Ema builds AI Employees for HR, IT and Finance. Our AI Employees take on the busy work across the employee experience, from recruiting, onboarding and benefits to IT support, invoice processing and payroll, so people can spend their time on work that needs them. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs.
Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver, Ema is at the frontier of what Agentic AI can do in production. We ship real systems that run real business processes at scale. 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. 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. Agent post-training. compare process and outcome supervision, shape rewards, and distill useful frontier behavior into smaller models. Measure whether gains transfer beyond the training environment.
Turn enterprise workflows into reproducible training and evaluation environments: fixture tenants, simulated users who may get impatient and leave, and rewards grounded in verifiable outcomes. Find the shortcuts an agent can exploit before a training run optimizes for them.




Data engines and evaluation. Calibrate judges against human labels, construct behavior-level benchmarks from real workflows, and quantify data quality, performance uplift and reliability across stochastic runs.
Quality per dollar. You’ll go deep in a subset of these areas. Projects combine applied research with the engineering needed to make the result work in production.

Most projects develop over roughly four to six months, with useful improvements shipping along the way. You’ll define the problem and baseline, build the data or environment needed to test it, run experiments, and own serving and integration. Follow the system through deployment, monitoring and failure analysis until it is ready for a clear engineering handoff.
We’re hiring from junior through senior levels, with project scope matched to your experience.

Substantial work in at least one of agent/tool-use systems, post-training, reward modeling or RL environments, retrieval and memory, or evaluation design. A system, model or research project you took from an ambiguous problem to a working result. You can size an experiment, choose meaningful baselines and held-out tests,



and account for variation across tasks, seeds and repeated runs. You can distinguish a real improvement from judge bias, data leakage or a benchmark shortcut.
You would rather retire your own approach after a clean negative result than ship an improvement that disappears under a stronger evaluation.
A master’s or PhD in a relevant field, or equivalent work or research experience. Open-model post-training with TRL, veRL, OpenRLHF, or similar or a custom loop, especially debugging reward hacking or unstable optimization.
Interactive agent environments/harnesses for software engineering, web or tool use; large-scale trace analysis, data curation or synthetic generation.
Serving with vLLM or SGLang, distillation, quantization, or multi-node GPU training.
Practical security work on prompt injection, data governance or permission boundaries for agents that can act and improve themselves.
These are project-specific strengths; post-training experience is optional, and no candidate needs the entire list. Bring a repository, paper, model or technical write-up that lets us examine how you think and what you built.
Certain roles may be eligible for variable compensation, equity, and benefits.
Ema Unlimited is an equal chance employer and is committed to providing equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or genetics.

📌 Software Development Engineer - Machine Learning (Vancouver)
🏢 EMA
📍 Vancouver