation systems, and adtech.
Recognized by Fast Company as #32 on the Top Workplaces for Innovators, we're proud to be Great Place to Work® certified and home to a dynamic team of technologists, product innovators, and business leaders who are passionate about solving meaningful challenges at scale.
Together, we reached unicorn status in 2021, and we remain committed to continuing this high-growth trajectory with the right team to fulfill our mission: building the infrastructure layer for content intelligence.
If you're inspired to dream big, innovate fast, and make a difference, we'd love to hear from you!
About the Role
Are you looking to engage in AI Account Management algorithm work as an excellent engineer to join our advertising team? In this role, you will be responsible for designing, developing, and optimizing an AI-driven intelligent account hosting and optimization platform. You will utilize LLMs, Multimodal Foundation Models, and related techniques to build AI advertising expert systems (AI Agents) that run a closed optimization loop — autonomously diagnosing accounts and safely auto-tuning delivery.
Your work will directly lower the delivery barrier for global advertisers on our platform. While ensuring the health and compliance of the account ecosystem, your intelligent operations will maximize advertisers' willingness to spend and platform revenue.
Responsibilities
- Build In-house AI Account Manager System: Own the algorithm and system design for the core "AI Account Manager" — realizing automated hosting, autonomous optimization, and intelligent scheduling of optimization across many advertiser accounts running concurrently.
- Construct Intelligent Diagnosis and Strategy Recommendation Models: Analyze advertisers' performance data and delivery history to assess account health and automatically diagnose delivery pain points (such as inability to scale volume, sudden drops in ROI, or budget capping), then use LLMs to generate and safely execute actionable, auditable account-level tuning strategies.
- Ensure Reliability and Measurable Quality: Build the long-running services, observability, dry-run safeguards, and offline/online evaluation that keep optimization quality measurable and catch regressions before they reach live spend.
- Cross-team Collaboration to Drive Growth: Work closely with the platform-side ad delivery algorithm team and product managers to transform AI account management capabilities into the platform's core commercial competitiveness, improving advertiser retention rate and overall consumption.
Minimal Qualifications
- Education Background: Bachelor's/Master's degree or higher in Computer Science, Artificial Intelligence, Data Science, or related fields.
- Work Experience:
- Possess a basic understanding of fields such as ad delivery platforms or recommendation systems; fresh graduates are acceptable.
- Familiar with building, deploying, and optimizing agentic systems in production environments.
- Strong software-engineering fundamentals: production-quality code, asynchronous/concurrent programming, typed and data-modeled code, and a solid testing discipline.
- Core Technical Stack:
- Master LLMs, RAG, tool-use, memory handling, and agent orchestration (including MCP-style tool ecosystems).
- Hands-on with harness engineering, structured/schema-validated LLM outputs, and integration with modern model APIs.
- Experience with REST/API integration and building reliable long-running services; familiar with modern deployment tools such as Kubernetes.
- Solid grounding in machine learning and AI fundamentals; familiar with advertising or recommendation system concepts.
- Possess excellent analytical and complex problem-solving abilities.
Preferred Qualifications
- Industry Background: Algorithm experience in the field of ad delivery, with more than 2 years applying large-model capabilities to systems such as automated delivery, automated hosting, autonomous tuning, or automatic scheduling.
- AI Production Experience: Experience landing commercial AI agents (agent-based systems), large-model instruction tuning, or multimodal models.
- Business Acumen: Understanding of digital advertising mechanics and core metrics (CPA, ROAS, CVR, CTR, conversions) and of online A/B experimentation.
What We Offer
- Technical Hard Power Born for Monetization: Participate in using the most cutting-edge Generative AI and Agent technologies to reshape the B-end ad monetization ecosystem under internet scale.
- Direct Commercial Impact: Your algorithm and engineering optimizations will directly reflect on the real growth of advertisers and the platform's revenue flywheel.
- Top-tier Computing Resources and Environment: Access to industry-leading foundation models, large-scale computing infrastructure, and opportunities to work side-by-side with world-class advertising, AI, and infrastructure engineers.
Benefits
We offer a competitive benefits package:
- Health, dental, and vision care for you and your family (100% coverage for employee)
- Top-tier 401(K) plan with company matching
- Paid time off and paid holidays
- FSA, HSA and commuter benefits programs
- Team activity budget
CPRA Privacy Notice for California Candidates
About NewsBreak
Founded in 2015, NewsBreak is the Content Intelligence platform shaping the future content economy. With over 40 million monthly active users, our flagship platform delivers highly personalized local news and information powered by advanced AI, recommendation systems, and adtech.
