Torc Robotics

Machine Learning Engineer

• Design, develop, and deploy production machine learning models supporting Torc's perception systems.
• Own end-to-end model development for scoped perception problem areas, from data preparation and training through 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, accuracy, and generalization across diverse driving environments.
• Develop and optimize perception models leveraging multi-modal sensor data, with an emphasis on camera-based and 3D perception systems.
• Collaborate with software engineers, infrastructure teams, and autonomy engineers to integrate perception models into production autonomy software.
• Support improvements to training pipelines, data workflows, and tooling that accelerate model iteration and deployment.
• Contribute to model architecture discussions and technical decision-making within the team.
• Mentor junior engineers on implementation, experimentation, and machine learning best practices.

Senior Machine Learning Engineer – 3D Perception

SDET

• Design, develop, and maintain HiL test benches to support validation of autonomous driving software and system-level functionality
• Define and execute test strategies, test cases, and validation plans based on system requirements, safety goals, and integration needs
• Develop and maintain automated test scripts and frameworks to improve test coverage, repeatability, and efficiency
• Integrate hardware and software components into HiL environments, managing interfaces, data flows, and synchronization across subsystems
• Conduct verification and validation (V&V) activities in alignment with safety standards such as ISO 26262
• Support the integration and validation of new autonomy features across perception, sensor fusion, and vehicle control systems
• Utilize tools and frameworks such as ROS/ROS2, CAN-based systems, and simulation platforms for testing and debugging
• Analyze test results, investigate system-level issues, and collaborate cross-functionally to drive resolution
• Document test procedures, results, and defects, and communicate findings to engineering and program stakeholders
• Continuously improve HiL infrastructure, tooling, and workflows to support scalable and efficient testing

Senior System Test Engineer – HiL

Full-stack Engineer

• Design, develop, and optimize motion planning algorithms including trajectory generation, trajectory selection, behavior planning, and optimization-based planning approaches for autonomous trucks.
• Develop planning solutions leveraging techniques such as graph search, sampling-based planning, optimization-based planning, spline/B-spline trajectories, convex optimization, and Frenet-frame approaches.
• Incorporate vehicle kinematic and dynamic constraints into planning systems to ensure safe, feasible, and comfortable vehicle behavior.
• Develop production-quality software using modern C++ within a Linux environment while adhering to quality, safety, testing, and deployment best practices.
• Participate in software architecture discussions and contribute to technical designs that support scalable and maintainable autonomy systems.
• Develop and execute validation strategies across Software-in-the-Loop (SiL), Hardware-in-the-Loop (HiL), and Vehicle-in-the-Loop (ViL) environments.
• Collaborate closely with Safety, Controls, Perception, Validation, and Simulation teams to develop safe and reliable autonomous driving behaviors.
• Investigate and debug vehicle behavior by reproducing issues in simulation, analyzing system performance, and implementing software improvements.
• Support vehicle integration, deployment activities, and post-deployment investigations to ensure reliable autonomy performance.
• Participate in technical design reviews, code reviews, and continuous improvement initiatives across the Behaviors organization.
• Mentor junior engineers through collaboration, technical guidance, and knowledge sharing.

Senior Motion Planning Engineer – Trajectory Optimization

• This role is focused on motion planning, trajectory generation, and trajectory optimization for autonomous vehicles in unstructured environments.
• Provide technical leadership across teams and platforms, driving architectural strategy, engineering excellence, and scalable system design.
• Lead high-impact technical initiatives, influence long-term engineering direction, and contribute directly to the design, development, validation, and deployment of planning capabilities.
• Work closely with engineers and leaders across autonomy, controls, simulation, safety, and validation to help deliver safe, robust, and reliable autonomy behaviors at scale.

