Robotics ML Scientist (Post-Training) – Real-World AI

Generalist AI, Inc.; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Generalist AI, Inc. is on a mission to train large pretrained robot models into production-ready systems via fine-tuning, reinforcement learning, steering, human feedback, task specialization, evaluation, and on-robot validation—at scale.

Regardless of your initial background, you will grow into becoming a full-stack ML roboticist capable of quickly pinpoint issues on either side of ML or controls, and all the places in between. This is where research meets reality.

Qualifications

• Experience fine-tuning large models for downstream tasks (RLHF, IL, RL, distillation, domain adaptation).
• Background in embodied AI, robotics, or real-world ML systems.
• Focus on evaluation, benchmarking, and failure analysis.
• Ability to debug across the ML stack from loss curves to robot behavior.
• Enjoy rapid iteration with real-world feedback loops.
• Interest in bridging foundation models with physical deployment.

Responsibilities

• Design fine-tuning and adaptation strategies for robot tasks and embodiments.
• Improve reliability, robustness, and controllability of systems.
• Build evaluation frameworks that measure real-world robot performance.
• Enhance inference-time performance in collaboration with ML infra.
• Leverage techniques like imitation learning, RL, distillation, synthetic data, curriculum learning.
• Close the loop between model outputs and physical-world outcomes.

Skills

Fine-tuning large models

Embodied AI

Robotics

Evaluation & benchmarking

Debug across ML stack

Iterative development

Job description

Generalist AI, Inc. is on a mission to train large pretrained robot models into production-ready systems via fine-tuning, reinforcement learning, steering, human feedback, task specialization, evaluation, and on-robot validation—at scale.

Regardless of your initial background, you will grow into becoming a full-stack ML roboticist capable of quickly pinpoint issues on either side of ML or controls, and all the places in between. This is where research meets reality.