Applied Robotics AI Post-Training Scientist

Generalist AI, Inc.; Boston (MA); In loco; USD 140.000 - 230.000; Tempo pieno

Descrizione del lavoro

Generalist AI, Inc. in the United States is seeking a full-time ML roboticist to help train large pretrained robot models into production-ready systems. You will work across fine-tuning, RL-based steering, and evaluation against real-world tasks.

Ideal candidates have hands-on experience with embodied AI, robotics, and real-world ML systems, and can improve latency, robustness, and controllability while bridging foundation models and physical deployment.

Competenze

• Experience fine-tuning large models for downstream tasks (RLHF, IL, RL).
• Background in embodied AI, robotics, or real-world ML systems.
• Strong evaluation, benchmarking, and failure analysis.
• Ability to debug across ML stack from losses to robot behavior.

Mansioni

• Designing fine-tuning and adaptation strategies for downstream robotic tasks and embodiments
• Developing methods for improving reliability, robustness, and controllability
• Building evaluation frameworks that measure real-world robot performance, not just offline metrics
• Improving inference-time performance (latency, stability, memory footprint) in collaboration with ML infrastructure
• Leveraging techniques such as imitation learning, RL, distillation, synthetic data, and curriculum learning

Conoscenze

Fine-tuning large models

Robotics expertise

Evaluation & benchmarking

Debug ML stack

PyTorch

RL frameworks

Experiment tracking

Strumenti

PyTorch

RL frameworks

Experiment tracking

Descrizione del lavoro

Generalist AI, Inc. in the United States is seeking a full-time ML roboticist to help train large pretrained robot models into production-ready systems. You will work across fine-tuning, RL-based steering, and evaluation against real-world tasks.

Ideal candidates have hands-on experience with embodied AI, robotics, and real-world ML systems, and can improve latency, robustness, and controllability while bridging foundation models and physical deployment.