Senior Software Engineer, Data Platform

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 Senior Software Engineer to help design, build, and scale Profluent’s data platform. This platform houses data from protein engineering campaigns, including protein designs, experimental results, partner datasets, analytical outputs, and model-ready training data. It enables rapid machine learning, biological discovery, and secure collaboration across internal and external programs.

This role is ideal for an engineer who enjoys building robust data systems: secure ingestion pipelines, well-structured warehouses, reliable data models, access controls, auditability, and infrastructure that makes complex scientific data usable at scale. You will work closely with ML, bioinformatics, and program teams to ensure Profluent’s data is organized, governed, accessible, and protected.

Responsibilities

Design, build, and maintain scalable data infrastructure for protein engineering campaigns, including ingestion, transformation, validation, storage, and retrieval of large scientific datasets Develop secure data pipelines for internal and partner-generated data, with strong attention to access control, data siloing, provenance, auditability, and compliance with data use restrictions Own core components of Profluent’s data warehouse and data platform, using Python, GCP, PostgreSQL, BigQuery, and related cloud-native technologies Build systems that transform raw experimental, computational, and partner data into structured, reliable, analysis-ready and model-ready datasets Establish best practices for data modeling, metadata management, data quality checks, schema evolution, versioning, and documentation Collaborate with ML engineers, computational biologists, data scientists, and program stakeholders to understand data

Requirements

and translate them into scalable technical systems Improve engineering quality through thoughtful system design, code review, testing, CI/CD, observability, and maintainable development workflows Contribute to architectural decisions for how Profluent stores, secures, organizes, and uses data across programs and partnerships

to production systems BS, MS, or PhD in Computer Science, Engineering, Data Science, Bioinformatics, or a related technical field, or equivalent practical experience Preferences (but not required) Experience with scientific, biological, clinical, genomic, laboratory, or high-throughput experimental data Experience managing external partner, customer, or restricted-access datasets Familiarity with data governance, lineage, metadata systems, schema registries, or data catalogs Experience with research data systems, LIMS, ELNs, Benchling, or adjacent scientific platforms Background working

Qualifications

5+ years of software engineering, data engineering, or data platform experience Strong proficiency in Python and modern software development practices, including git, testing, code review, CI/CD, and production deployment Experience designing and operating production data pipelines, data warehouses, and data models at scale Hands-on experience with cloud platforms, preferably GCP, and technologies such as BigQuery, PostgreSQL, object storage, workflow orchestration, and containerized services Strong understanding of data security, access control, data partitioning or siloing, audit logging, and managing sensitive or restricted datasets Experience working with complex, heterogeneous datasets and building systems that make them reliable, discoverable, and usable Ability to work independently, make sound technical decisions, and drive projects from ambiguous