Profluent Bio

Research Engineer

• 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

Machine Learning Research Engineer

Controller

• Serve as Profluent's in-house technical accounting authority; research and prepare position memos on complex transactions, new GAAP pronouncements, and company-specific accounting policy decisions
• Executes general ledger operations, leading monthly, quarterly, and annual financial close process to ensure accurate and timely US GAAP financial statements
• Drive R&D accounting across CRO and vendor accruals, prepaids, and program-level cost tracking, bringing structure and visibility to one of the company's largest spend categories
• Build and deliver financial reporting packages and board-level management decks; translate financial results and key metrics into clear narratives for executive leadership and the Board
• Own equity accounting and plan administration under ASC 718 (including stock-based compensation expense, 409A valuation support, cap table integrity, and related financial statement disclosures) in partnership with the equity platform (Carta or equivalent)
• Design, implement, and maintain an internal control framework scaled for a high-growth tech company, balancing rigor with operational practicality as the company grows
• Lead audit readiness and serve as the primary point of contact for external auditors; drive clean, timely delivery of annual financial statement audits
• Manage federal, state, and local tax compliance in coordination with external advisors; lead R&D tax credit studies and other R&D-related tax matters, including Section 174 capitalization considerations
• Establish, document, and maintain accounting infrastructure, policies and procedures that meet institutional standards and support rigorous audit and regulatory scrutiny
• Evaluate and implement finance technology, including procure-to-pay and close management tools, to reduce manual effort, improve auditability, and strengthen data integrity at scale
• Own cash management, banking relationships, and treasury operations, including cash forecasting and liquidity monitoring appropriate for a venture-backed company at Profluent's stage

Translator

• Build and oversee a high-performing liver biology and translational pharmacology organization, including direct management of program and in vivo pharmacology leaders.
• Lead scientific strategy and execution for 2–3 liver-directed genetic medicine programs, translating Profluent’s customized base editor platform into a differentiated portfolio for severe inborn metabolic diseases.
• Provide accountable leadership for cross-functional therapeutic program teams, integrating translational biology, genome editing, protein engineering, delivery, pharmacology, bioanalytics, CMC, regulatory, and preclinical development expertise.
• Partner with functional leaders to define program resourcing, critical-path activities, timelines, risks, and key experiments required to deliver development candidates and IND-ready data packages.
• Guide in vivo pharmacology strategy across rodent and NHP studies, including model selection, dose strategy, biodistribution, editing activity, durability, target engagement, pharmacodynamic biomarkers, tolerability, and translational relevance.
• Bring deep liver and metabolic disease expertise to the design of experiments that distinguish mechanism, causality, pharmacology, and therapeutic impact across genetically defined disease contexts.
• Apply strong scientific judgment to experimental design, data interpretation, and program decision-making, ensuring studies are efficient, well-controlled, and capable of supporting clear go/no-go decisions.
• Translate Profluent’s established liver portfolio vision into actionable program strategy for selected targets, while contributing to future prioritization of additional genetically defined opportunities in liver and other tissues.
• Represent program strategy, data, risks, and recommendations to executive leadership, board members, external advisors, potential partners, investors, and collaborators.
• Cultivate a collaborative, execution-oriented culture that moves quickly while maintaining scientific rigor, thoughtful prioritization, and high standards for data quality.

Executive Director – Vice President, Biology & Translational Pharmacology

Designer

• Partner with ML and Protein Design leads to scope MDE campaigns; including target selection, choice of assays, and what readouts are needed to improve the next generation of models
• Define assay readiness levels, controls, and QC acceptance criteria for each campaign, and enforce them across internal execution and external CROs
• Review incoming data (biochemical, biophysical, NGS-based screens) for scientific soundness before it is accepted into the data warehouse and used for model training
• Work with Bioinformatics on metadata schemas and data ingestion so that every dataset is consistent, queryable, and traceable back to its parent project.
• Serve as the scientific point of contact for CRO technical scoping; evaluate whether a vendor’s platform, assay conditions, and controls are fit for purpose for ML-guided optimization
• Contribute to campaign charters, benchmarking assay design, and post-campaign readouts; help turn individual experiments into a growing institutional dataset
• Represent the MDE program in cross-functional technical forums and help raise the data-quality bar across the organization

Scientist II, ML – Guided Protein Design Evaluation

General

• Design and implement computational approaches to advance cutting-edge research in bioinformatics and machine learning
• Analyze large-scale genomic and protein datasets to uncover new biological insights and capabilities
• Collaborate closely with scientists and engineers across machine learning, protein engineering, and biology teams
• Document and present research findings internally, with the potential to publish in leading journals and conferences

Research Intern, Computational

• Lead design and implementation of scalable NGS analysis pipelines supporting gene editing and protein engineering workflows
• Develop clear, intuitive visualizations and reports to communicate sequencing results and biological insights
• Manage cloud-based compute environments and data systems for large-scale datasets
• Partner with biology teams to analyze results, troubleshoot, and optimize experimental design
• Collaborate with machine learning teams to curate data for model training
• Establish best practices for code review, testing, and CI/CD in computational workflows
• Mentor junior scientists and foster a culture of rigor, reproducibility, and innovation

Lead Bioinformatics Scientist, NGS

Project Manager

• Translate partner milestones into internal execution plans
• Create, track, and dynamically update detailed Gantt charts across multiple concurrent programs
• Partner closely with the Alliance Manager to monitor project health
• Utilize project mapping tools and ML/Bioinformatics dashboards to generate clear status reports, burn-down charts, and timeline visualizations

Biotech Project Manager, Early Discovery

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