Product Manager

12 of the top 15 pharma generic companies in the world trust Leucine to manufacture the drugs that reach millions of patients every day.

Pharma manufacturing is one of the last large industries still running on paper, Excel, and tribal knowledge. A single missed step in a batch record can mean a recall, a regulatory warning, or a drug that should never have left the plant.

Leucine is the AI platform replacing all of it. Series A, backed by top-tier global investors, 60+ pharma enterprises in production, and the team that's going to define this category.

About the Role

You will deploy and run our platform inside pharma customers with your own hands, and be the product manager for the platform based on what that teaches you. You’ll model a customer’s QC and manufacturing data, build the pipelines, ontology, and apps (directing our AI agents to do the heavy lifting — and verifying everything they produce), and get real users live. Then you’ll turn the friction and gaps you hit in the field into the product roadmap, working directly with the CPO.

This is the shortest path from a customer’s real problem to a shipped platform improvement. It suits a technical builder who is genuinely comfortable in SQL and data models, skeptical and sharp when reviewing AI output, and obsessed with getting the data right.

What You’ll Do

Deploy the platform at customers (hands-on)

- Model the customer’s data end-to-end: build source→gold data pipelines (transforms, joins, incremental loads, dedup, grain, schema drift) and design the business ontology — entities (batch records, deviations, QC results, instruments, sampling, etc.), their relationships, and how they map to the customer’s real source columns.

- Write and debug SQL against the data warehouse; validate correctness with dry-runs, row counts, and spec checks before anything reaches a user.

- Build operational apps and dashboards on the platform for customer users (data bindings, platform components), and drive adoption with quality/manufacturing stakeholders.

- Troubleshoot pipeline, data-model, and app issues to root cause — across the data, the generated artifacts, and logs.

Operate and verify AI agents

- Direct the platform’s AI agents to generate pipelines, data models, and apps — and rigorously review their output: read the generated SQL/specs, catch silent failures (all-zero dashboards, fan-out joins, malformed models), and tell a genuine platform bug apart from a bad prompt.

- Use AI-assisted / “vibe coding” tools to prototype and validate ideas before spending engineering time.

Be the platform’s PM

- Convert field learnings — recurring friction, capability gaps, real bugs — into prioritized, well-scoped product work; own the roadmap for one or more areas with the CPO.

- Write crisp PRDs and specs engineering and design can build from without guesswork.

- Define and track the metrics that matter (time-to-value on a deployment, adoption, retention) and report to the CPO.

- Partner with engineering, design, sales, and customer success to ship and land features.

What We’re Looking For (must-haves)

- Solid SQL — day one, non-negotiable. You can write and debug non-trivial queries (joins, aggregations, window functions, CASE logic) without ramp-up. This is tested in the interview.

- Proven data modelling. You can take messy source tables and design a clean, correct model — reasoning about grain, keys, relationships, incremental loading, and dedup. Comfortable with a cloud data warehouse (BigQuery a plus).

- Operate and critically evaluate AI agents / AI coding tools (Claude, Cursor, Replit, v0, Lovable) — you build with them and reliably catch when they’re wrong.

- A correctness mindset — you never confuse “it ran” or “it looks done” with “it’s right,” especially where data drives decisions.

- Strong product instincts: turn ambiguous, real-world needs into clear requirements and prioritize ruthlessly.

- Excellent communication — equally credible with a customer’s QC lead, your engineers, and the CPO.

- High agency; comfortable owning a customer’s outcome in a fast-moving, ambiguous environment.

- 3–5 years in product management, forward-deployed / solutions engineering, data/analytics engineering, or a hands-on technical delivery role.

- Bachelor’s in Engineering, Computer Science, or a related technical field (or equivalent hands-on experience).

Good to Have

- Experience in pharma / life sciences / manufacturing / quality / compliance (GxP) — not required; we’ll teach the domain to someone with the hunger to master it.

- Prior forward-deployed, implementation, or solutions experience deploying software directly at customers.

- Ontology / semantic-layer experience; familiarity with data-pipeline or warehouse tooling (dbt, Data form, Airflow, or similar).

- A portfolio of things you’ve actually built (including with AI tools).

- Early-stage start-up experience.

• Be part of a fast-growing SaaS company transforming the future of pharmaceutical manufacturing through cutting-edge technology
• Collaborate with a passionate, high-performing team building innovative AI-powered digital solutions
• Take ownership of impactful client implementations from day one, with ample opportunities for learning, career growth, and professional development
• Enjoy a competitive compensation package designed to reward your contributions
• Comprehensive Health Insurance with industry-leading coverage for your peace of mind
• Complimentary Meals & Refreshments: Stay energized with unlimited breakfast, lunch, snacks, and dinner provided at the office
• Family-Friendly Leave Policies, including Paternity Leave and Compassionate Leave, to support you through life's important moments