Torc Robotics

Senior Machine Learning Engineer- Learned Planning/Reinforcement Learning

Vaga remota de Machine Learning Engineer com fit claro de localização do candidato.

Publicada14 de jul. de 2026

Países elegíveis1 país aceito

Sinal de senioridadeSenior

Modelo de trabalhoRemoto

Locais aceitos para candidatos

Estados Unidos

Resumo da vaga

Conteúdo da vaga extraído em seções para revisão mais rápida.

Design, develop, and deploy learned behavior models using approaches such as reinforcement learning, behavior cloning, and imitation learning
Own end-to-end model development for scoped problem areas, from data ingestion and training to evaluation and deployment
Write production-quality ML code to support scalable training, evaluation, and inference workflows
Analyze model performance, identify failure modes, and iterate to improve robustness and generalization across driving scenarios
Contribute to training pipelines, data workflows, and infrastructure, including working with large-scale datasets from simulation, fleet logs, and on-vehicle data
Collaborate with simulation, validation, and autonomy teams to test and evaluate learned behavior models across diverse environments
Support integration of learned planning models into simulation and validation frameworks, enabling faster iteration and improved coverage
Contribute to model architecture discussions and technical decision-making within the team
Mentor junior engineers on implementation, experimentation, and best practices

Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or related technical field with 6+ years of industry experience, OR Master’s degree with 3+ years OR PhD with 1+ years of experience
Experience applying reinforcement learning, imitation learning, or sequence modeling to robotics, autonomous systems, or complex control problems
Strong programming skills in Python and PyTorch, with experience writing production-quality ML code
Experience training, evaluating, and improving models using large-scale datasets and distributed compute environments
Solid understanding of ML architectures used in autonomy systems (e.g., transformers, RNNs, graph neural networks, policy networks)
Experience debugging model behavior, analyzing performance metrics, and improving model reliability
Ability to translate ambiguous problems into structured ML solutions and deliver results independently
Experience collaborating cross-functionally to integrate ML models into larger autonomy systems

Experience in autonomous driving, robotics, or simulation-based training environments
Experience with reinforcement learning frameworks or distributed training systems (e.g., Ray)
Experience working with simulation environments, scenario generation, or large-scale behavior datasets
Familiarity with vehicle dynamics, motion planning, or multi-agent decision-making systems
Experience deploying ML models into production or real-world robotics systems
Experience with learned planning systems or policy learning in real-world or simulation environments
Experience integrating learned behavior models into validation and V&V workflows
Background in multi-agent modeling, driver behavior modeling, or long-horizon decision-making systems