Staff Software Engineer – Motion Planning

• Own the offline dataset pipeline — design, implement, test, and deploy Cloud-based pipelines that convert logged multi-sensor data into VLM/VLA training datasets, spanning geometric labels (3D/2D detection, tracking, segmentation, depth) through semantic, scenario-level, and action/trajectory-grounded annotations.
• Build VLM-assisted auto-labeling — develop open-vocabulary detection, dense captioning, semantic enrichment, and scene/scenario description generation that move beyond closed-set bounding boxes, using foundation models to scale annotation and cut manual labeling cost.
• Generate reasoning-grounded labels — produce language-grounded reasoning and chain-of-causation style annotations, temporally aligned to ego-motion and trajectories, to support VLA training and explainable driving behavior.
• Mine and curate the long tail — surface rare, difficult, and high-uncertainty scenarios, and build curated datasets that measurably improve downstream VLM/VLA model metrics rather than simply adding volume.
• Close the data flywheel — define dataset schemas, quality metrics, and validation; track auto-labeling quality against model requirements; route model failures back into re-labeling and retraining loops.
• Partner with the end-to-end model team — co-define dataset specifications with VLM/VLA model developers, own the quality bar and delivery cadence, and operationalize a continuous dataset delivery loop into their training pipelines.
• Scale on cloud infrastructure — build distributed, reproducible pipelines using columnar data formats and distributed compute, with disciplined software practices, version control, and documentation.
• Lead and mentor — serve as project lead, guide less-experienced engineers, run design reviews, set coding and annotation standards, and drive alignment across team interfaces to the rest of the organization.
• Stay current — track the latest advances in multimodal models, auto-labeling, and end-to-end autonomous driving, and translate relevant research into production data systems.

Senior ML Engineer – VLM

• Develop and train machine learning models for learned behavior systems, including approaches such as behavioral cloning, imitation learning, and reinforcement learning.
• Implement production-quality ML code to support model training, evaluation, and inference within the autonomy stack.
• Analyze model performance, identify failure modes, and propose improvements to increase robustness and generalization across different scenarios.
• Contribute to model training pipelines and data workflows, organizing behavior datasets from simulation, fleet logs, and vehicle data.
• Collaborate with simulation, validation, and autonomy engineering teams to test and evaluate learned behavior models across diverse driving environments.
• Help integrate learned behavior models into simulation and test workflows to enable faster iteration and more comprehensive validation.
• Support the development of tools and infrastructure that improve experimentation speed, repeatability, and model iteration.
• Contribute to technical discussions regarding model architectures and training strategies within the team.

Machine Learning Engineer II

Engineering Manager

• Lead and grow a team building the application framework that integrates deep learning models into Torc’s autonomy stack.
• Drive technical execution across key focus areas: CUDA optimization, GPU resource management, model conversion pipelines (PyTorch, TensorRT, ONNX), and real-time system integration.
• Ensure reliability, determinism, and scalability across multi-sensor autonomous driving workloads.
• Partner with cross-functional teams (Perception, Planning, Systems, Validation, Hardware, Safety) to align technical direction and integration priorities.
• Mentor, coach, and develop engineers while cultivating a collaborative, high-trust team culture.
• Manage execution and delivery, balancing near-term goals with long-term architectural vision.
• Establish scalable processes for development, testing, and integration in a safety-critical environment.

Manager, Engineering – App Engine, CUDA

• Deliver through your team as well as collaborate across teams to provide practical and suitable quality management procedures for Torc’s autonomous driving product line.
• Support a rapid engineering development schedule for Torc’s self-driving system by completing root cause analysis activities and driving efficiency improvements back into the organization.
• Collaborate with Development teams, V&V teams and corporate quality management for planning, execution and delivery of quality assurance activities.
• Communicate and coordinate QMS processes, procedures and work instructions that cover development and testing activities for hardware, software, system and overall transportation eco-system.
• Sponsor and implement methods to inspect and evaluate engineering deliverables, with automation and data analytics to provide insight into the health of Torc’s QMS system.
• Facilitate regular audits and feedback to leadership, outlining findings and follow-up improvements.
• Facilitate QMS training materials for Torc employees.
• Identify and report on release artifacts included in the Product Development lifecycle including requirements, architecture, design, hardware, software, test and verification deliverables.
• Ensure the proper baseline identification of current and historical configuration items can be accurately determined throughout the product lifespan, perform periodic audits, and provide verification of configuration items release versioning.
• People leadership responsibilities include new hire selection, onboarding, goal setting, career pathing and professional development, employee team building and training, performance management, scheduling and assigning work, resource management and controlling expenses, and enforces systems, policies, and procedures. Proactively manages employee relations and conflict while representing Torc’s core values.
• Identify hiring needs, partner with People Operations and Talent Acquisition, and assist in the recruiting, screening, interviewing, selection, and hiring processes for talent across multidisciplinary teams. Recommends salary increases, promotions, transfers, demotions, or terminations.
• Accomplishes team objectives by supervising staff and organizing and monitoring work processes with the highest degree of quality and safety.

