Sit in on a customer session, understand how their agents are failing, design an eval that captures it, and drive a fix through to shipped improvement.
Close a piece of the outer loop end to end: production signal in, dataset out, eval scored, harness change shipped, metric moved.
Own a slice of our eval infrastructure: dataset curation, harness configuration, runner, analysis, and the comms back to engineering.
Prototype a new harness or context configuration and measure whether it actually moves the needle on real customer tasks.
Dig through pages of agent traces, build the tooling you need to make sense of them, and brief the team on what you found.
Partner with product and engineering on near-term shipping problems by bringing research rigour.
Pull a recent paper apart, work out what's actually transferable to our platform, and turn it into a concrete experiment.

4+ years shipping AI/ML products in a startup or applied industry setting
Demonstrated depth in at least one of the four skill areas above
Strong product and customer instincts: comfort joining customer calls, watching session recordings, and letting real workflows shape what you work on
Sharp evaluation judgement: benchmarks where they exist, vibes and quick prototypes where they don't, and the taste to know which is appropriate
Experience building datasets for evaluation or training
Deeply curious about agents and excited about reshaping how software is built.
Nice to have: A Masters or PhD in a relevant computational field
Direct experience with coding agents or code-generation systems
Background in RL, bandits, or other outer-loop optimisation frameworks applied to LLMs
Experience building synthetic data, dataset infrastructure, or internal tooling that other engineers actually used
A project you can show us (GitHub links welcome) and a thoughtful answer to "Why Tessl?"

Technical Staff Member – Research Engineer, Harness Engineering, Agentic Orchestration

Key skills