Member of Technical Staff, Product Engineer

Warp•South San Francisco, CA

About The Position

Warp Sciences is building scientific infrastructure in the United States to accelerate the discovery of new medicines by bringing AI, robotics, and life sciences together in increasingly automated laboratories. The company builds infrastructure for research teams rather than maintaining a proprietary drug pipeline. The Product Engineer will take technical ownership of software connecting people to autonomous laboratories. This full-stack product engineering role owns staff- and customer-facing workflows, from scientific objectives and experiment planning through execution and results, with hands-on responsibility for frontend, backend services, APIs, and data models. The engineer will work directly with chemists, researchers, operators, and customers to decide what to build and deliver reliable software for production use. The position calls for an experienced engineer who has led substantial software initiatives, can turn open-ended problems into technical plans, and remains hands-on through implementation and ongoing operation. The role also provides technical leadership through architecture discussions, code reviews, and mentoring. Prior chemistry or life sciences experience is not required, but curiosity and enthusiasm for learning are mandatory.

Requirements

Experienced full-stack engineer with a track record delivering and maintaining production web applications and substantial hands-on contributions to frontend interfaces, backend services, APIs, and data models.
Has set technical direction for a product area and independently identified and scoped important problems.
Has personally led significant projects from an ambiguous problem to production adoption, with impact across a product area or team, and can explain ownership, implementation, tradeoffs, and outcomes.
Has served as a technical lead for work involving multiple engineers over multiple months, defining the approach, sequencing delivery, coordinating dependencies, and remaining accountable through launch and follow-up.
Strong software engineering foundations and experience designing systems for reliability, performance, and maintainability, including architectural decisions that held up in production.
Product sense and sound judgment about user experience, including direct user engagement and decisions about scope and interactions that help users accomplish their work.
Can communicate technical plans and tradeoffs clearly to engineers and non-engineers and help other engineers through code review, technical guidance, and mentoring.
Uses AI development tools effectively and critically and takes responsibility for understanding, testing, and maintaining resulting code.
Has operated software after launch, used monitoring and user feedback to diagnose problems, and followed through on fixes and improvements.
Curiosity and enthusiasm for learning; prior chemistry or life sciences experience is not required.

Nice To Haves

Experience with scientific software, laboratory workflows, or tools for technical users.
Domain knowledge in chemistry, drug discovery, or related scientific disciplines.
Experience with robotics, automation, or products that interact with physical systems.
Experience with complex workflow tools, operational interfaces, or collaborative applications.
Experience with data visualization, experiment tracking, and communicating uncertainty.
Experience with AI-powered products and interfaces for guiding, reviewing, or correcting automated work.

Responsibilities

Lead full-stack delivery of scientific workflows, from understanding user needs and defining scope through architecture, implementation, testing, deployment, and ongoing operation.
Independently scope initiatives spanning multiple months and engineers; write technical designs, identify dependencies and risks, and break work into milestones that deliver useful software along the way.
Work directly with chemists and customers to capture objectives, constraints, procedures, and exceptions, translating them into requirements and usable workflows for automated experimentation.
Build user interfaces, backend services, APIs, and data models connecting experiment planning, robotic execution, and analysis, and integrate with internal systems alongside other engineers.
Own the reliability and maintainability of shipped software by adding tests and monitoring, investigating production failures, and making deliberate tradeoffs among delivery speed, performance, and technical debt.
Make experiment status, results, uncertainty, and failures understandable so users can investigate problems and guide automated work; use feedback and usage data to improve task completion and reduce manual effort.
Provide technical leadership through architecture discussions, code reviews, and mentoring while continuing to write production code.
Use AI development tools to accelerate work and verify the correctness of their output.
Set technical direction for a product area, identify and scope important problems, and lead multiple engineers through delivery while remaining hands-on.
Operate software after launch, use monitoring and user feedback to diagnose problems, and follow through on fixes and improvements.