We're looking for a Protein Design Scientist to own the development and deployment of agentic workflows for protein design. In this role, you will build systems that orchestrate protein language models, structure predictors, fitness scoring tools, and experimental feedback into automated or semi-automated design pipelines - closing the loop between computation and the wet lab.

This is a hands-on, high-ownership role. You'll work across the full stack of a design workflow: selecting and integrating models, defining agent logic and iteration strategies, building robust infrastructure for deployment, and collaborating closely with ML scientists, protein design scientists, and biologists to ensure your systems produce experimentally actionable results.

As an early employee, you will shape how Profluent approaches workflow automation and help define best practices for agentic systems in protein engineering.

Responsibilities

• 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

Qualifications

• PhD (or equivalent industry experience) in Computational Biology, Bioinformatics, Computer Science, Machine Learning, Biochemistry, or a related field
• Minimum of 3+ years of experience as a protein design scientist
• Strong software engineering skills: you write clean, modular, production-quality code (Python required; experience with workflow orchestration frameworks is a plus)
• Hands-on experience with protein language models (e.g., ESM, ProGen, ProtTrans) for sequence generation, scoring, or representation
• Demonstrated understanding of protein engineering principles: structure-function relationships, fitness landscapes, and how computational predictions connect to wet-lab validation
• Experience building multi-component systems that chain model inference with data processing, filtering, and decision logic

Preferences (but not required)

• Experience with agentic AI systems, LLM tool-use patterns, or multi-step reasoning pipelines
• Familiarity with structure prediction tools (AlphaFold, RoseTTAFold, ESMFold) and inverse folding methods (ProteinMPNN, LigandMPNN)
• Track record of computational designs that were experimentally validated
• Experience with cloud compute platforms (GCP, AWS, Azure) and containerized deployment (Docker, Kubernetes)
• Publications in protein design, protein engineering, or ML for biology at major conferences or journals
• Familiarity with directed evolution, rational design, or de novo protein design strategies

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.

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.

We're looking for an experienced Machine Learning Engineer to build and improve the models and ML systems that drive our protein design efforts. In this role, you'll deploy and optimize large-scale generative models for protein design, and develop the surrounding infrastructure and tooling that enable our ML and protein design scientists to work faster and more confidently. As an early member of a small, fast-moving engineering team, you'll have significant ownership over our ML stack and the opportunity to shape how our platform evolves.

Responsibilities

• 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

Who You Are

• You're comfortable taking ownership and working independently in a fast-moving environment
• You're an execution-oriented engineer who maintains high standards, and focuses on the highest-impact work
• You're comfortable owning the full stack of your work, from training code to the infrastructure it runs on
• You care deeply about model quality, efficiency, and reliability
• You're willing to step beyond your core responsibilities when the team needs it

Representative Projects

• Building hyperparameter search frameworks for SFT and Alignment workflows
• Increasing protein language model throughput during long context generation
• Updating existing model architectures to work and run efficiently on new GPU hardware
• Implementing a protein design pipeline that integrates prompt retrieval, sequence generation, attribute prediction, and structure prediction
• Establishing an ETL pipeline for sampling and tokenizing training datasets from an internal database of billions of sequences
• Developing a benchmarking and evaluation system for newly trained sequence generation models
• Contributing to the development of an internal service that provides transparent multi-node job submission for ML scientists

Qualifications

• BS or MS in Computer Science, Machine Learning, or a related field
• 3+ years of hands-on experience building and training ML models in PyTorch
• Strong Python and software engineering fundamentals, including testing, code quality, and version control
• Experience profiling, benchmarking, and optimizing ML model training and inference
• Experience implementing or optimizing transformer-based architectures
• Familiarity with cloud infrastructure and containerization (GCP, AWS, Azure, Kubernetes, Docker)
• Strong fundamentals in ML, statistics, and/or linear algebra

Preferences (but not required)

• Familiarity with protein language models or computational biology
• Experience with GPU-level optimization (CUDA, Triton)
• Experience with distributed training (DDP, FSDP, multi-node GPU clusters)
• Experience with databases and data processing pipelines
• Experience orchestrating multi-step ML workflows
• Experience building backend systems that serve ML models in production
• Contributions to open source ML projects or published research

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.

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.

As we continue to push the boundaries of what is possible, we’re seeking an ML Platform / MLOps Engineer on the machine learning team to build and operate the infrastructure that powers our machine learning systems. You will work closely with machine learning scientists, protein design scientists, and engineers to enable reliable, scalable platforms for training, evaluating, and deploying large-scale generative biology models.

As an early member of the company, you’ll have significant ownership over the systems and tools that enable our research team to move quickly from experiments to production models.

What You'll Work On

• Infrastructure supporting large-scale generative models for proteins
• Systems that process massive biological datasets
• Experimentation platforms that enable rapid iteration by ML researchers
• Production services powered by machine learning models

Responsibilities

• Develop infrastructure that enables researchers to run large-scale ML training and inference workloads reliably and efficiently on GPU clusters
• Implement and maintain security best practices across our ML infrastructure, including access control, secrets management, and least-privilege policies
• Monitor and optimize infrastructure performance, reliability, and cost
• Evaluate different open source infrastructure solutions and cloud providers
• Build and maintain machine learning pipelines to support model inference workloads
• Implement CI/CD pipelines for machine learning models and services
• Develop tooling that helps researchers move quickly from experiments to production models

Qualifications

• BS in Computer Science or a related field
• 3+ years of experience building or operating production ML systems
• Experience with MLOps, ML infrastructure, or ML platform engineering
• Strong experience with cloud infrastructure (GCP preferred)
• Experience working with containerized workloads and orchestration systems (Kubernetes, Docker)
• Experience building data or ML pipelines
• Familiarity with CI/CD and infrastructure-as-code practices

Preferences (but not required)

• Experienced with the challenges of working with large scale ML models
• Experience with transitioning research ideas into production
• Familiarity with ML frameworks such as PyTorch, MLFlow
• Interested in the intersection between biology and AI

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.

We're looking for a motivated and creative Machine Learning (ML) Scientist to drive research into new technologies for biomolecular design. This position offers an opportunity to work at the forefront of generative modeling research across language processing, representation learning, and protein engineering. You should be a self-directed researcher who has the ability to rapidly prototype and evaluate new models and algorithms in the biomolecular domain. 

As an early employee, you will proactively shape the direction of our machine learning efforts and collaborate across diverse teams of computational and experimental scientists.

Responsibilities

• Design and develop state-of-the-art deep learning methods for protein sequence, structure, and function prediction and apply them to protein design
• Curate relevant datasets and design tasks for rigorous evaluation of generative models 
• Collaborate with Biology team to design and characterize novel designed biomolecules
• 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

Qualifications

• PhD (or equivalent industry experience) in Computer Science, Machine Learning, Natural Language Processing, Applied Math, Computational Biology, Statistics, or a related field
• Experience with conceiving of, implementing, and evaluating novel machine learning techniques
• Publications at major machine learning conferences (NeurIPS, ICML, ICLR) or scientific journals (Nature, Science, Nature Biotech, Nature Methods, PNAS)
• Experience with modern deep learning frameworks such as Pytorch or Jax

Preferences (but not required)

• Familiarity with foundational biology of proteins and nucleic acids
• Experience developing machine learning models for proteins (language models, structure prediction, design)
• Previous experience in data extraction and curation from bioinformatics data sources
• Familiarity with wet lab experimental assays and associated limitations
• Experience with cloud compute platforms (GCP, AWS, Azure)

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.

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.