Torc Robotics

Translator

• Design, build and optimize ML models for localization, including learning-based pose estimation, map-matching, and sensor-fusion pipelines using data from cameras, LiDAR and radar.
• Develop high-performance training and evaluation workflows, leveraging frameworks such as PyTorch, distributed training infrastructure, and large-scale datasets.
• Collaborate with robotics and mapping engineers to integrate localization models into the autonomy stack, ensuring performance and stability to meet real-time constraints.
• Analyze failures, run ablation studies, improve model robustness, and conduct rigorous experiments to achieve production-grade reliability.
• Contribute to system design, code reviews, best practices, and documentation across the ML and autonomy organization.

Senior Machine Learning Engineer – Localization

Machine Learning Engineer

• Design, build, and optimize ML models for localization, including learned pose estimation, map-matching, and sensor fusion pipelines using camera, LiDAR, and radar data.
• Develop high-performance training and evaluation workflows, leveraging frameworks such as PyTorch, distributed training infrastructure, and large-scale datasets.
• Collaborate with robotics and mapping engineers to integrate localization models into the autonomy stack, ensuring performance, stability, and real-time constraints are met.
• Analyze failure cases, run ablations, improve model robustness, and drive rigorous experimentation to achieve production-level reliability.
• Contribute to system design, code reviews, best practices, and documentation across the ML and autonomy organization.

Senior ML Engineer – Learned Localization

• You will participate in the design, maintenance, and improvement of Torc's ML frameworks, which include the following tools:
• Cloud tools: Terraform, AWS Managed Services, EKS, Ray
• Data operations: Parquet processing (PyArrow, Daft, Pandas), vector databases / LanceDB, MCAP/ROS
• ML training and fine-tuning pipeline: Python, Ray, PyTorch, Lightning, Gymnasium
• Logging and aggregation, experiment tracking, model registry and lineage: visualization and dashboards (e.g., Tableau, Voxel51, Rerun)

Senior Machine Learning Engineer – ML Operations Frameworks

• Design, implement, test, and deploy offline object detection, tracking, and fusion modules to automatically generate annotations on cloud services from recorded sensor data (cameras, lidars, radars)
• Demonstrated project management skills, acting as a project lead guiding a team whose members have limited experience across multiple aspects of project execution
• Stay current with the latest developments in AI and machine learning for autonomous driving
• Independently develop models or algorithms for offline perception
• Document created applications
• Define and implement ingestion, preprocessing, curation, and governance of large, multidimensional datasets to support models and analytical workflows
• Proactively assess current capabilities to identify areas for improvement and propose solutions aligned with core strategy and operations
• Measure and monitor the quality of automatic labeling to meet internal customer requirements
• Guide and produce insights, supporting data visualization and accessibility in a customer-focused manner
• Evaluate and recommend technical advances that improve productivity and quality, reduce time-to-delivery, and strengthen operational reliability
• Develop guidelines and standards for analytical and machine learning models, their deployment, and associated processes
• Provide technical guidance or business-process expertise, technical leadership, coaching, and mentorship to team members.

Senior Engineer – Machine Learning, Offline Perception

• Lead BEV model development: define and execute the technical roadmap for BEV-based perception models across multiple tasks (e.g., detection, segmentation, road topology and scene understanding).
• Design advanced multimodal architectures that fuse heterogeneous sensor data (camera, LiDAR, radar, HD maps) into unified spatial representations.
• Develop foundational perception models leveraging BEV transformers, voxel-based encoders, or implicit scene representations.
• Own large-scale training workflows, from data sampling strategies and augmentation pipelines to distributed training and hyperparameter optimization.
• Improve model robustness and generalization, accounting for long-tail conditions such as low visibility, occlusions, and rare scene configurations.
• Establish evaluation frameworks for geometric accuracy, temporal stability, and cross-domain transfer performance.
• Work cross-functionally with sensor calibration, mapping, and fusion teams to ensure cohesive perception model interfaces.
• Mentor and guide machine learning engineers, cultivating best practices in experimentation, code quality, and model validation.

