Engineering Manager, Active Sensors – LiDAR

Torc Robotics

. Lead, coach, and develop a team of machine learning and software engineers, including hiring, performance management, career development, and continuous feedback .

Posted 10/8/2026full-timeRemote • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading machine learning and software engineering teams, with a strong focus on developing multitask models for perception tasks using lidar and radar data. Proficient in managing the machine learning lifecycle, ensuring high-quality data and model performance across various conditions.

Highest-signal resume keywords

Machine Learning Lifecycle ManagementMultitask Learning and Object DetectionLidar Sensing and Sensor FusionPython and PyTorch ProficiencyTeam Leadership and Development

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningComputer Vision3D GeometryModel EvaluationUncertainty EstimationObject DetectionRoad and Lane DetectionData CurationDeep Learning OptimizationEmbedded Computing

Soft Skills

CoachingPerformance ManagementCommunication

Tools & Technologies

PyTorchTensorRTC++NVIDIA LibrariesCUDACuDNNCuBLASNPP

Certifications & Qualifications

Bachelor's Degree in Computer ScienceMaster's Degree in RoboticsPhD in Related Field

Industry Keywords

Autonomous DrivingRoboticsSensor DegradationPerception Failure ModesReal-Time Perception Systems

Tech Stack

Tools & technologies

PythonPyTorchC++

About the role

Key responsibilities & impact

Lead, coach, and develop a team of machine learning and software engineers, including hiring, performance management, career development, and continuous feedback
Set technical direction, roadmap, and priorities for active-sensor perception
Own development and delivery of multitask models for object detection, road and lane detection, and free-space estimation using lidar and radar data
Guide architecture and design decisions involving shared backbones, task-specific representations, sensor fusion, temporal modeling, uncertainty estimation, and perception-task interactions
Ensure improvements do not introduce unacceptable regressions in other tasks or downstream system behavior
Define strategies for consistent perception performance across adverse weather, changing conditions, sensor degradation, and sensor failures
Drive designs supporting graceful degradation when sensor inputs are missing, degraded, delayed, or unreliable
Own delivery across the machine learning lifecycle, from data requirements and model development through experimentation, evaluation, integration, release, and monitoring
Ensure training and evaluation datasets provide sufficient quality and coverage across operating conditions, geographic features, rare events, adverse weather, and sensor-failure modes
Establish task-level and system-level metrics, benchmarks, and failure-analysis practices
Review technical designs, model architectures, experimental results, training artifacts, and verification evidence
Collaborate with multimodal perception, prediction and planning, data, infrastructure, simulation, sensor hardware, embedded platforms, systems engineering, and safety teams
Track execution and communicate progress, risks, dependencies, and staffing needs to senior leadership
Maintain engineering standards through design reviews, code and model reviews, reproducible experimentation, and release-readiness criteria

Requirements

What you’ll need

Bachelor's degree in Computer Science, Robotics, Electrical Engineering, or related field with 6+ years of professional experience, or a master's degree with 4+ years of experience
2+ years of experience leading and managing engineers, including coaching, performance management, and career development
Strong technical foundation in machine learning and computer vision, including 3D geometry, model evaluation, uncertainty, and perception failure modes
Experience developing and deploying production machine learning systems for autonomous driving, robotics, or another real-world application
Experience with multitask learning, object detection, road and lane detection, or 3D occupancy estimation
Strong understanding of lidar sensing, including scan patterns, reflectance, FMCW lidar, and sensor time synchronization
Experience across the machine learning lifecycle, including data curation, model training, controlled experimentation, offline evaluation, system integration, and production validation
Experience analyzing data distributions, dataset coverage, long-tail scenarios, and the relationship between training data and model performance
Strong proficiency in Python and PyTorch, with practical experience using C++ in production perception or machine learning systems
Experience deploying and optimizing deep learning models using TensorRT
Strong understanding of embedded computing platforms and real-time perception system constraints
Experience defining technical roadmaps, planning complex machine learning projects, managing cross-functional dependencies, and delivering against program milestones
Strong written and verbal communication skills, with the ability to explain technical decisions, results, tradeoffs, and risks to technical teams and senior leadership
Bonus: PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or related field
Bonus: Experience with NVIDIA libraries and frameworks such as CUDA, CuDNN, CuBLAS, NPP, and custom TensorRT operations
Bonus: Publications, patents, or open-source contributions in machine learning, computer vision, robotics, or autonomous driving

Benefits

Comp & perks

A competitive compensation package that includes a bonus component and stock options
100% paid medical, dental, and vision premiums for full-time employees
401K plan with a 6% employer match
Flexibility in schedule
Generous paid vacation available immediately after start date
Company-wide holiday office closures
AD+D and Life Insurance
Potential sign-on payments, relocation, and other forms of compensation may be provided as part of the total compensation package