Engineering Manager – Engineering Quality Management

Systems Engineer

• Support release readiness activities through analysis of software performance, testing results, and system-level metrics.
• Develop, maintain, and improve Python-based tools, scripts, dashboards, and workflows used to evaluate software quality and release health.
• Analyze test results from simulation, hardware-in-the-loop, closed-course, and on-road testing environments.
• Investigate system-level issues and support root cause analysis efforts in collaboration with software, systems, validation, and autonomy engineering teams.
• Work with large datasets to identify trends, anomalies, and opportunities for improvement across the autonomy stack.
• Help define, implement, and maintain metrics that provide visibility into software quality, system performance, and release readiness.
• Collaborate with engineers across Perception, Behaviors, Platform, Validation, and Systems Engineering teams to understand system behavior and support technical investigations.
• Participate in requirements reviews, design discussions, and validation planning activities.
• Contribute to the development and improvement of release processes, triage workflows, and engineering tools.
• Document findings, communicate investigation results, and provide actionable feedback to development teams.
• Support defect tracking and issue management activities throughout the software development lifecycle.

System Engineer II – Release Readiness & Metrics, Python

• Design, develop, and maintain planning and prediction systems, including trajectory generation, behavior prediction, decision-making, and motion planning algorithms for autonomous trucks.
• Contribute to the evaluation and development of planning approaches, including optimization-based, learning-based, and hybrid architectures.
• Develop production-quality software using modern C++ within a Linux environment while adhering to quality, safety, testing, and deployment best practices.
• Participate in software architecture discussions and contribute to technical designs that support scalable and maintainable autonomy systems.
• Develop and execute validation strategies across Software-in-the-Loop (SiL), Hardware-in-the-Loop (HiL), and Vehicle-in-the-Loop (ViL) environments.
• Collaborate closely with Safety, Controls, Perception, Validation, and Simulation teams to develop safe and reliable autonomous driving behaviors.
• Investigate and debug vehicle behavior by reproducing issues in simulation, analyzing system performance, and implementing software improvements.
• Support vehicle integration, deployment activities, and post-deployment investigations to ensure reliable autonomy performance.
• Participate in technical design reviews, code reviews, and continuous improvement initiatives across the Behaviors organization.
• Mentor junior engineers through collaboration, technical guidance, and knowledge sharing.

Senior Software Engineer – Behaviors

• Responsible for software development, including but not limited to, algorithm development, software design, implementation, unit testing, vehicle testing and deployed software maintenance while following quality, build, deploy and test processes, safety and process requirements and guidelines. Includes both Autonomous and Perception Engineers.
• Responsible for executing full software development lifecycle activities using primarily C++ skills in Linux development environment using Lean-Agile methodologies.
• Responsible to complete software assignments including but not limited to software design, implementation, unit testing, vehicle testing and deployed software maintenance while following quality, build, deploy and test processes, safety and process requirements and guidelines.
• Assist in root cause analysis of issues found in testing and process automation steps.
• Support team in identifying daily assignments and reporting progress at daily stand ups.
• Support software and system level test plans and verification strategies to support ongoing feature development and bug fixes.
• Designing and implementing systems responsible for data acquisition and analysis from remote vehicles in the field.
• Responsible for ensuring software updates do not regress the software performance by utilizing simulation software and scenarios.
• Passion for autonomy technology product area
• Communicate well in a team environment being able to clearly articulate progress, design expectations and support needed to help the team accomplish goals.
• Expected to drive alignment across team’s interfaces to the rest of the organization. Designs, maintains and owns team’s technical solutions and drives consensus. Mentors and guides engineers within the group.

Senior Software Engineer – New AI Initiatives

• Lead highly complex data science and analytics projects in support of process improvement, defect reduction, and predictive analytics for manufacturing and business applications.
• Demonstrated project management skills, serving as project lead guiding less experienced team members in multiple facets of project execution.
• Independently develop data science models or algorithms using disciplined software development processes, making recommendations for developing new code or re-using existing code, implementing version control, and maintaining documentation of created applications.
• Define and implement ingestion, data preparation, curation, and governance of large, multi-faceted data sets supporting analytics models and workflows.
• Proactively assess current capabilities to identify areas for improvement proposing solutions that align with core strategy and operation.
• Guide and produce information products, supporting visualization and data accessibility in a customer centric manner.
• Evaluate and make recommendations regarding technical advances that improve productivity and quality, reduce flow times, and enhance operational surety.
• Develop guidelines and standards for analytics and machine learning models, their deployment, and associated processes.
• Provides technical guidance or business process expertise, technical leadership, coaching and mentoring to team members.
• Expected to communicate and collaborate within the team and participate with the team’s interfaces to the rest of the organization.
• Interface with other teams and design software units for the team’s products

