AI Product Manager Jobs
AI Product Manager jobs are open across tech, fintech, healthtech, and enterprise software, from associate to principal and director level, with specializations in generative AI, platform products, and MLOps tooling. Find a role that fits from the openings below and apply directly.
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About Future Works
Future Works is a US-focused, AI-native professional services firm building operational AI and data systems. Our foundation beliefs are simple: optimization lowers need for resources, which increases sustainability and resilience. We are a team of "Time Benders" on a mission to collapse timelines to operational transformation of high footprint companies in energy, real estate, data and beyond. Together, we master velocity.
We achieve our mission by building flawless, AI-powered systems. Our technology is already far more advanced than that of boutique services firms, but we are only getting started and the road from here leads to becoming world's best Service-as-Software company. Regardless of the specific role, our ideal candidate will always have a keen interest in building symbiotic human-AI systems both individually and for teams; high energy, high agency and a certain passion for bending time.
The role
Future Works is searching for a results-driven, AI-Enabled Product Manager to lead delivery of custom data and AI platforms for institutional real estate owners, operators and fund managers. These are fixed-fee, fixed-date engagements of roughly sixteen weeks, with payment tied to signed gates, and you are the day-to-day lead.
A typical cycle connects every system the client runs on (property accounting, third-party operator packages, investor and fund platforms, deal and leasing platforms, loan and JV documents, models in Excel and SharePoint) into one trusted asset record they own, hosted in their cloud environment, read-only and never writing back to source. On top of it, AI agents map new operator data, run data checks, abstract loan and JV terms, answer portfolio questions with sources cited and draft quarterly commentary, feeding the views each group needs: portfolio and asset performance, lease and tenant exposure, debt and covenants, equity and JV structure, business plan against locked underwriting, quarterly reporting and an exceptions log. Often the cycle also has to retire an incumbent platform by a contract date.
You own the plan, the gates and the scope line, supported by a data and technology lead, a full-stack engineer, a solution architect and a partner on business outcomes.
Key Responsibilities
Own the cycle end to end. Lead the full product development lifecycle across discovery workshops, data mapping, platform build, views, training and handover, against a fixed fee and a firm finish date.
Run gate-based delivery and hold the scope line. Define gate criteria, evidence and acceptance with the client before signature. Translate an ambitious scope into what one cycle can deliver, document what moves to later cycles, and manage change so a fixed fee stays fixed. Set a value baseline with client finance at the start and measure against it.
Put the data foundation first. Treat numbers that tie as the adoption bar. Work with the data lead on source mapping, data quality rules, exception routing to the responsible deal team, lineage and locked snapshots, before any view is built.
Define the views with the people who use them. Run workshops with asset management, finance, investor relations, deal teams and technology, and turn what they say into view specs, KPI definitions and acceptance criteria the client signs.
Decide where AI belongs, and where it does not. Work with engineering and data science on agent-based mapping, document extraction, retrieval with cited sources and drafted commentary, while keeping reported financial figures on fixed, auditable calculations the client approves.
Own the client relationship at working level. Be the primary contact for the executive sponsor, the business owner and the technology lead. Report progress plainly and surface problems before they land.
Land the retirement and the handover. Plan the history migration, the parallel run and the sign-off that the old system can be switched off, train teams by role inside the cycle, and transfer documentation and operating knowledge to the client's platform owner so their team builds what comes next.
Requirements
Must have
Experience. Minimum 5 years in product management with hands-on ownership of every phase of the PDLC, delivering custom software for external clients.
Real estate domain depth. At least 2 years inside or directly serving institutional real estate: an owner-operator, REIT, private equity real estate or fund manager, or a real estate technology or data vendor. You read a rent roll, an operating statement, a budget vs actual, a T-12, a loan abstract and a JV waterfall summary without a translator, and you know what WALT, DSCR, NOI and equity funded vs committed mean in practice.
Real estate systems fluency. Hands-on exposure to a property accounting system (Yardi, MRI or RealPage) plus at least one of Juniper Square, Altrio, VTS, ARGUS or a portfolio and asset management platform such as Pereview.
Data platform product experience. You have shipped a data warehouse, data lake, reporting or BI product where the adoption bar was trust in the numbers, not feature count, and can make product calls on ingestion, mapping, reconciliation, data quality rules, exception handling, lineage and immutable snapshots.
Technical knowledge in AI and software development. Strong grasp of LLM-based extraction, retrieval with cited sources, agent workflows and human-in-the-loop approval, with the ability to turn business requirements into specifications design and engineering can build from.
Microsoft data stack literacy. Comfortable delivering into a client's Microsoft environment (Azure, Fabric or Synapse, Power BI), including single sign-on, role and fund-level permissions, audit logging and test environments.
Fixed-fee consulting delivery. Track record on milestone or gate-based engagements where payment depends on acceptance, including writing acceptance criteria that survive a client signature.
Leadership and client presence. 5 to 7+ years leading multidisciplinary teams (design, engineering, data), and comfortable in the room with a CFO, COO, head of asset management or head of technology, delivering unwelcome news early and clearly.
Analytical and data-driven. Able to define KPIs, interrogate a number, find why actuals diverge from a plan and decide from evidence.
Practicalities. Proficiency in our project software suite (GSuite, ClickUp, Figma, Jira), and overlap with US Eastern business hours for workshops, gate reviews and training.
Strongly preferred
Experience retiring or replacing an incumbent platform against a contract date, including a parallel run.
Experience with quarterly investor reporting cycles, fund-level reporting or DDQ response processes.
Data governance experience: ownership models, stewardship, and approval of mappings and thresholds by the business rather than by IT.
Our Culture & Benefits
We believe that breakthrough results are driven by breakthrough experiences. Our culture is built on a foundation of freedom, high performance, and our seven core values.
Our Core Values
AI native. We use AI as a natural extension of our abilities to deliver faster, smarter, and better work.
Delivering joy. We underpromise and overdeliver, creating delight for our clients and our team.
Smooth & fast. We believe speed is the result of precision, flawless systems, and calm execution, not frantic effort.
Selfless. We proactively make work easier for others and measure our success by the success of the team.
Candid. We are direct, honest, and clear with each other, believing that transparent feedback is a gift.
Pursuing excellence. We are obsessed with quality and empower each other to break through boundaries and set new standards.
Ever evolving. We are relentlessly curious and improve every single week, sharing our learnings openly to elevate the entire organization.
Benefits & Perks
Connect deeply. We gather for immersive, all-expenses-paid company retreats in unique locations to connect, learn, and grow together.
Share in the upside. A competitive compensation package including equity, bonuses, substantial participation in company profits with a clear growth path to C-Level leadership based on performance.
AI Product Manager Jobs by Experience Level
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Who's Hiring
- TikTok31

