Member of Technical Staff - Research Engineer

One of our startup clients in San Francisco, CA is looking to hire Research Engineer who will design experiments, build task generation systems, run evaluations, inspect model failures, and develop methods for mining tasks that are just out of reach of today's agents. The work is empirical, systems-heavy, and close to the frontier. You will consume real-world trajectories or researcher hypotheses, materialize realistic data, propose candidate tasks, benchmark those tasks against frontier computer-use and agent models, and hill-climb until you find the failures that produce useful learning signal. This is not a role for someone who only wants to run experiments or only wants to write research code. You will own the full loop: hypothesis, implementation, evaluation, analysis, iteration, and productionalization. You Will Work On Discover model failure modes from real-world traces, agent telemetry, targeted researcher hypotheses, and customer workflows. Generate realistic curricula grounded in actual workflows rather than toy synthetic benchmarks. Benchmark candidate tasks against frontier CUA and agent models using pass rates, rollouts, and behavioral traces as difficulty signals. Build hill-climbing loops that mutate, filter, and rescore tasks until they surface high-signal targets. Study reward hackability, distribution mismatch, task realism, long-horizon failures, and transfer from simulation to deployed agents. Turn research prototypes into reliable internal systems for continuous curriculum generation. Python, PyTorch, Reinforcement Learning, LLM Agents, Computer-Use Agents, Synthetic Data Generation, Model Evaluation, Simulation Environments #J-18808-Ljbffr Tykhe Inc