Remote AI Product Owner Jobs
Remote AI Product Owner jobs are in steady demand across the U.S., with remote-first firms and distributed product teams actively hiring for roles that sit at the intersection of machine learning systems and user-facing products. Companies across software, fintech, healthcare technology, and enterprise SaaS are bringing on remote ai product owners to drive AI roadmaps without a physical office requirement. Employers hiring remotely right now include Humana, Cwill, and ServiceNow. Find a role that fits below and apply directly.
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About Positron AI
Positron AI is building next-generation AI inference accelerators designed from the ground up for low-latency, high-throughput large language model inference. Our first-generation ASIC, Asimov, is a cutting-edge accelerator targeting frontier AI workloads, with additional generations already underway.
Role Overview
Positron AI is looking for a Technical Product Manager to own AI inference and software technical product planning end to end. In this role, you will be the person who translates where models and inference systems are heading into concrete, well-scoped requirements for our inference software stack, spanning model coverage, numerics, inference-engine features and modes, serving-stack capabilities, and our managed service.
This is a deeply technical planning role that sits at the intersection of engineering, go-to-market, and the broader inference ecosystem. You will track the model frontier as a discipline, convert that movement into engineering requests before it becomes a customer escalation, and serve as the connective tissue between our engineering organization, our GTM teams, and our ecosystem partners. You will also be expected to use agentic AI daily as a core part of how the planning function operates.
Key Responsibilities
Product Requirements and Roadmap
- Serve as the leader at Positron for all aspects of AI inference and software technical product planning.
- Write requirements for the inference software stack spanning model coverage, numerics, inference-engine features and modes, serving-stack features and modes, and managed-service capabilities.
- Partner with engineering and GTM to build and communicate a clear, defensible roadmap.
- Create scope and feasibility frameworks that convert model and inference-system innovations into tangible engineering requests.
Market and Frontier Tracking
- Keep planning ahead of where models and inference systems are going, tracking the model frontier as a discipline and converting movement into requirements before it surfaces as a customer escalation.
- Work closely with GTM teams to understand customer and market needs and feed them back into the roadmap.
- Create competitive briefings covering inference providers, serving stacks, and adjacent hardware platforms.
- Engage key ecosystem partners, including model labs, open-source runtimes, and serving and orchestration partners, to understand their technology roadmaps.
Lifecycle, Documentation, and Process
- Define and manage the software product lifecycle, including versions and release trains, feature modes, model catalog, and deprecation policy.
- Ensure our software products are well documented for both internal and customer-facing audiences.
- Streamline and automate the product planning process using agentic AI.
Required Qualifications
- 10+ years of experience across ML systems, inference infrastructure, or serving-stack engineering, with direct ownership of performance or architecture trade-offs.
- Deep expertise in transformer internals at the operator level, including attention variants, MoE routing, KV-cache mechanics, and quantization formats along with their hardware implications.
- Hands-on experience with production inference serving at scale, covering multi-tenancy, latency SLAs such as TTFT and TPOT, batching and scheduling, disaggregated serving, KV-cache management, and observability.
- Working fluency in the open-source inference ecosystem, including vLLM and SGLang-class runtimes, kernels, model ingestion, and how models are released, quantized, and adopted in practice.
- Strong performance analysis skills spanning models and systems, including utilization reasoning, tokens per dollar and tokens per watt arithmetic, and benchmark design, with the ability to build and defend the math personally.
- Demonstrated experience in competitive landscaping and analysis of inference providers and serving stacks, gained at a model lab, an inference API provider, or an AI hardware company.
- A proven ability to learn quickly and span the full stack, from model-architecture details up to fleet-scale serving systems, while staying current with the model and inference landscape.
- Excellent communication and interpersonal skills, with comfort navigating uncertainty and driving a process of idea and decision socialization.
- Confidence being the most technically grounded person in a GTM room and the most market-aware person in an engineering room.
- A strong instinct for owning decision history, serving as the documented answer to "why did we choose X," including with executive leadership.
- Daily, hands-on use of agentic AI in real technical work, building and running agent workflows for research, analysis, and requirements drafting, with the judgment to verify and own everything the agents produce.
Preferred Qualifications
- Prior experience at an AI hardware or custom silicon company, with exposure to the realities of bringing a new accelerator platform to market.
- Direct contribution to or close engagement with open-source inference runtimes or serving projects.
- Experience defining and operating a managed inference service, including model catalog and deprecation policy.
- A track record of building internal automation or agentic workflows that measurably improved a planning or research function.
Leveling & Scope
While this role is currently posted at a specific level, we are a growth-oriented organization and are open to hiring at a more senior level for the right candidate. Please note that this job description serves as a focused but generalized overview of the role; specific responsibilities and impact expectations will be tailored to the experience and seniority of the final hire.
Why Join Us?
- You will shape the software roadmap for a purpose-built inference accelerator, working at the layer where model architecture, systems performance, and real customer workloads meet.
- You will have unusually direct influence, defining what gets built across the inference stack and seeing it land in silicon-backed products that compete on performance per dollar and performance per watt.
Compensation & Benefits
The base salary range for this role is $200,000 – $350,000.
