Profluent Director to lead software engineering and build platform connecting AI-driven protein design with experimental screening and data analysis. Manage team and own software and data architecture.
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
Qualification
BSStrong technical foundation in PythonExperience designing data architecturesExperience with Benchling
Required
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
