Ema builds AI employees that carry out complex workflows across enterprise applications. Our ML team works on the loop that makes them better: production traces become data, data becomes training and evaluation, and better agents produce better traces. The hard part is deciding which intervention will improve behavior in the next real workflow
Harnesses and inference-time compute. Design context, tools, skills and orchestration for multi-step agents, including work across documents, slides, images, audio and video. Test where extra reasoning, search or verification earns its latency and cost. Build self-improvement loops with explicit permissions, evaluation gates and rollback
Agent post-training. Curate trajectories for SFT, optimize preferences, or run RL on real agent tasks. Investigate methods such as DPO, GRPO or DAPO where they fit; compare process and outcome supervision, shape rewards, and distill practical frontier behavior into smaller models. Measure whether gains

📌 Machine learning software engineer (Vancouver)
🏢 Anonymous
📍 Vancouver