Please note that the figures provided represent the base salary range only and do not include other elements of our total compensation package, equity, or comprehensive benefits.
At Positron AI, we value the unique expertise each candidate brings. While the range above reflects our typical expectation for the position, we reserve the flexibility to exceed this range for candidates whose specialized skills, significant experience, or unique qualifications fall outside the standard scope of the role. Final offers are determined based on a variety of factors, including internal equity, and individual impact.
Benefits & Perks
We want you to do your best work and feel confident that you and your family are taken care of. That means comprehensive coverage, real time to rest, and support for your future.
Health and wellness
- Fully company-paid medical, dental, and vision insurance for you and your dependents
- Company-paid life and disability coverage, with voluntary options to add more
- Supplemental hospital, critical illness, and accident coverage available
Time off and flexibility
- Unlimited paid time off, we encourage everyone to truly unplug and recharge
- 13 paid company holidays
- Remote-first culture with a company-provided computer and home office setup
Compensation and future
- Competitive salary and equity
- 401(k) with company matching, eligible from day one
Visa Support
This position is open to candidates currently authorized to work in the U.S. We cannot provide new visa sponsorship for this role but are open to facilitating H-1B visa transfers for eligible candidates.
Equal Opportunity Employer. If you're excited about the role but don't meet every bullet, we'd still love to hear from you.
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Who's Hiring



Top Industries Hiring
- Technology & Software23
- Consulting & Professional Services3
- Insurance2
- Cybersecurity2
- Construction & Real Estate2
What Employers Look For
The qualifications that appear most often in remote AI product owner jobs.
- 3 or more years of product management experience in an AI or ML-driven product environment
- Ability to write and prioritize requirements for model training, evaluation, and deployment workflows
- Familiarity with machine learning concepts including model versioning, drift detection, and inference pipelines
- Experience working cross-functionally with data scientists, ML engineers, and applied research teams
- Proficiency with product discovery and roadmap tools such as Jira, Productboard, or Aha
- Bachelor's degree in computer science, engineering, data science, or a related quantitative field
Tips for Your Remote AI Product Owner Job Search
Apply early to remote roles that fit
Migrate Mate lists remote ai product owner openings from across the U.S. in one place, so you can find roles that match your background and apply directly without sifting through postings mixed with on-site work.
Show async communication skills concretely
Remote ai product owner hiring managers want evidence you can align engineers and stakeholders in writing. Include links to PRDs, Confluence specs, or Notion roadmaps in your application materials to show you document decisions clearly without relying on real-time meetings.
Highlight AI tooling you have shipped against
Name the specific frameworks, APIs, or ML platforms you have defined product requirements for, whether that is an LLM integration, a recommendation system, or a computer vision pipeline. Generic product experience reads as undifferentiated on remote ai product owner job postings.
Prepare for async remote interview rounds
Many distributed teams run their ai product owner interviews through recorded video prompts or written take-home exercises before a live call. Practice articulating your AI product thinking in a structured written format, because your first impression may come through text, not a conversation.
Remote AI Product Owner Jobs: Frequently Asked Questions
How do I get a remote ai product owner job?
Target companies that already run distributed product teams, because those employers have the async workflows and tooling that make remote ai product owner roles actually function. Remote hiring managers screen hard for written communication, self-directed prioritization, and hands-on experience with AI or ML product cycles. Demonstrating that you can drive stakeholder alignment and manage model deployment decisions without daily in-person check-ins sets you apart from candidates who have never worked outside a co-located office.
Which companies hire remote ai product owners?
Employers currently hiring remote ai product owners include Humana, Cwill, and ServiceNow, per current remote listings on Migrate Mate as of August 2026. Remote ai product owner roles tend to concentrate at remote-first software companies, AI-native startups, and distributed enterprise teams in sectors like fintech, healthtech, and cloud infrastructure.
Can you get a remote ai product owner job with no experience?
Yes, but remote entry-level ai product owner roles are harder to land because employers expect you to operate independently from day one without on-site mentorship. You can close that gap by contributing to open-source AI projects, building a product case study that shows you can define requirements for an ML feature, or completing a recognizable product management certification with an AI focus. Remote-first startups and early-stage AI companies are the most likely to take a chance on a candidate who shows initiative over a long resume.
Do you need a degree for remote ai product owner jobs?
Not always. Remote employers hiring ai product owners consistently weigh demonstrated product instincts, a working knowledge of machine learning concepts, and a portfolio of shipped AI features more heavily than a specific degree. A background in computer science or data science helps when the role is deeply technical, but candidates who can show clear documentation, prioritization decisions, and measurable AI product outcomes regularly get hired without a traditional four-year credential.
Which industries hire the most remote ai product owners?
Remote ai product owner roles concentrate in Technology & Software, Consulting & Professional Services, and Insurance, based on current remote listings on Migrate Mate as of August 2026. Those sectors hire ai product owners remotely because their product and engineering teams are already structured as distributed organizations that ship continuously without needing everyone in the same building.
See All 160+ Remote AI Product Owner Jobs
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Find Remote AI Product Owner Jobs