Profluent Bio

Machine Learning Engineer

• Design and develop state-of-the-art predictive and generative models incorporating domain-specific evolutionary and experimental data
• Leverage massive-scale protein and nucleic acid data to train specialized models for protein understanding and design
• Curate relevant datasets and design tasks for rigorous evaluation of generative models
• Collaborate across the machine learning and protein design teams to adapt and apply techniques for experimental validation
• Implement, analyze, and interpret multiple computational approaches and present results to colleagues in regular update meetings
• Work within a collaborative, fast-paced, interdisciplinary team across biology and machine learning to help shape the scientific and strategic vision of the company

Machine Learning Scientist, BioML

Data Engineer

• 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

Senior Software Engineer, Data Platform

Attorney

• Lead legal strategy and execution for complex transactions, including licensing, joint ventures, and strategic collaborations across therapeutics, diagnostics, agriculture, biocatalysis, and manufacturing
• Serve as a genuine thought partner to BD and executive leadership from the earliest stages of deal structuring, not just at the contract execution phase
• Collaborate with R&D teams to critically refine research plans and SOWs to ensure technical objectives are precise, actionable, and aligned with contractual terms
• Draft, negotiate, and advise on a range of mid-to-complex agreements, including IP licenses, options, evaluations, MSAs, revenue sharing agreements, SRAs, and similar instruments
• Develop generalizable deal principles and strategies in partnership with BD, R&D, and executive leadership, including partnering with alliance management through the contract lifecycle
• Triage and communicate risk in ways that are actionable and grounded in evidence, and translate legal considerations into business strategy, not just legal analysis
• Perform effectively in high-stakes, live negotiation settings, including multi-party sessions with sophisticated counterparties

Vice President, Legal

Engineering Manager

• Lead, mentor, and grow a software engineering team focused on lab informatics systems, scientific data infrastructure, and scientist-facing applications
• Architect and deliver the roadmap for systems that support sample tracking, assay data capture, Benchling integration, lab automation, experimental metadata, and analysis workflows
• Design robust data models for protein engineering and gene editing experiments, including constructs, samples, reagents, cell systems, assay results, metadata, and experimental lineage across mammalian and bacterial workflows
• Partner closely with scientists to translate complex laboratory requirements into scalable software solutions that accelerate Profluent’s design-build-test-learn cycle
• Build and maintain backend systems, APIs, databases, data pipelines, and web applications using technologies such as Python, Django, PostgreSQL, BigQuery, GCP, and modern development workflows
• Establish strong engineering practices across architecture, code review, testing, CI/CD, documentation, and agile delivery

Software Engineering Manager, Lab Informatics Platform

• Lead, mentor, and grow a software engineering team using cloud-native technologies to build scalable data integration pipelines that transform raw protein engineering data into actionable insights for our ML models
• Architect and deliver the technical roadmap for our data platform, using GCP and Python to build scalable pipelines and web-based applications that organize, process, and visualize the output of our protein engineering campaigns
• Drive cross-functional alignment by sitting at the intersection of biology, data science, and machine learning to translate complex experimental requirements into technical solutions that accelerate our protein design cycle

Senior Software Engineering Manager, Lab Data Platform

• Build robust, reproducible and user-friendly pipelines for automated model fine-tuning, alignment and evaluation
• Design and implement modular, easy-to-maintain, multi-model pipelines for protein design.
• Develop highly scalable ETL pipelines to process petabyte-scale protein data for model pretraining
• Optimize model training and inference code to maximize throughput and resource utilization when deployed at scale
• Develop software and infrastructure that enable the ML team to work quickly and frictionlessly in distributed and multi-cloud environments
• Partner with ML and protein design scientists to prototype research ideas and bring them into production

Designer

• Design, build, and deploy end-to-end agentic workflows that take protein function specifications and produce ranked candidate sequences for experimental validation
• Integrate protein language models, structure prediction tools, and scoring functions into multi-step pipelines with iteration, feedback loops, and human-in-the-loop checkpoints
• Own the full lifecycle of workflow systems: architecture, implementation, testing, deployment, monitoring, and iteration based on experimental outcomes
• Collaborate with ML scientists to evaluate and integrate new models into workflows, and with protein design scientists and biologists to translate scientific goals into computational specifications and ensure workflows align with experimental realities
• Develop tooling for experiment tracking, logging, reproducibility, and versioning of design campaigns
• Evaluate and benchmark workflow performance against experimental results, using outcomes to improve agent strategies and model selection
• Partner with ML scientists to identify where existing models fall short in agentic contexts and inform priorities for model development, fine-tuning, and evaluation