Job description

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 BS, MS, or PhD in Computer Science, Bioengineering, Computational Biology, or a related field, or equivalent practical experience Track record of leading teams that architect and deliver production-quality, data-intensive software platforms Strong technical foundation in Python, backend systems, databases, APIs, cloud infrastructure, and modern software development practices Experience designing data architectures, schemas, and domain models for complex scientific, experimental, or similarly interconnected workflows Demonstrated ability to translate ambiguous user and scientific needs into reliable systems, make sound technical and product tradeoffs, and drive adoption Preferences Experience building scientific data infrastructure, research software, LIMS integrations, or scientist-facing applications Experience with Benchling, laboratory automation, instrument integration, sample tracking, or high-throughput experimental platforms Experience connecting experimental workflows with downstream analysis and machine learning Familiarity with biological data from protein engineering, gene editing, sequencing, screening, or mammalian or bacterial systems Frontend development experience and an interest in building intuitive scientist-facing interfaces What We Offer High-growth opportunity with meaningful impact on the future of protein design Competitive compensation package with equity participation 401(k) with a strong employer match Comprehensive benefits including health/dental/vision insurance Generous PTO policy and commitment to work-life balance Professional development opportunities in a cutting-edge field at the intersection of AI and biology Profluent Bio, Inc is an equal opportunity employer promoting diversity and inclusion in the workspace. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical conditions, veteran status, sexual orientation, gender (including gender identity and gender expression), sex (which includes pregnancy, childbirth, and breastfeeding), genetic information, taking or requesting statutorily protected leave, or any other basis protected by law. Work Authorization Requirement Applicants must have ongoing work authorization in the United States that does not require employer sponsorship. Sponsorship will not be provided now or at any time in the future for this position. Employment Eligibility Verification Legal authorization to work in the United States is required. In compliance with federal law, all persons hired must verify their identity and work eligibility and complete the required employment verification form upon hire. Hiring Salary Range $268,000 — $355,000 USD NOTICE TO STAFFING AND SEARCH FIRMS: Profluent does not accept unsolicited candidate submissions. We ask that agencies refrain from sending resumes to our careers inbox, hiring managers, or any Profluent employee. Any resume submitted without a signed agreement and an active search request from our Talent team will be treated as the property of Profluent, and no placement fee will be owed.

Requirements

Must-haves

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
8+ years of software engineering experience, including 3+ years managing or leading engineers
BS, MS, or PhD in Computer Science, Bioengineering, Computational Biology, or a related field, or equivalent practical experience
Track record of leading teams that architect and deliver production-quality, data-intensive software platforms
Strong technical foundation in Python, backend systems, databases, APIs, cloud infrastructure, and modern software development practices
Experience designing data architectures, schemas, and domain models for complex scientific, experimental, or similarly interconnected workflows
Demonstrated ability to translate ambiguous user and scientific needs into reliable systems, make sound technical and product tradeoffs, and drive adoption
Experience building scientific data infrastructure, research software, LIMS integrations, or scientist-facing applications
Experience with Benchling, laboratory automation, instrument integration, sample tracking, or high-throughput experimental platforms
Experience connecting experimental workflows with downstream analysis and machine learning
Familiarity with biological data from protein engineering, gene editing, sequencing, screening, or mammalian or bacterial systems
Frontend development experience and an interest in building intuitive scientist-facing interfaces

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

aimachine learningmlpython

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto

Professional Development