Torc Robotics

Systems Engineer

• Collaborate with systems design and architecture teams to ensure safety considerations are integrated into autonomous trucking specifications.
• Work closely with functional safety and system engineers to ensure safety targets are achieved in autonomous vehicle development.
• Lead safety evaluations and guide engineers to meet acceptance criteria.
• Identify functional insufficiencies and triggering conditions, providing risk assessment (HIRE analysis) and safety improvements.
• Define areas for design improvement and collaborate with design teams to refine system specifications.
• Develop verification and validation targets for known and unknown safety scenarios.
• Support safety-related operational strategies, ensuring the safe deployment of autonomous vehicles.
• Communicate safety achievement metrics and risk assessments to management and key stakeholders.

Senior Systems Architect – SOTIF, FuSa

Machine Learning Engineer

• Design, develop, and deploy deep learning models for camera-based perception (e.g., object detection, segmentation, depth estimation, scene understanding)
• Own end-to-end model development for scoped areas, from data curation and training to evaluation and deployment
• Write production-quality ML code to support scalable training, evaluation, and inference pipelines
• Analyze model performance across diverse driving scenarios, identify failure modes, and improve robustness and generalization
• Contribute to and improve large-scale training pipelines, including dataset preparation, distributed training, and experiment tracking
• Partner with data teams to improve dataset quality, including labeling strategies and coverage of edge cases
• Collaborate with perception, simulation, and validation teams to evaluate and integrate models into the autonomy stack
• Improve tooling, workflows, and infrastructure to accelerate experimentation and model iteration
• Contribute to model architecture decisions and technical discussions within the team
• Mentor junior engineers on implementation, debugging, and best practices

Senior Machine Learning Engineer – Camera Model

Program Manager

• You'll partner closely with engineering leadership and cross functional stakeholders to keep priorities clear and delivery moving.
• You'll also lead and develop the TPM team, setting direction, supporting growth, and creating a strong, team first environment that drives results.
• Refines technical program management and engineering processes for the delivery of an industry leading autonomous commercial vehicle product.
• Drives TPM team’s progress towards completion of required milestones, communicating regularly to all levels within the team, engineering department, and overall organization.
• Sees the bigger picture and works through organizational silos to create alignment and partnerships between leaders.
• Responsible for learning and understanding the technical domain of their team and stakeholders.
• Enforces engineering and program management best practices for the team, improving quality, efficiency, and accuracy of the team’s work.
• Collaborates with other leaders and teams to meet program goals, support team performance, and achieve strategic objectives.
• Supports TPM management in maintaining and reporting group budgets, initiatives, and procedures.
• Participates in team, department, and company events, meetings, and activities.
• Responsible for employee engagement, relationship development, and performance feedback to understand career goals and competencies to create and implement career development and coaching plans for the team.
• Responsible for identifying organizational hiring needs and working with their managers to have the roles efficiently approved, posted, hired, and onboarded.
• Responsible ongoing compensation analysis in close collaboration with peer managers and People Operations to retain existing talent.
• Responsible for employee corrective actions and conflict resolution to ensure a healthy work environment. Models compliance to all company regulations and procedures and ensures team complies in kind

Director, Technical Program Management

Full-stack Engineer

• Contribute to the development and maintenance of AWS-based data pipelines that process vehicle telemetry and validation data
• Develop Python code and services supporting safety and performance metric computation
• Assist in analyzing truck-generated data and translating it into measurable system signals
• Support implementation of release validation logic tied to automated testing pipelines
• Collaborate with systems, validation, and autonomy teams to help define meaningful KPIs and metrics
• Investigate pipeline failures, data inconsistencies, and telemetry anomalies using logs and large-scale datasets
• Support statistical analysis and reporting across validation and testing data
• Participate in code reviews and collaborate with team members to deliver reliable, maintainable software

Software Engineer, I – Metrics for Release Platform

Technical Recruiter

• You will be responsible for helping to scale our growing organization. Responsibilities include sourcing, screening and serving as recruiting partner to various hiring teams. This role will perform all duties of a full life cycle Recruiter.
• Supporting networking and outreach of online and offline with potential candidates to promote our employer brand, reduce our time-to-hire, and build a strong talent pipeline - helping to hire for our current and future positions.
• Partnering with engineering hiring teams to set recruiting strategy and achieve annual hiring goals.
• Owning the full life cycle recruitment process from screening and selection to negotiation, offer and close, ensuring an outstanding candidate experience.
• Strategically planning and owning a pipeline of exceptionally qualified candidates and maintain their interest throughout the entire process.

