Lead Platform Engineer
BioLM is building an AI platform for molecular discovery, enabling researchers to design and analyze biological sequences using state-of-the-art language models.
We are seeking a Lead Platform Engineer to own the architecture, development, and evolution of our core product platform. You'll work directly with the founding team to make high-leverage technical decisions - and be accountable for the reliability, scalability, and quality of what we ship.
Responsibilities
Ship products and features with startup-caliber velocity and a high bar for quality
Lead the development of agentic workflows for molecular design and discovery
Build and extend platform UI features that enable researchers to create, manage, and deploy fine-tuned protein and DNA models
Integrate our Console, Builder, and Solutions apps into a seamless full-stack experience for molecular development
Build API integrations with sequence and assay ordering providers, including internal data modeling and full-stack UI development
Experience
5+ years of full-stack engineering experience, with a track record of shipping production systems end-to-end, from infrastructure to user-facing interface
Led technical projects - scoped work, delegated effectively, and delivered on time
Designed and operated cloud infrastructure at scale
Built and maintained complex third-party integrations or API-driven systems
Brought product instincts and UI sensibility to ambiguous, early-stage problems
Required Qualifications
Deep proficiency in Python and React/Node.js
Strong command of cloud infrastructure (AWS or equivalent), CI/CD, and Docker
Solid understanding of relational data modeling and API design patterns
Experience with REST, WebSockets, and modern integration patterns including agentic/MCP-based systems
A good eye for UI design and the product thinking to match
Preferred Qualifications
Experience building tooling for scientific, research, or ML/AI-adjacent workflows
Familiarity with interactive data visualization (D3.js, Plotly, Vega-Lite)
Experience integrating or deploying large-scale ML model APIs or agentic AI systems
Background or genuine interest in biology, bioinformatics, or computational life sciences
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