The posting
Why this role matters
Biomanufacturing is one of the most complex and failure-prone systems in the world. Every year, billions of dollars and critical therapies are delayed or lost — not because the science fails, but because process understanding and manufacturing execution break down somewhere along the way. It’s a systems problem more than a software or biology problem, and we’re building the AI platform to solve it.
What we’re building
Axella Biosciences is a stealth mode company developing an AI-driven CMC and biomanufacturing execution platform that designs manufacturing processes in silico, reduces deviations and failures during development and GMP production, and helps diagnose and resolve issues in real time. We’re already in production with early customers, and now we’re scaling into regulated environments (GxP, 21 CFR Part 11), across more of the biomanufacturing lifecycle, and toward autonomous, agent-driven execution.
We’re backed by top VCs such as General Catalyst, Sequoia, and Hitachi Ventures, among others.
The role
We’re hiring a foundational technical leader to own engineering execution end-to-end — architecture, AI systems design, infrastructure, and the engineering standards and culture we build from here. You’ll work closely with domain experts across biomanufacturing and AI.
What you’ll work on
AI systems and agents. You’ll build and deploy agentic systems based on frontier models (Claude, Open AI) for real manufacturing workflows, design tool-augmented agents using MCP and structured data access, improve our RAG systems (retrieval quality, grounding, citations, reasoning), and develop evaluation frameworks — gold sets, regression testing, live metrics.
Core platform. Full-stack development across React, Node.js, and MongoDB. Building document intelligence pipelines for batch records, PDFs, Excel files, and images, and turning unstructured manufacturing data into structured, usable knowledge.
Infrastructure and scale. Evolving our AWS architecture from early-stage to production-grade, implementing observability, reliability, and CI/CD, and designing systems that hold up in regulated environments (GxP, Part 11, SOC 2).
Customer deployment. Getting systems into real manufacturing environments, integrating with LIMS, ERP, and QMS, and working directly with customers and partners to validate how these systems perform in the wild.
What we’re looking for
The core: 7+ years of software engineering experience with a proven track record of delivering production systems end-to-end in startup environments — including problem scoping, requirements gathering, product planning, architecture, modern web backend and frontend development, infrastructure, and maintenance. You're comfortable identifying which problems are worth solving, not just executing on well-defined ones, and you can drive technical direction independently in ambiguous environments.
Candidates must have strong production experience owning LLM-powered systems (RAG, agents, tool use) — including the reliability, evaluation, and iteration strategy that keeps them working as they scale. You should be comfortable operating production systems in cloud environments. Experience with infrastructure as code tools such as Terraform is a plus, as is experience with AWS, Typescript, React, Node.js and GitHub Actions.
We're looking for a bar raiser who works independently and can build cross-functional alignment around the technical practices that matter — things like code coverage, evaluation infrastructure, and observability — and drive them to adoption without heavy process.
Things that stand out: experience with agent frameworks or MCP-style architectures, building evaluation systems for LLM reliability, document parsing and structured extraction pipelines, work in regulated or high-reliability environments, and any exposure to biotech, pharma, or complex industrial systems.
What makes this role unique
The work has real-world impact — your systems shape how drugs are manufactured. The problems are genuinely hard: reasoning over messy, high-stakes data in regulated environments. You’ll have high autonomy and own major technical decisions, and as a foundational hire, the engineering culture and architecture will reflect the choices you make. The system also gets better with every deployment, which is a fun place to be.
Compensation
Competitive salary plus meaningful early-stage equity.