Senior Technical Recruiter

Strategy

• Assist in the coordination, development, integration, and delivery of key safety programs such as driverless safety case programs, traffic safety law compliance, SPI programs
• Manage documentation, evidence collection, and internal/external reviews.
• Support external communications and regulatory engagement in collaboration with government affairs.
• Support other key safety strategy & programs delivery as needed
• Translate high-level safety objectives into actionable plans and collaborate with technology teams to define, track and deliver milestones to achieve those plans.
• Define goals, objectives, and success metrics for safety programs.
• Identify and manage dependencies, risks, and blockers across departments.
• Provide regular updates to senior leadership on program status, risks, and mitigation plans.
• Prepare and present dashboards, metrics, and business narratives.
• Represent safety programs in internal reviews and external assessments.
• Foster strong working relationships across technical teams and business operations teams.
• Algin with the Program Management Office (PMO) and Torc’s Technical Product Mangers (TPMs) to ensure consistent practices, shared learning, and clear communication, ensure removal of blockers.
• Drive continuous improvement in planning, communication, and execution processes.
• Support the Safety Review Board by coordinating meetings, tracking actions, and ensuring accountability.
• Contribute to the development of standards and processes for safety program delivery.
• Promote a proactive safety culture through education, communication, and recognition.

Safety Strategy Lead

Data Engineer

• Design and develop high‑performance data converters for multi‑sensor autonomous‑driving data (camera, lidar, radar), ensuring accurate time alignment and robust handling of raw sensor logs.
• Design, build, and optimize large‑scale ingestion and transformation pipelines (ETL/ELT) capable of processing petabyte‑scale autonomous‑driving sensor data, and automate them for reliable, production‑grade deployment.
• Work with data formats such as ROS bags, MCAP, and custom binary encodings; establish standards for schema evolution and metadata integrity.
• Implement automated data validation, quality checks, and lineage tracking to ensure reliability of production datasets.
• Collaborate closely with ML, annotation, simulation, and perception teams to ensure cross‑team ownership of data products and deliver datasets that are consistent, semantically correct, and ready for downstream consumption.
• Proactively assess current capabilities to identify areas for improvement proposing solutions that align with core strategy and operation.

Senior Software Engineer – Data Pipeline

Product Manager

• As a Manager of Technical Product Management, you will serve as a leader, manager, and coach for a team of TPMs serving one or more pillars in our Technology Organization.
• You will drive best practice in program management skills, provide coaching and development to a team of TPMs, and build a team by empowering your reports to deliver on their roadmap in their corresponding technical product areas.
• Additionally, you will be responsible for the planning and execution of your team’s portion of the technology roadmap, working cross functionally with stakeholders across the entire company, to provide your team the knowledge and tools needed for successful delivery.

Manager, Technical Product Management

Engineer

• Evaluate performance data and test results collected from on road testing, simulation testing and off-road target hardware testing
• Create value add analysis and trends as feedback to development teams
• Perform triaging and analysis of issues discovered at the system level including sensor input issues, logic issues, embedded resource issues and training data issues
• Triaging is required across different test environments including hardware-in-loop (HIL), Simulation for behavior and perception and Vehicle level closed-course and public road work
• Perform triage, tracking, and reporting of metrics for test escapes
• Provide system performance metrics based on data collected by our self-driving truck
• Combine environment observations with event logs to determine avenues for investigation
• Report on software quality using concepts such as defect ratios and software heatmap viewpoints
• Track and follow-up on high priority and blocking defects, ensuring continued progress
• Create tooling requirements for efficient triage and root cause analysis, metric reporting and visualization with the goal of quickly extract key learnings and trends related to software maturity
• Establish and maintain defect management process, defect workflow and tracking including definition of bug ticket quality and ability to understand state of defects.
• Contribute to a rapid integration and deployment schedule for self-driving software stack with consistent releases to extended testing teams.

Senior Systems Triage Engineer, ML – Embedded Systems

Software Engineer

• Deliver through your team as well as collaborate across teams to deliver high quality system releases for Torc’s autonomous driving product line.
• Maintain a rapid engineering development schedule for self-driving software stack with consistent and managed releases, both internal and external to Torc.
• Collaborate with Development teams, fleet teams, V&V teams and product management for planning, execution and delivery of releases
• Communicate and coordinate feature deliveries, bug fixes, risks, release notes and change logs for software test and release candidates
• Manage impact analysis supporting the product roadmap
• Facilitate regular release trains with Git branching, software versioning, impact analysis process, pre-release software process and daily/weekly release meetings with Torc technology representatives.
• 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.

