FluidstackAustin, TXBuild and own ML/LLM systems for internal operations including forecasting, risk flagging, and document extraction. Ship production agentic systems end-to-end with guardrails and partner with data engineering to integrate predictions into tools.
Build ML and LLM systems that run inside the company's operations: forecasting build timelines, flagging schedule risk, and extracting structure from vendor documents. Own models end to end, from problem framing and data through deployment, evaluation, and iteration in production. Ship agentic systems with real guardrails, authorization, audit, and evals, so agents act on company systems instead of just advising. Partner with data engineering and product pods to put predictions in the tools people already use.
Deploys state-of-the-art ML models and fine-tunes LLMs in production to enhance platform integrity and safety against adversarial threats. Requires Master's/PhD, deep learning expertise, PyTorch/TensorFlow proficiency.
Lead a team developing large-scale RL and ML models for autonomous vehicle behavior planning and driving decisions. Requires RL/ML expertise, production ML pipeline experience, and 3+ years in leadership.
Builds core infrastructure for AI software agents in Devin and Windsurf, focusing on long-horizon task execution, tool use, planning, and reliability at scale. Requires strong Python, systems engineering depth, and AI curiosity; onsite in San Francisco.
Designs and optimizes TPU kernels to address performance issues in ML research, training, and inference systems. Provides feedback on model impacts and solves large-scale systems problems, requiring deep accelerator expertise.
Designs and runs experiments to improve oversight of increasingly capable AI models, including model training, evaluation, and deployment of practical systems. Analyzes failures and develops techniques to train more aligned models using oversight signals.