Fractional AI Product Manager · Los Angeles, CA

Hire an AI product manager in Los Angeles.

A full-time AI PM in Los Angeles earns $160,000 to $200,000 per year, plus equity. The recruiting cycle runs four to six months. A three-month fractional engagement produces a working AI feature roadmap, product specs for your top three features, and a vendor evaluation for each — at a fixed price, agreed before work starts.

NBCUniversal, Sony, and Lionsgate each need AI product decisions in content recommendation, metadata generation, and script coverage automation. The PM spec for a content AI feature looks different from a fintech AI feature. The success metrics are different. The failure modes are different.

Fixed engagement, fixed price. You know the price before work starts.

How we work

Tell us about your AI feature roadmap gap.

Full-time vs. fractional: the cost math.

A full-time AI PM in Los Angeles costs $160,000 to $200,000 in base salary. Add employer taxes, benefits, and equity, and the first-year cost reaches $180,000 to $240,000. The recruiting cycle is four to six months. Onboarding takes another four to six weeks before the person ships anything useful.

You also bear the risk. If the hire turns out to be wrong for the role, you have spent twelve months of salary finding that out.

A three-month fractional engagement, at a fixed price agreed up front, produces the same first-pass deliverables: AI feature roadmap, product specs for the top three use cases, and vendor evaluations. Start date is two weeks from signature, not six months.

The total first-year cost difference against a $180,000 to $240,000 full-time hire is substantial. If you do not yet know whether you need a permanent AI PM, that gap makes the fractional option obvious.

What three months produces.

Four artifacts, not a deck of possibilities.

AI feature roadmap

Top three prioritized use cases, ranked by business impact and engineering feasibility. Each use case has a one-paragraph problem statement, a definition of done, and a rough timeline.

Product specs for each feature

Written to the level your engineering team can estimate from. Each spec covers inputs, outputs, edge cases, failure modes, and success metrics. No ambiguity about what done looks like.

Vendor evaluations

Build, buy, or partner recommendation for each prioritized feature, with supporting rationale. Includes a comparison table of evaluated vendors and a recommended integration path.

Evaluation framework

A structured process your team can reuse to assess future AI vendor proposals without external help. Criteria are weighted against your specific business priorities.

Entertainment AI product specifics.

Content recommendation at a media company is not the same product problem as recommendation at a retail company. The catalog is finite. Completion rate matters as much as click-through rate. A user who watches 80% of a title and does not return is a different failure mode from a user who clicks and immediately exits.

Script coverage automation has a ground truth problem that most AI proposals skip. The evaluation framework has to be built against a validated set of historical coverage decisions, not tested on production scripts from day one.

Metadata generation for film and TV requires decisions about taxonomy ownership. If your metadata taxonomy lives in a legacy system at NBCUniversal or a custom catalog at Lionsgate, the AI feature spec must include a taxonomy governance section explaining what the model is allowed to create versus what requires a human editor to approve.

These are PM responsibilities that get skipped when the person writing the spec does not know the domain. They surface as production bugs six months later.

How we work.

Remote-first, fixed scope, no surprises.

Discovery call, then written scope

One hour on the phone. We send a written scope within 48 hours: deliverables, timeline, and a fixed price. You approve it before any work starts.

Friday written updates

Every Friday, a written summary of what was completed, what is in progress, and any open decisions that require your input. Loom walkthroughs for every milestone.

You own everything at handoff

Every artifact transfers to you. Roadmaps, specs, vendor comparisons, evaluation frameworks. No lock-in, no ongoing retainer required.

Questions from Los Angeles product teams.

What does a 3-month fractional AI PM engagement actually deliver. What can you expect to have at the end?: At the end of a three-month engagement you have four artifacts: an AI feature roadmap covering the top three prioritized use cases, a product spec for each of those three features written to the level your engineering team can estimate from, a vendor evaluation for each feature that recommends build, buy, or partner with supporting rationale, and an evaluation framework your team can use to assess future AI vendor proposals on their own. You do not get a slide deck of possibilities. You get documents your team can act on.
How do you write success metrics for an AI content recommendation feature. What does "working" mean?: For a content recommendation feature, success metrics depend on the goal layer. At the engagement layer: click-through rate on recommended titles and session duration following a recommendation click. At the satisfaction layer: thumbs-up rate on recommendations and explicit feedback scores. At the business layer: subscription retention for users who engage with recommendations versus those who do not. The spec defines a baseline measurement period, a minimum detectable effect size worth acting on, and an upper bound on how long to run before calling the test. "The model is producing recommendations" is not a success metric. A number is a success metric.
What is the right evaluation framework for a script coverage AI tool. How do you know it is giving useful feedback?: Script coverage AI evaluation requires a ground truth set: 30 to 50 scripts with documented coverage outcomes from your existing reader team (pass, consider, and pass with notes) along with the reasons given. The AI tool is scored on agreement rate with the human panel, false positive rate (AI recommends pass on scripts the panel rejected), and false negative rate (AI rejects scripts the panel passed). You also evaluate on coverage quality: does the AI summary include the same elements a human reader would flag: character, theme, marketability, format fit? Agreement rate of 75% or higher on a validated test set is a reasonable bar before production use. Below that, the tool adds noise to your coverage pipeline, not speed.
How do you manage the handoff from a fractional engagement to a full-time AI PM hire?: The engagement produces a handoff package as a deliberate output, not an afterthought. It includes a decisions log that captures every significant choice made and the reasoning behind it, an open questions file for items that remain unresolved, a vendor contract status summary, and a roadmap with milestones flagged for owner assignment. That package means a new full-time AI PM can read the context in a day and continue without losing ground. If the timeline allows, we schedule a two-hour live handoff call with the incoming hire before the engagement closes.