Staff Machine Learning Engineer – BEV

• Own the model roadmap for road and lane detection within the perception machine learning organization, from concept to production-grade model maturity.
• Research, design, and train advanced neural architectures (e.g., multi-camera BEV transformers, LiDAR-vision fusion models, topological lane-graph networks) to detect, segment, and model roadway structures and lane connectivity.
• Lead the data strategy for this domain: define data curation, labeling policies, and active learning pipelines to capture long-tail scenarios (e.g., occlusions, complex merges, construction zones).
• Develop robust metrics and evaluation frameworks for lane and road geometry accuracy, temporal consistency, and cross-domain generalization.
• Advance foundational capabilities such as self-supervised pretraining, synthetic-to-real adaptation, and temporal modeling for road and lane understanding.
• Run large-scale experiments: design, execute, and analyze distributed training workflows and ablation studies to identify scalable improvements.
• Collaborate with other perception teams to ensure model and interface consistency.
• Mentor engineers and scientists, defining best practices for training, model evaluation, and code quality.
• Stay at the cutting edge of research by evaluating and adapting emerging techniques (e.g., large BEV-based models, vectorized map prediction, lane-graph transformers) to production-grade perception.

Staff Machine Learning Engineer – Machine Learning, Road and Lane Detection

• Lead the design and development of E2E models, defining architectures that directly map multimodal sensor inputs (camera, LiDAR, radar, HD maps) to driving actions or mid- to high-level cost/objective functions.
• Lead large-scale training and evaluation for E2E learning, integrating data from perception, behavior prediction, and control systems.
• Develop and refine learning objectives aligned with real-world driving criteria: safety, comfort, compliance, and efficiency.
• Build scalable pipelines for multitask, multimodal learning, leveraging both real-world and synthetic data.
• Prototype and evaluate new paradigms such as differentiable planning, imitation learning, reinforcement learning, and world models for autonomous vehicle behavior.
• Collaborate cross-functionally with Perception, Prediction, and Motion Planning teams to align interfaces and ensure consistency between learned and modular components.
• Establish robust evaluation frameworks for E2E performance, including closed-loop simulation and on-road validation.
• Mentor engineers and scientists in large-scale experimentation, model interpretability, and data-driven debugging.
• Stay at the forefront of machine learning research, exploring advances in foundation models, sequence modeling, self-supervision, and generative world representations.

Staff Machine Learning Engineer – E2E

• Lead E2E model design and development — define architectures that directly map multi-modal sensor inputs (camera, LiDAR, radar, HD maps) to mid- or high-level driving actions or cost functions.
• Drive large-scale training and evaluation for E2E learning, integrating data from perception, behavior prediction, and control systems.
• Develop and refine learning objectives that align with real-world driving metrics: safety, comfort, compliance, and efficiency.
• Architect scalable pipelines for multi-task, multi-modal learning, leveraging both real-world and synthetic data.
• Prototype and evaluate new paradigms such as differentiable planning, imitation learning, reinforcement learning, and world models for AV behavior.
• Collaborate cross-functionally with Perception, Prediction, and Motion Planning teams to align interfaces and ensure consistency between learned and modular components.
• Establish robust evaluation frameworks for E2E performance, including closed-loop simulation and on-road validation.
• Mentor engineers and scientists in large-scale experimentation, model interpretability, and data-driven debugging.
• Stay at the frontier of ML research, exploring advancements in foundation models, sequence modeling, self-supervision, and generative world representations.

Staff ML Engineer – E2E

• Lead architecture, development, and validation of learned behavior models (e.g., driver mimicry, multi-agent interaction, behavior cloning, reinforcement learning) for highway and freight-truck autonomy.
• Define and implement data strategies: collect, label, and curate large behavior datasets (in-vehicle, simulation, fleet logs) for training and evaluation.
• Develop scalable model training pipelines, infrastructure, and tooling to iterate quickly on behavior models, from prototype to production deployment.
• Design and track performance metrics, analyze model behavior, diagnose failure modes, and drive continuous improvement of learned behaviors in simulation and on-vehicle.
• Work with simulation, scenario, and validation teams to embed learned behavior models into verification and validation frameworks, ensuring coverage across operational design domains (ODDs).
• Mentor and lead mid and senior-level ML engineers, set technical direction, and drive best practices in learned behavior engineering across the team.

