Member of Technical Staff, Model Training (applied research)

• Train, fine-tune, and adapt language, vision-language, and robotics models using supervised fine-tuning (SFT), reinforcement learning (RL), imitation learning, and distillation.
• Develop specialized models for complex applications, including multimodal understanding, robotic perception and control, and autonomous agents capable of reasoning, planning, and tool use.
• Build simulation-based training and evaluation environments to study policy learning, task generalization, and the transfer of capabilities from simulated to real-world settings.
• Combine expert demonstrations, human feedback, synthetic datasets, and environment-generated trajectories into effective training datasets, reward signals, and learning curricula.
• Develop specialized models that power the hiring partner's flow ecosystem, improving data generation, annotation, quality assurance, and automated grading workflows.
• Incorporate expert corrections and model performance feedback into iterative training cycles to continuously improve accuracy, robustness, and task success.
• Optimize models for production inference, balancing quality, latency, throughput, and computational efficiency against frontier-model baselines.
• Collaborate with research and platform engineering teams to transition promising prototypes into scalable, production-ready model deployments.

• Strong foundations in machine learning and deep learning, with hands-on experience training, adapting, and evaluating modern AI models.
• Practical experience in one or more areas, including large language models, vision-language models, robotics, reinforcement learning, or autonomous agents.
• Strong proficiency in Python and PyTorch, with the ability to develop reliable training, evaluation, and inference pipelines.
• Experience translating application requirements into measurable model objectives, benchmarks, and performance improvements.
• Solid software engineering fundamentals, including the ability to build maintainable systems and move research prototypes toward production.
• Familiarity with model adaptation techniques such as fine-tuning, distillation, imitation learning, or reinforcement learning.
• Strong experimental judgment and the ability to independently identify performance bottlenecks, evaluate trade-offs, and iterate toward effective solutions.

• Experience with robotics, embodied AI, vision-language-action models, or simulation-based policy learning.
• Familiarity with agent frameworks, tool-using models, and environment-driven training.
• Experience with synthetic data generation, human-in-the-loop learning, or automated evaluation systems.
• Knowledge of inference optimization techniques, including quantization, batching, model compression, and efficient serving.
• Experience deploying specialized models in production environments with demanding latency, reliability, or cost requirements.
• Research contributions or open-source work in applied machine learning, multimodal AI, robotics, or agent systems.

Project and contracting process: the hiring partner. Applications continue on the provider's website.