Recognized by Fast Company as #32 on the Top Workplaces for Innovators, we're proud to be Great Place to Work® certified and home to a dynamic team of technologists, product innovators, and business leaders who are passionate about solving meaningful challenges at scale.
Together, we reached unicorn status in 2021, and we remain committed to continuing this high-growth trajectory with the right team to fulfill our mission: building the infrastructure layer for content intelligence.
If you're inspired to dream big, innovate fast, and make a difference, we'd love to hear from you!
About the Role
Are you looking to engage in AI Account Management algorithm work as an excellent engineer to join our advertising team? In this role, you will be responsible for designing, developing, and optimizing an AI-driven intelligent account hosting and optimization platform. You will utilize LLMs, Multimodal Foundation Models, and related techniques to build AI advertising expert systems (AI Agents) that run a closed optimization loop — autonomously diagnosing accounts and safely auto-tuning delivery.
Your work will directly lower the delivery barrier for global advertisers on our platform. While ensuring the health and compliance of the account ecosystem, your intelligent operations will maximize advertisers' willingness to spend and platform revenue.
Responsibilities
- Build In-house AI Account Manager System: Own the algorithm and system design for the core "AI Account Manager" — realizing automated hosting, autonomous optimization, and intelligent scheduling of optimization across many advertiser accounts running concurrently.
- Construct Intelligent Diagnosis and Strategy Recommendation Models: Analyze advertisers' performance data and delivery history to assess account health and automatically diagnose delivery pain points (such as inability to scale volume, sudden drops in ROI, or budget capping), then use LLMs to generate and safely execute actionable, auditable account-level tuning strategies.
- Ensure Reliability and Measurable Quality: Build the long-running services, observability, dry-run safeguards, and offline/online evaluation that keep optimization quality measurable and catch regressions before they reach live spend.
- Cross-team Collaboration to Drive Growth: Work closely with the platform-side ad delivery algorithm team and product managers to transform AI account management capabilities into the platform's core commercial competitiveness, improving advertiser retention rate and overall consumption.
Minimal Qualifications
- Education Background: Bachelor's/Master's degree or higher in Computer Science, Artificial Intelligence, Data Science, or related fields.
- Work Experience:
- Possess a basic understanding of fields such as ad delivery platforms or recommendation systems; fresh graduates are acceptable.
- Familiar with building, deploying, and optimizing agentic systems in production environments.
- Strong software-engineering fundamentals: production-quality code, asynchronous/concurrent programming, typed and data-modeled code, and a solid testing discipline.
- Core Technical Stack:
- Master LLMs, RAG, tool-use, memory handling, and agent orchestration (including MCP-style tool ecosystems).
- Hands-on with harness engineering, structured/schema-validated LLM outputs, and integration with modern model APIs.
- Experience with REST/API integration and building reliable long-running services; familiar with modern deployment tools such as Kubernetes.
- Solid grounding in machine learning and AI fundamentals; familiar with advertising or recommendation system concepts.
- Possess excellent analytical and complex problem-solving abilities.
Preferred Qualifications
- Industry Background: Algorithm experience in the field of ad delivery, with more than 2 years applying large-model capabilities to systems such as automated delivery, automated hosting, autonomous tuning, or automatic scheduling.
- AI Production Experience: Experience landing commercial AI agents (agent-based systems), large-model instruction tuning, or multimodal models.
- Business Acumen: Understanding of digital advertising mechanics and core metrics (CPA, ROAS, CVR, CTR, conversions) and of online A/B experimentation.
What We Offer
- Technical Hard Power Born for Monetization: Participate in using the most cutting-edge Generative AI and Agent technologies to reshape the B-end ad monetization ecosystem under internet scale.
- Direct Commercial Impact: Your algorithm and engineering optimizations will directly reflect on the real growth of advertisers and the platform's revenue flywheel.
- Top-tier Computing Resources and Environment: Access to industry-leading foundation models, large-scale computing infrastructure, and opportunities to work side-by-side with world-class advertising, AI, and infrastructure engineers.
