About the Job
Ema is building the world’s leading Agentic AI platform to transform enterprise productivity. We enable organizations to delegate repetitive tasks to Ema, the Universal AI Employee, delivering 10x gains in workforce efficiency, across functions. Founded by former executives from Google, Coinbase, Flipkart, and Okta,...
We are backed by industry leading investors including Accel, Naspers/Prosus, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. 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...
You own one hard problem end to end. You write the proposal, build the system, design the evaluation, ship behind a gate, and finish with a write-up of what turned out to be true, including the parts that didn't work. You'll sit in the production codebase with a senior mentor and real production data. Recent residen...
Required Skills & Abilities
No specific degree required. Strong undergrads, grad students, and self-taught builders are all welcome; what matters is demonstrated depth in ML or agent systems.
Solid ML fundamentals and strong engineering: Python, PyTorch, and the discipline to ship in a large production codebase.
Real depth in at least one of: post-training (SFT/DPO/GRPO-family RL), reward modeling or LLM judges, agent and tool-use systems, retrieval and memory, eval design. One area you can teach us beats five you've touched.
Statistical literacy. You can size an experiment, and you know 25 samples at one seed is a datapoint, not a result.
Honest measurement as a habit. You'd rather kill your own feature with a clean experiment than ship it on a hunch.
Hands-on post-training with open models (TRL, veRL, OpenRLHF, or your own loop). Bonus points if you've debugged a reward-hacked run.
Built or trained in interactive agent environments (SWE, web, or tool-use gyms).
Large-scale trace analysis, data curation, or synthetic data work.
Serving and efficiency experience: vLLM/SGLang, distillation, quantization.
Multi-node GPU training, or the infra fluency to get there fast.
Publications, open-source work, or writing that shows how you think.
Security instincts: prompt injection, data governance, why a self-improving agent needs a fence.
Logistics
SF Bay Area, on-site/hybrid, half/full-time for the term. Flexible start.
Salary: $4,000 month
Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.
Ema Unlimited is an
