Lead Research Engineer , Ontology & Algorithms

Turn complex information into useful representations, learning algorithms, and systems that improve with evidence.

Lead hands-on research at the intersection of machine learning, structured knowledge, and algorithms. You’ll help define how Canopy learns from changing information, turn promising ideas into reproducible experiments, and decide which approaches deserve to become production systems. Work directly with the founders while staying close to the code.

02 / Your contribution

• Structure information into useful concepts, entities, and relationships, and develop algorithms that learn from and reason over them.
• Frame ambiguous research problems, choose useful baselines, and design experiments with clear success criteria.
• Build and compare models for evolving data; investigate where their assumptions break.
• Create reproducible training and evaluation workflows, and explain results in terms the team can act on.
• Work with engineering and validation to take useful prototypes into dependable systems, helping set technical priorities while continuing to write and improve the code.

03 / Your experience

• Strong foundations in machine learning, probability, statistics, and experimental design.
• Hands-on experience implementing research in Python and a modern ML framework.
• Evidence of independent technical work through research, deployed systems, open-source contributions, or substantial projects.
• Ability to distinguish an interesting result from a robust improvement and communicate the evidence clearly.
• A relevant degree or equivalent demonstrated experience. PhD-level research experience is welcome; a PhD is not required.

Helpful experience: Knowledge representation, ontologies, information extraction, representation learning, time-series modeling, or reasoning under uncertainty.

We value demonstrated ability and individual contribution. You don’t need to match every helpful qualification to apply.