Director, AI-Driven Biotech Platform Engineering

Profluent; Emeryville (CA); On-site; USD 268,000 - 355,000; Full time

Benefits offered by this job

Equity participation

401(k) with employer match

Health/dental/vision insurance

Generous PTO

Job summary

Profluent, a frontier AI biology lab, seeks a Director of Software and Data Engineering to lead software and data architectures connecting computational design, lab automation, and ML-driven analysis. You will mentor a small team, shape platform strategy, and drive scalable data systems for experimental data and provenance.

The role requires deep experience building data-intensive platforms, strong Python/backend skills, and collaboration across science and ML teams.

Qualifications

• 8+ years of software engineering experience.
• 3+ years managing or leading engineers.
• BS, MS, or PhD in Computer Science, Bioengineering, Computational Biology, or related field, or equivalent experience.
• Track record of delivering production-quality, data-intensive software platforms.
• Strong Python, backend, databases, APIs, cloud infrastructure knowledge.

Responsibilities

• Lead, mentor, and grow Profluent’s software engineering team and set technical direction.
• Own the roadmap for software and data engineering balancing scientific impact and scalability.
• Define software/data architecture and data flows across computational design to ML.
• Architect scientist-facing apps and data systems for design, tracking, and analysis.
• Establish scalable data models, provenance, lineage, and access controls.
• Partner across science, automation, data science, and ML to drive delivery and adoption.

Job description

Profluent, a frontier AI biology lab, seeks a Director of Software and Data Engineering to lead software and data architectures connecting computational design, lab automation, and ML-driven analysis. You will mentor a small team, shape platform strategy, and drive scalable data systems for experimental data and provenance.

The role requires deep experience building data-intensive platforms, strong Python/backend skills, and collaboration across science and ML teams.