About us

Chipper develops AI agents to enhance operations within life-science environments such as R&D labs, diagnostics, CRO/CDMOs, and biomanufacturing facilities. By continuously analyzing instrument logs and operational data, Chipper's autonomous AI agents predict failures, flag anomalies, prevent errors, and optimize equipment performance. The platform helps reduce downtime, enhance throughput, decrease operational costs, and improve processes over time. Chipper is leading innovation in automating complex workflows across modern labs and industrial biomanufacturing settings.

The role

This role is for a true early-stage builder who thrives in ambiguity. As the founding ML Engineer, you'll design and build the platform that creates a machine learning model of our customers' labs — modeling the state and behavior of instruments, samples, and workflows to enable intelligent automation and decision support.

This role blends machine learning, systems engineering, and product ownership. You'll move quickly from research to production while building infrastructure that supports reliable model deployment and agentic workflows. You'll have a direct line to the founding team and significant influence over technical direction.

What you'll do

• Design and train predictive and anomaly detection models on real-world lab instrument data (HPLC, NGS, and beyond).
• Build and own end-to-end ML pipelines: data ingestion, feature engineering, experimentation, evaluation, and continuous improvement.
• Deploy ML models and monitoring systems into production environments used by pharma and diagnostics customers.
• Architect and implement agentic workflows that coordinate multiple tools, APIs, and data sources to automate lab operations.
• Work directly with customers to understand their data, validate model performance, and iterate quickly.
• Help define the technical roadmap and contribute to product strategy as an early team member.

About you

• 3+ years of ML engineering experience, ideally in an early stage startup.
• Experience designing and training predictive models for real-world operational data.
• Experience building ML pipelines for experimentation, evaluation, and continuous improvement.
• Experience deploying ML systems into production environment.
• Experience building agentic workflows that coordinate multiple tools, APIs, and data sources.
• High ownership mindset and desire to help shape company direction.