Research Scientist

We're working with a well-backed AI startup building frontier technology in a high-impact, real-world domain. The team is small, technical, and moving fast, with research sitting right at the core of the product. This is applied research - your work ships into the product and directly improves how the system performs. If you like doing real research and seeing it in users' hands quickly, rather than working purely on benchmarks or papers, this is that kind of environment. Areas of focus (depending on your background): Long-horizon and agentic systems - planning, tool use, and reliable multi-step autonomy Post-training - SFT, RL, preference optimisation, and evaluation design Continual learning and adaptation - how systems learn, retain, and improve over time without constant retraining What we're looking for: A strong applied-research track record where your work has translated into shipped improvements Depth in one or more of: agentic systems, post-training, continual/lifelong learning, memory and adaptation, or evaluation Genuinely strong engineering ability - you build, not just publish Comfortable with high ownership and ambiguity in a lean, fast-moving team An AI-native builder who's excited to work close to the code Details: Location: Bay Area - onsite/hybrid Comp: Competitive base + meaningful equity Process: Fast and lightweight - a matter of days, not weeks Publications at top venues are a plus, but shipped, applied impact matters more. #J-18808-Ljbffr