Ready to scope your AI product roadmap?

Tell us the AI feature decision you are trying to make. We reply within one business day with a rough scope and price.

Hire an AI product manager in Los Angeles.

Fixed engagement, fixed price. You know the price before work starts.

Full-time vs. fractional: the cost math.

You also bear the risk. If the hire turns out to be wrong for the role, you have spent twelve months of salary finding that out.

What three months produces.

Four artifacts, not a deck of possibilities.

AI feature roadmap

Top three prioritized use cases, ranked by business impact and engineering feasibility. Each use case has a one-paragraph problem statement, a definition of done, and a rough timeline.

Product specs for each feature

Written to the level your engineering team can estimate from. Each spec covers inputs, outputs, edge cases, failure modes, and success metrics. No ambiguity about what done looks like.

Vendor evaluations

Build, buy, or partner recommendation for each prioritized feature, with supporting rationale. Includes a comparison table of evaluated vendors and a recommended integration path.

Evaluation framework

A structured process your team can reuse to assess future AI vendor proposals without external help. Criteria are weighted against your specific business priorities.

Entertainment AI product specifics.

These are PM responsibilities that get skipped when the person writing the spec does not know the domain. They surface as production bugs six months later.

How we work.

Remote-first, fixed scope, no surprises.

Discovery call, then written scope

One hour on the phone. We send a written scope within 48 hours: deliverables, timeline, and a fixed price. You approve it before any work starts.

Friday written updates

Every Friday, a written summary of what was completed, what is in progress, and any open decisions that require your input. Loom walkthroughs for every milestone.

You own everything at handoff

Every artifact transfers to you. Roadmaps, specs, vendor comparisons, evaluation frameworks. No lock-in, no ongoing retainer required.

Questions from Los Angeles product teams.

What does a 3-month fractional AI PM engagement actually deliver. What can you expect to have at the end?

At the end of a three-month engagement you have four artifacts: an AI feature roadmap covering the top three prioritized use cases, a product spec for each of those three features written to the level your engineering team can estimate from, a vendor evaluation for each feature that recommends build, buy, or partner with supporting rationale, and an evaluation framework your team can use to assess future AI vendor proposals on their own. You do not get a slide deck of possibilities. You get documents your team can act on.

How do you write success metrics for an AI content recommendation feature. What does "working" mean?

For a content recommendation feature, success metrics depend on the goal layer. At the engagement layer: click-through rate on recommended titles and session duration following a recommendation click. At the satisfaction layer: thumbs-up rate on recommendations and explicit feedback scores. At the business layer: subscription retention for users who engage with recommendations versus those who do not. The spec defines a baseline measurement period, a minimum detectable effect size worth acting on, and an upper bound on how long to run before calling the test. "The model is producing recommendations" is not a success metric. A number is a success metric.

What is the right evaluation framework for a script coverage AI tool. How do you know it is giving useful feedback?

Script coverage AI evaluation requires a ground truth set: 30 to 50 scripts with documented coverage outcomes from your existing reader team (pass, consider, and pass with notes) along with the reasons given. The AI tool is scored on agreement rate with the human panel, false positive rate (AI recommends pass on scripts the panel rejected), and false negative rate (AI rejects scripts the panel passed). You also evaluate on coverage quality: does the AI summary include the same elements a human reader would flag: character, theme, marketability, format fit? Agreement rate of 75% or higher on a validated test set is a reasonable bar before production use. Below that, the tool adds noise to your coverage pipeline, not speed.

How do you manage the handoff from a fractional engagement to a full-time AI PM hire?

The engagement produces a handoff package as a deliberate output, not an afterthought. It includes a decisions log that captures every significant choice made and the reasoning behind it, an open questions file for items that remain unresolved, a vendor contract status summary, and a roadmap with milestones flagged for owner assignment. That package means a new full-time AI PM can read the context in a day and continue without losing ground. If the timeline allows, we schedule a two-hour live handoff call with the incoming hire before the engagement closes.

Ready to scope your AI product roadmap?

Tell us the AI feature decision you are trying to make. We reply within one business day with a rough scope and price.