Jobs at Profluent

Owns the company’s IT infrastructure, including Google Workspace, identity and access, endpoint security, Meraki networking, cloud platforms, storage, backups, workstations, MDM, software licensing, and user support. Administers Rippling, Slack, Jira, GCP, Oracle Cloud, and Synology systems; maintains security controls, VPNs, MFA, and compliance evidence; manages device provisioning, patching, conferencing equipment, and disaster recovery. Provides hands-on troubleshooting and infrastructure planning in a high-growth startup environment.

Lead the design and development of the NGS analysis platform for gene editing and protein engineering while mentoring team members and collaborating with various teams.

The Machine Learning Scientist will develop reinforcement learning algorithms for protein design, collaborate with teams, and shape machine learning strategies.

Develop and evaluate generative and predictive deep learning models for protein and nucleic acid design; curate large biomolecular datasets; collaborate with computational and experimental teams to validate models and shape company ML strategy.

Lead and mentor a software engineering team to build and maintain a data platform for protein engineering, utilizing Python and cloud technologies.

Lead full accounting function for a high-growth, pre-IPO biotech: own technical accounting, monthly/annual close, R&D/CRO accounting, equity/ASC 718, internal controls, audit readiness, tax compliance, finance systems implementation, and treasury/cash management.

Build and maintain ML training and inference infrastructure, model APIs, and design pipelines. Partner with ML and protein design scientists to convert research into reproducible tooling and production services. Optimize compute and storage, implement CI/CD, monitoring, and follow engineering best practices to scale and accelerate protein design workflows.

Drive research and implementation of large-scale pretraining for protein generative models. Design autoregressive, diffusion, and representation-learning architectures, curate datasets, build pretraining infrastructure, evaluate models, and collaborate with computational and experimental teams to shape ML strategy for biomolecular design.