Software and Data Engineering Director

Profluent is the frontier AI lab for biology. Profluent builds powerful foundation models for all of life's molecules, unlocking solutions that transform medicine, agriculture, and beyond. Founded in 2022 and headquartered in Emeryville, CA, Profluent is backed by leading investors including Altimeter Capital, Bezos Expeditions, Spark Capital, Insight Partners, Air Street Capital, AIX Ventures, and Convergent Ventures and has raised over $150M to date.

We’re looking for a Director of Software and Data Engineering to lead Profluent’s software engineering function and build the platform connecting AI-driven protein design, high-throughput experimental screening, and quantitative data analysis.

You’ll own the software and data architecture connecting computational designs to the constructs, samples, assays, and measurements generated through automated laboratory workflows. Your team will build the scientist-facing tools, integrations, and data systems needed to manage experimental data at scale, preserve lineage and context, and turn results into reliable inputs for analysis, machine learning, and the next round of design.

The ideal candidate is an experienced engineering leader with a strong technical foundation, sound judgment, and a track record of building scalable data platforms. You’ll manage and mentor a small team while remaining close to architecture, data modeling, and technical decision-making, partnering across science, automation, data, and ML to advance Profluent’s lab-in-the-loop platform.

Responsibilities

• Lead, mentor, and grow Profluent’s software engineering team, with responsibility for technical direction, hiring, organizational development, and execution
• Own the roadmap for software and data engineering, balancing scientific impact, user needs, delivery speed, scalability, and long-term platform strategy
• Define the software and data architecture, system boundaries, and data flows connecting computational design, high-throughput experimentation, analysis, and machine learning
• Architect and guide delivery of scientist-facing applications and data systems supporting experimental design, sample tracking, laboratory automation, assay data capture, and analysis
• Establish scalable data models and standards for identifiers, provenance, lineage, versioning, interoperability, and access control, ensuring experimental results are reliable, reusable, and model-ready
• Partner across science, automation, bioinformatics, data science, and ML while establishing strong engineering and product practices that drive reliable delivery and adoption

Qualifications

• 8+ years of software engineering experience, including 3+ years managing or leading engineers