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Your Impact at LILA
Lila's AI Science Factories (AISFs) are the automated experimental systems that let AI learn science by running it. The Chemistry AISF spans a broad array of domains including molecular chemistry and small-molecule synthesis, targeted drug delivery and LNP formulation, colloidal nanochemistry and quantum materials, and the flow-chemistry and analytical backbone that connects them.
We are hiring a Product Manager to own the product layer for the Chemistry AISF; deciding which capabilities we build, in what order, to what specification, and how we know they are working. You will partner with the Chemistry AISF Program Lead, who owns scientific direction, and with AISF Engineering, which owns the build. Your lane is the what and the why: turning commercial pull, scientific ambition, and AI model needs into a prioritized capability roadmap, and then into requirements crisp enough that engineering teams can scope, staff, and execute against them.
What You'll Be Building
- Set the product vision, roadmap, and capability priorities for the Chemistry AISF, spanning molecular chemistry and small-molecule synthesis, targeted drug delivery and formulation, colloidal nanochemistry, and the shared analytical backbone.
- Translate commercial pull, scientific ambition, and AI model needs into a single prioritized roadmap, making the tradeoffs between competing demands explicit and defensible.
- Define what a capability must do to count as delivered, including scope, throughput, performance, and level of autonomy, in requirements that engineering teams can scope, staff, and build against.
- Co-own the Chemistry AISF program charter with the Program Lead, including the capital plan, resourcing, and phasing that turn a roadmap into a funded sequence of work.
- Drive product execution from concept through commissioning across science, engineering, automation, software, and AI.
- Own the build-versus-buy and platform architecture decisions that determine how the Chemistry AISF scales.
- Represent the Chemistry AISF to executives, scientific leaders, partners, and customers as the product voice for the domain, and report progress against technical, product, and commercial milestones.
What You'll Need to Succeed
- MS, PhD, or equivalent experience in chemistry, chemical engineering, materials chemistry, or a closely related field.
- 5+ years in product management, technical product ownership, or a product-adjacent platform role delivering complex hardware and software systems: lab automation, scientific instrumentation, robotics, or ML-enabled experimental platforms.
- Working fluency in at least one Chemistry AISF domain and the ability to get credible quickly in the others. You should be able to hold a real conversation about reaction workup and purification, analytical characterization, or nanoparticle formulation, and know which questions you cannot answer yourself.
- A track record of writing requirements engineers can actually build from, not feature wish lists.
- Comfort making prioritization calls on incomplete information, in front of people who disagree, and writing down the reasoning so it survives the next reorganization.
- Experience leading through influence in a matrixed organization spanning science, engineering, software, and commercial teams.
- Strong written communication. A large share of this job is producing artifacts, including charters, roadmaps, and requirements documents, that other people act on without you in the room.
Bonus Points For
- Hands-on experience with automated synthesis platforms (ChemSpeed or similar), liquid handling under inert atmosphere, or high-throughput formulation systems.
- Familiarity with closed-loop and self-driving lab architectures: Bayesian optimization, active learning, agentic experiment design.
- Experience with lab orchestration and scheduling software, instrument integration, or scientific data capture architecture.
- Background in small-molecule discovery workflows or in LNP and nucleic acid delivery.
- Experience supporting partner-funded or customer-funded platform programs, including scoping deliverables and structuring milestones.
- Capital equipment planning experience: specification, vendor evaluation, commissioning and qualification timelines.
About LILA
Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.
We're All In
Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.
A Note to Agencies
Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
Salary Context
This $140K-$197K range is below the median for AI Product Manager roles in our dataset (median: $194K across 123 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 9,079 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Lila Sciences, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills in Demand for This Role
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,200 based on 947 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($168K) sits 23% below the category median. Disclosed range: $140K to $197K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Lila Sciences AI Hiring
Lila Sciences has 6 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span Cambridge, MA, US, San Francisco, CA, US, Cambridge, MA. Compensation range: $197K - $374K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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