Benefits
We offer a competitive benefits package:
- Health, dental, and vision care for you and your family (100% coverage for employee)
- Top-tier 401(K) plan with company matching
- Paid time off and paid holidays
- FSA, HSA and commuter benefits programs
- Team activity budget
CPRA Privacy Notice for California Candidates
See All 575+ Applied AI Engineer Jobs
Jump back to the full list of openings and apply to any applied AI engineer role that fits.
Find Applied AI Engineer JobsApplied AI Engineer Job Market
A snapshot from current openings nationwide, updated as new roles post.
Who's Hiring
- Amazon181
- Apple33
- Adobe20
- JPMorganChase19
- TikTok19
Top Industries Hiring
- Technology & Software182
- Electronics & Hardware50
- Banking & Financial Services42
- Consulting & Professional Services32
- Science & Research31
What Employers Look For
The qualifications that appear most often in applied AI engineer jobs.
- Proficiency in Python with hands-on experience building and deploying machine learning models
- Experience with large language models and frameworks such as PyTorch, Hugging Face, or LangChain
- Familiarity with MLOps tooling including model versioning, monitoring, and CI/CD pipelines
- Bachelor's or master's degree in computer science, machine learning, or a closely related field
- Experience integrating AI models into production software systems via APIs or microservices
- Working knowledge of cloud platforms such as AWS, Google Cloud, or Azure for model serving
Tips for Your Applied AI Engineer Job Search
Tailor your resume to production depth
Hiring managers distinguish candidates who have deployed models in production from those who have only run experiments. Call out inference latency improvements, cost reductions, or uptime metrics from systems you owned end to end, not just notebooks you built.
Show your model evaluation methodology
Interviewers for applied AI roles probe how you measure whether a model actually works in the real world. Document your evals framework, the failure modes you caught before launch, and how you iterated, so you can speak to this concisely and specifically.
Target roles by tech stack, not just title
Applied AI engineer openings vary widely by stack. Filter by the tools you know best, whether that's LangChain, vLLM, or PyTorch with CUDA, so you apply where your day-one contribution is clearest to the hiring team reading your resume.
Apply early to roles that fit
Migrate Mate lists applied ai engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prep a system design answer for AI pipelines
Most applied AI interviews include a system design round specific to ML infrastructure. Practice walking through prompt chaining, retrieval-augmented generation retrieval flow, or batch inference architecture out loud, focusing on trade-offs rather than a single correct answer.
Negotiate on compute access, not just compensation
Applied AI engineers often find that GPU budgets and model API access affect their day-to-day more than title. Before accepting an offer, ask directly what compute resources the team uses and how new projects get resourced, so you can assess the role accurately.
Applied AI Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most applied ai engineers?
The companies hiring the most applied ai engineers right now include Amazon, Apple, and Adobe, with the largest share of openings in California, Washington, and New York, based on current listings on Migrate Mate as of June 2026. Demand is concentrated at companies actively building or integrating AI-powered products rather than conducting pure research.
How many applied ai engineer jobs are remote?
About 21% of applied ai engineer openings are fully remote or hybrid as of June 2026, making this one of the more flexible engineering roles in the market. Sub-areas with the highest remote share include prompt engineering, LLM integration, and retrieval-augmented generation work, where collaboration with on-site hardware is rarely required.
How do you become an applied ai engineer?
Build a strong foundation in Python and machine learning fundamentals, then move into hands-on projects that take a model from training through deployment. Focus on production experience, including API integration, monitoring, and evaluation frameworks. Contributing to open-source AI tooling and publishing documented project work signals to employers that you can ship, not just prototype.
Can you get hired as an applied ai engineer with little experience?
Yes, entry-level applied AI roles exist, but employers expect demonstrable project work even without professional experience. Build public repositories showing end-to-end pipelines, fine-tuned models, or RAG implementations with documented evals. Roles at startups building AI-native products and companies expanding AI teams often have more flexibility on years of experience than established enterprises do.
What does the applied ai engineer interview process look like?
Most processes include a recruiter screen, a technical phone interview covering Python and ML fundamentals, a take-home or live coding exercise focused on building or evaluating a model pipeline, and a system design round specific to AI architecture. Final rounds often include a presentation of past project work and a conversation about how you measure model quality in production.
Where can I find and apply to applied ai engineer jobs?
You can find and apply to applied ai engineer jobs on Migrate Mate, which lists current openings from across the United States. Search the listings to find roles that match your skills and experience level, then apply directly to each one that fits.
See All 575+ Applied AI Engineer Jobs
Jump back to the full list of openings and apply to any applied AI engineer role that fits.
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