Senior Manager, Engineering – Release Management

• Provide leadership, direction and definition for verification and validation lifecycle including strategic approaches for a comprehensive and defensible V&V test plan.
• Be a key stakeholder to create a state of the art verification and validation strategy covering multiple test levels and test environments such as component, subsystem and system testing on Hardware-in-loop (HIL), Simulation for behavior and perception and Vehicle level closed-course and public road work.
• Define and review strategies for product release and associated risks with mitigations
• Verify and review test results and tool qualification activities that support the V&V claim evidence across multiple test levels and test environments.
• Report and communicate to leadership on critical gaps on traceability, defensibility, and completeness
• Provide direction and guidance to teams to apply safety test methods such as test derivation methods, test coverage calculations, sampling strategies, edge case scenarios, FMEA or others
• Lead cross-functional collaboration with PMO (Program Management Office), Engineering, and other stakeholders within and between ARTs (Agile Release Train) and teams to ensure strategic and execution alignment
• Understand the day-to-day activities associated with Torc’s product development lifecycle, finding opportunities for efficiency improvements and improved quality.
• Provide insight and guidance for machine learned software with emphasis on training data validation and model testing as part of a larger system.

Staff Systems Engineer – Release Strategy, Validation

Security Engineer

• Perform cybersecurity assurance activities such as static analysis, dynamic analysis, and fuzz testing.
• Perform attack surface analysis at varying levels of software and hardware abstraction.
• Collaborate with product development teams to embed cybersecurity into software development activities.
• Focus on ensuring compliance with best practices and industry standard requirements pertaining to software security activities defined by the AVCDL.
• Prepare and present evidence in the safety case to demonstrate readiness to launch new products or release new software versions from a software security perspective.
• Train internal stakeholders on software security practices and act as a resource where expert software security assistance is needed.

Cybersecurity Architect, Level 1

• Develop and train computer vision and deep learning models for road-lane detection using monocular and multimodal sensor data (camera, LiDAR, radar).
• Build 3D road surface and lane geometry models in BEV space and integrate them into Torc’s autonomy pipeline.
• Analyze model performance, identify corner cases, and improve robustness under diverse environmental and long-tail conditions.
• Develop and optimize large-scale data processing workflows, including annotation, pseudo-labeling, and data augmentation.
• Implement scalable training and evaluation pipelines for lane perception models.
• Own deployment-focused work to optimize models for real-time execution on automotive-grade hardware.
• Leverage SD and HD map priors to improve lane estimation accuracy and stability.
• Contribute to architectural discussions, model reviews, and system-level integration efforts.

ML Engineer, II – Road & Lane

• Develop and train deep learning models for camera-based perception, enabling the autonomy stack to detect objects, understand scenes, and estimate geometric information from visual inputs.
• Implement production-quality machine learning code to support model training, evaluation, and inference for camera perception systems.
• Analyze model performance across diverse driving scenarios, identify failure modes, and improve robustness and generalization.
• Contribute to the development and optimization of large-scale training pipelines, including dataset preparation, distributed training, and experiment management.
• Work closely with data teams to curate and improve training datasets derived from fleet logs, simulation, and annotation pipelines.
• Collaborate with cross-functional teams across perception, simulation, and validation to evaluate model performance and support integration into the autonomy stack.
• Improve experimentation workflows and tooling to accelerate model iteration, reproducibility, and evaluation.
• Contribute to discussions on model architecture, training strategies, and perception system design.

ML Engineer, II – Camera Models

• Develop and train machine learning models for End-to-End perception and planning, including approaches such as 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 scenarios.
• Contribute to model training pipelines and data workflows, curating datasets from simulation, fleet logs, and on-vehicle data.
• Collaborate with simulation, validation, and autonomy engineering teams to test and evaluate End-to-End models across diverse driving environments.
• Help integrate End-to-End models into simulation and testing workflows, enabling faster iteration and more comprehensive validation.
• Support the development of tooling and infrastructure that improve experimentation speed, reproducibility, and model iteration.
• Contribute to technical discussions around model architecture and training strategies within the team.