Staff ML Engineer – Learned Behaviors

• Own the model roadmap for Road & Lane Detection within the Model Dev ML org — from concept through production-grade model maturity.
• Research, design, and train advanced neural architectures (e.g., multi-camera BEV transformers, LiDAR-vision fusion models, topological lane graph networks) to detect, segment, and model road structures and lane connectivity.
• Lead data strategy for this domain — defining data curation, labeling policies, and active learning pipelines to capture long-tail scenarios (e.g., occlusions, complex merges, construction zones).
• Develop robust metrics and evaluation frameworks for lane and road geometry accuracy, temporal consistency, and cross-domain generalization.
• Advance foundational capabilities such as self-supervised pretraining, synthetic-to-real adaptation, and temporal modeling for road and lane understanding.
• Drive large-scale experiments — designing, running, and analyzing results from distributed training workflows and ablations to identify scalable improvements.
• Collaborate with other model dev/perception teams to ensure model coherence and interface consistency.
• Mentor engineers and scientists, setting best practices for model training, evaluation, and code quality.
• Stay ahead of the research frontier by evaluating and adapting emerging techniques (e.g., BEV-based large models, vectorized map prediction, lane graph transformers) to production-grade perception.

Staff ML Engineer – Road & Lane Detection

Senior ML Engineer – Localization

Software Engineer

• Lead the development and optimization of ML inference pipelines on embedded hardware, including model conversion (PyTorch/ONNX), TensorRT integration, and CUDA-based pre/post-processing.
• Design, review, and guide development of custom CUDA kernels to support proprietary model layers and performance-critical operations.
• Drive technical execution across model optimization, inference scalability, benchmarking, and real-time system integration.
• Ensure high-quality C++ and CUDA code through robust design, documentation, and test coverage.
• Integrate optimized models and processing stages into Torc’s Application Engine and support Virtual Driver teams in adopting the optimized inference layer.
• Hire, lead, and develop a high-performance engineering team, building a culture of ownership, collaboration, and continuous improvement.
• Set technical and operational goals aligned with company-wide objectives; define and track team KPIs and milestones.
• Provide coaching, career development, and skill-building opportunities for engineers; maintain development plans and performance expectations.
• Establish and improve engineering processes for planning, delivery, testing, documentation, and cross-team collaboration.
• Reinforce Torc’s values through transparent communication, conflict resolution, and proactive change leadership.

Manager, Engineering – Hardware Acceleration, CUDA

• Lead BEV model development: Define and execute the technical roadmap for BEV-based perception models across multiple tasks (e.g., detection, segmentation, road topology, and scene understanding).
• Design advanced multi-modal architectures that fuse heterogeneous sensor data (camera, LiDAR, radar, HD maps) into unified spatial representations.
• Develop foundational perception models leveraging BEV transformers, voxel-based encoders, or implicit scene representations.
• Own large-scale training workflows — from data sampling strategies and augmentation pipelines to distributed training and hyperparameter optimization.
• Advance model robustness and generalization, addressing long-tail conditions such as low visibility, occlusions, and rare scene configurations.
• Establish evaluation frameworks for geometric accuracy, temporal stability, and cross-domain transfer performance.
• Collaborate cross-functionally with sensor calibration, mapping, and fusion teams to ensure cohesive perception model interfaces.
• Mentor and guide ML engineers, cultivating best practices in experimentation, code quality, and model validation.
• Stay at the forefront of ML research, exploring self-supervised learning, large-scale pretraining, or foundation models for 3D perception.

Senior Machine Learning Engineer – Localization

Senior ML Engineer – Learned Localization

Senior Machine Learning Engineer – ML Operations Frameworks

Senior Engineer – Machine Learning, Offline Perception

Staff Machine Learning Engineer – BEV

Staff Machine Learning Engineer – Machine Learning, Road and Lane Detection

Staff Machine Learning Engineer – E2E

Staff ML Engineer – E2E

Staff ML Engineer – Learned Behaviors

Staff ML Engineer – Road & Lane Detection

Senior ML Engineer – Localization

Manager, Engineering – Hardware Acceleration, CUDA

Staff ML Engineer – BEV

Staff ML Engineer – E2E

Staff ML Engineer – Learned Behaviors

Staff ML Engineer – Road & Lane Detection