Profluent

Tech / AI / Software

SeniorSoftwareEngineer,DataPlatform

$170–220k emeryville, california, united states
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Senior Software Engineer, Data Platform at Profluent. Skills: Data Platform Engineering, Scalable Data Infrastructure, Secure Data Pipelines, Data Warehousing, Data Modeling, Cloud-Native Technologies (GCP, BigQuery, PostgreSQL), Python, Data Security and Governance. 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 inte”

What You'll Achieve.

enable rapid machine learning, biological discovery, and secure collaboration across internal and external programs; make complex scientific data usable at scale

Industry & Context.

Tech / AI / Software
Problems you'll solve

make sound technical decisions

Eligibility Requirements

ongoing work authorization in the United States that does not require employer sponsorship, Legal authorization to work in the United States

What They're Looking For.

Must Have

5+ years of software engineering, data engineering, or data platform experience, 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, 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 requirements to production systems, BS, MS, or PhD in Computer Science, Engineering, Data Science, Bioinformatics, or a related technical field, or equivalent practical experience, ongoing work authorization in the United States that does not require employer sponsorship, Legal authorization to work in the United States

Nice to Have

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 with ML, data science, computational biology, or cross-disciplinary technical teams, Interest in learning biology, gene editing, protein design, or machine learning concepts

What You'll Do.

and maintain scalable data infrastructure for protein engineering campaigns

and retrieval of large scientific datasets

Develop secure data pipelines for internal and partner-generated data

with attention to access control

and compliance with data use restrictions

Own core components of Profluent’s data warehouse and data platform

and related cloud-native technologies

Build systems that transform raw experimental

and partner data into structured

analysis-ready and model-ready datasets

Establish best practices for data modeling

Improve engineering quality through thoughtful system design

and maintainable development workflows

Contribute to architectural decisions for how Profluent stores

and uses data across programs and partnerships

How You'll Work.

Team & Collaboration

Work closely with ML, bioinformatics, and program teams to ensure Profluent’s data is organized, governed, accessible, and protected; Collaborate with ML engineers, computational biologists, data scientists, and program stakeholders to understand data requirements and translate them into scalable technical systems; Background working with ML, data science, computational biology, or cross-disciplinary technical teams

Process & Methodology

drive projects from ambiguous requirements to production systems

Full Job Description

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 che

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