Software Engineering Manager, Lab Informatics Platform
Profluent
| Company | Profluent |
| Category | Engineering |
| Location | Emeryville |
| Remote | Hybrid |
| Employment | Not stated |
| Level | Manager |
| Salary | Not stated by the employer |
| Posted | 4 Sept 2025 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
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. 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. 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.
Responsibilities
Lead, mentor, and grow a software engineering team focused on lab informatics, scientific data infrastructure, and scientist-facing applications
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
Architect and guide delivery of systems that support sample tracking, assay data capture, Benchling integration, lab automation, experimental metadata, and analysis workflows
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
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
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
Qualifications
7+ years of software engineering experience, including at least 2 years managing or leading engineers
BS, MS, or PhD in Computer Science, Bioengineering, Computational Biology, or a related field
Strong technical leadership skills, with a track record of architecting and delivering production-quality software systems through teams
Proficiency with Python, backend development, databases, APIs, and modern software engineering workflows, with the ability to guide technical design and implementation
Experience building or leading teams that build scientific data platforms, LIMS integrations, research software, or scientist-facing applications, including data modeling and database design for complex scientific or experimental workflows
Demonstrated ability to work closely with scientists and technical stakeholders to translate ambiguous laboratory workflows into reliable software systems, define requirements, prioritize tradeoffs, and drive adoption of internal tools
Preferences
Experience with Benchling, lab automation, instrument data ingestion, sample tracking, or high-throughput experimental data
Experience with data generated from mammalian or bacterial cell systems, gene editing, protein engineering, or rel
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