Machine Learning Engineer – New AI Initiatives

Data Engineer

• Own the design and organization of the program’s data lake, including schema definitions, partitioning strategy and metadata indexing.
• Design and maintain end-to-end pipelines that ingest high-bandwidth sensor logs from vehicles into cloud storage with high reliability and tolerant of ad-hoc and intermittent connectivity mechanisms.
• Develop data validation and integrity checks that can detect corrupted information, missing sensors, and inconsistent calibration prior to the data being processed by downstream systems.
• Implement retention, tiering and lifecycle policies for data to balance storage costs with development value.
• Build tooling to query raw logs to produce curated training and evaluation datasets.
• Build automation to run cost-effective pseudo-labeling workflows at the scale of data ingest.
• Implement data quality and model performance metrics that are used to direct labeling effort toward the highest-value examples.
• Deploy and maintain data visualization tooling to support log review, annotation QA, and autonomy debugging workflows.
• Build integrations between the visualization tooling and the data lake so engineers can navigate from a dataset entry or model failure directly to the origin log data
• Work with autonomy engineers to define and surface custom visualization panels and implement metrics for analyzing unstructured operating environments.
• Build dashboards that provide the autonomy engineers visibility into data coverage by terrain type, operating environment and geographic region.
• Establish and document data contracts between the data services and model training consumers.
• Partner with perception, planning and embedded engineers across the data lifecycle: from shaping the logging schemas and collection triggers to defining the dataset interfaces that supply model training and evaluation.
• Define data engineering standards, best practices, and tooling choices for an innovative and fast-paced team.
• Contribute to the data roadmap and provide input to technical leadership on investment priorities.
• Mentor junior engineers and raise the team’s capabilities in data infrastructure scalability and operational hygiene.

Senior Autonomy Data Engineer

• Design, develop, and maintain core planning and prediction systems, including trajectory generation, behavior prediction, decision-making, and motion planning algorithms for autonomous trucks.
• Advance and evaluate multiple planning approaches, including optimization-based, learning-based, and hybrid architectures, making data-driven decisions on where each methodology delivers the greatest value.
• Own technical solutions across the full software lifecycle, including architecture design, implementation, testing, simulation validation, vehicle integration, deployment, and long-term maintenance.
• Develop production-quality software using modern C++ within a Linux environment while adhering to quality, safety, testing, and deployment best practices.
• Drive simulation-based validation efforts across Software-in-the-Loop (SiL), Hardware-in-the-Loop (HiL), and Vehicle-in-the-Loop (ViL) environments to ensure robust performance and prevent regressions.
• Partner closely with Safety, Controls, Perception, Validation, and Simulation teams to develop safe and reliable autonomous driving behaviors, including fallback strategies and minimum-risk maneuvers.
• Lead root-cause investigations by reproducing field issues in simulation, debugging vehicle behavior, and translating findings into actionable software improvements.
• Influence technical strategy, roadmap planning, and long-term architectural direction across the Behaviors organization.
• Act as a technical lead on complex, cross-functional initiatives, driving alignment and execution across teams and stakeholders.
• Mentor engineers, raise technical standards, and help develop the next generation of autonomy leaders within the organization.

Staff Software Engineer – Behaviors

• Implement the latest research advances in Neural Rendering and generative models
• Translate cutting edge solution in the domain of autonomous driving for high-quality Camera, LiDAR and Radar sensor simulations
• Support implementing a neural rendering framework that allows to scale perception simulation and AV 3.0 training
• Integrate the framework in a cloud environment and automate the pipeline to allow scaling for the target verification and validation of our autonomous trucks
• Own development projects in the team – From research, design, to implementation, testing and deployment
• Design, implement, test and deploy shippable production quality software starting from early prototypes using disciplined software development processes.
• Work in the cloud machine learning ecosystem alongside other machine learning services existing in the company.
• Proactively assess current capabilities to identify areas for improvement proposing solutions that align with core strategy and operation.
• Demonstrate project management skills, serving as project lead guiding less experienced team members in multiple facets of project execution, coaching and mentoring as needed.