Software Engineering Manager, Lab Informatics Platform

<div class="content-intro"><p>Profluent is an AI-first protein design company. Founded in 2022, we develop deep generative models to design and validate novel, functional proteins to revolutionize biomedicine. Based in Emeryville, CA, we are backed by leading investors including Altimeter Capital, Bezos Expeditions, Spark Capital, Insight Partners, Air Street Capital, AIX Ventures, and Convergent Ventures, and have raised over $150M to date.</p></div><p>We’re looking for a product-minded engineering manager to lead Profluent’s Lab Informatics Platform team. This role combines engineering management, technical leadership, and product ownership for the systems scientists use to design experiments, track samples, capture assay data, integrate with Benchling and lab automation, and make experimental data usable for analysis and machine learning.<br><br>The ideal candidate is an experienced people manager with a strong technical foundation and sound product judgment. You’ll manage and mentor a small team while staying close to architecture, data modeling, database design, and technical decision-making. You’ll work closely with wet-lab scientists, automation engineers, data scientists, and ML experts to understand workflows, identify pain points, define requirements, prioritize tradeoffs, and lead the delivery of practical scientist-facing tools that make complex experimental workflows scalable, traceable, and actionable.</p> <p><strong>Responsibilities </strong></p> <ul> <li>Lead, mentor, and grow a software engineering team focused on lab informatics, scientific data infrastructure, and scientist-facing applications</li> <li>Own and communicate the roadmap for Profluent’s lab informatics platform, balancing scientific impact, user needs, engineering effort, usability, scalability, and long-term data strategy</li> <li>Architect and guide delivery of systems that support sample tracking, assay data capture, Benchling integration, lab automation, experimental metadata, and analysis workflows</li> <li>Design robust data models for protein engineering and gene editing experiments, including constructs, samples, reagents, cell systems, assay results, metadata, and experimental lineage across mammalian and bacterial workflows</li> <li>Partner closely with scientists, automation engineers, data scientists, and ML researchers to understand workflows, identify pain points, define requirements, prioritize tradeoffs, and translate complex laboratory needs into scalable software solutions that accelerate Profluent’s design-build-test-learn cycle</li> <li>Establish strong engineering and product practices across architecture, technical design, code review, testing, CI/CD, documentation, stakeholder communication, feedback loops, success metrics, adoption, and agile delivery</li> </ul> <p&g