- Google27

- Meta24

- Amazon20

- JPMorganChase18

Top Industries Hiring
- Technology & Software27
- Banking & Financial Services6
- Retail4
- Education4
- Consulting & Professional Services4
What Employers Look For
The qualifications that appear most often in AI product manager jobs.
- Proven experience defining and shipping AI or ML-powered product features
- Ability to collaborate directly with data scientists and ML engineers on model requirements
- Proficiency with product analytics tools and experimentation frameworks such as A/B testing
- Bachelor's degree in computer science, engineering, or a related quantitative field
- Familiarity with large language models, prompt engineering, or generative AI workflows
- Experience writing technical product specifications and acceptance criteria for AI systems
Tips for Your AI Product Manager Job Search
Tailor your resume to AI product depth
Hiring managers for ai product manager roles scan for evidence you've shipped AI-specific features, not just managed sprints. Call out model evaluation cycles, prompt engineering tradeoffs, or data labeling pipelines you owned, not just general roadmap ownership.
Filter openings by AI stack specificity
Job posts that mention specific frameworks like LLMs, RAG pipelines, or vector databases signal a team that ships AI products, not one that bolts on AI as an afterthought. Prioritize those over listings that use only generic phrases like 'leverage AI capabilities'.
Apply early to roles that fit
Migrate Mate lists ai product manager openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Document your AI-specific portfolio work
Create a one-page case study for at least one AI product you shipped or influenced. Describe the problem, the model choice tradeoffs, how you measured quality beyond accuracy, and what you would do differently. Attach it to every application as a PDF.
Prepare for responsible AI interview questions
Interviewers at AI-forward companies routinely ask how you handled bias in a training dataset, how you balanced speed against safety, or how you explained a model decision to a non-technical stakeholder. Prepare two concrete examples before your first screen.
Negotiate scope, not just compensation
In offer negotiation, ai product managers often leave meaningful leverage on the table by focusing only on base pay. Ask explicitly about model access, compute budget, data infrastructure support, and autonomy over prioritization, because these define the actual job you'll do.
AI Product Manager Jobs: Frequently Asked Questions
Which companies are hiring the most ai product managers?
The companies hiring the most ai product managers right now include TikTok, Google, and Meta, with the largest share of openings in California, New York, and Texas, based on current listings on Migrate Mate as of November 2026. Demand is concentrated in companies building or integrating large language model features into core products.
How many ai product manager jobs are remote?
About 80% of ai product manager openings are fully remote or hybrid as of November 2026, which is higher than the average for product management broadly. Sub-areas like developer tools, API platform products, and AI infrastructure tend to have the greatest share of remote-eligible roles.
How do you become an ai product manager?
Start by building a foundation in product management fundamentals, then layer in applied AI knowledge through coursework, personal projects, or by moving into an AI-adjacent squad at your current company. You'll need to understand model evaluation, data pipelines, and how ML systems fail, not just how they work when they succeed. A portfolio case study on an AI product you influenced is more persuasive than a certification alone.
Can you get hired as an ai product manager with little experience?
Yes, but you need to substitute direct experience with demonstrated AI product thinking. Build a side project that uses an API from a major AI provider, document the product decisions you made, and write up what you learned about latency, cost, and output quality tradeoffs. Associate or rotational PM roles at AI-forward companies are the most accessible entry points for candidates transitioning from adjacent roles in engineering, data, or UX.
What does the ai product manager interview process look like?
The process typically starts with a recruiter screen, followed by a product sense interview where you design or critique an AI feature, and a technical screen where you discuss model tradeoffs, evaluation metrics, or data quality issues with an ML engineer. Later rounds usually include a cross-functional collaboration interview and a case study presentation where you walk through a product decision involving an AI system you worked on or researched.
Where can I find and apply to ai product manager jobs?
You can find and apply to ai product manager jobs on Migrate Mate, which lists current openings from across the United States. Find roles that match your background and apply directly to each listing from the page.
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