Founding Machine Learning Engineer

About Novaflow

Novaflow is the AI data analyst for biology labs. Analytics is the bottleneck for scientific discovery. Biologists have to outsource analysis to expensive bioinformaticians or core services and wait weeks or months for results. We're solving this by putting analysis directly back into the hands of researchers, allowing them to go from raw data to publication-ready insights and visualizations in days instead of months. We're well-funded, growing extremely fast, and backed by leading investors including Y Combinator and BioRender.

About the Role

We're hiring a Founding Machine Learning Engineer to build the AI systems that power our platform. The core challenge: take a natural language query from a biologist and turn it into a correct, reliable bioinformatics workflow. That means working with LLMs, building evaluation systems to know when outputs are good (and when they're not), and shipping models that real scientists depend on every day.

This is a high-agency, high-ownership role. You'll work directly with the founders and have real say in our AI strategy. Biology background is not required. What matters is that you're a strong ML engineer who ships.

This role is in-person at our San Francisco office. The founding team works side by side every day to make fast decisions and ship.

What You'll Work On

• Build and improve the LLM-powered pipelines that turn natural language into bioinformatics workflows

• Design evaluation pipelines and benchmarks to measure model quality against real scientific expectations

• Experiment with fine-tuning, RAG, prompt engineering, and different model architectures

• Deploy and scale models in production on AWS or GCP

• Make build-vs-buy decisions on AI tooling and infrastructure

• Work across the team to integrate ML capabilities into the product

Requirements

• Bachelor's or Master's in Computer Science, Machine Learning, AI, or a related field (PhD is a plus)

• 2+ years building and deploying ML models in production

• Strong Python skills and hands-on experience with PyTorch, TensorFlow, or similar

• Real experience with LLMs: fine-tuning, RAG, prompt engineering, evals

• Experience working with large datasets and building data pipelines

• Familiar with AWS or GCP for model training and deployment

• Docker and CI/CD experience for ML workflows

• Can spot when an LLM is confidently wrong

Nice to Have

• Experience applying ML to scientific or biological data