Engineering Manager, Active Sensors – LiDAR

This is a fully remote position, open to applicants in Michigan, +2 more states.

📋 Description

• Lead, mentor, and enhance the capabilities of a team consisting of machine learning and software engineers, focusing on hiring, performance management, career development, and ongoing feedback.

• Establish the technical direction, roadmap, and priorities for active-sensor perception.

• Take ownership of the development and delivery of multitask models for object detection, road and lane detection, and free-space estimation utilizing lidar and radar data.

• Direct architectural and design decisions regarding shared backbones, task-specific representations, sensor fusion, temporal modeling, uncertainty estimation, and interactions between perception tasks.

• Ensure that improvements do not lead to unacceptable regressions in other tasks or the behavior of downstream systems.

• Formulate strategies to maintain consistent perception performance in adverse weather, varying conditions, sensor degradation, and sensor failures.

• Propel designs that support graceful degradation when sensor inputs are absent, impaired, delayed, or unreliable.

• Oversee the entire machine learning lifecycle, from data requirements and model development to experimentation, evaluation, integration, release, and monitoring.

• Guarantee that training and evaluation datasets are of sufficient quality and coverage across various operating conditions, geographic features, rare events, adverse weather, and sensor-failure scenarios.

• Establish task-level and system-level metrics, benchmarks, and failure-analysis methodologies.

• Review technical designs, model architectures, experimental results, training artifacts, and verification documentation.

• Collaborate with teams focused on multimodal perception, prediction and planning, data, infrastructure, simulation, sensor hardware, embedded platforms, systems engineering, and safety.

• Monitor execution and communicate progress, risks, dependencies, and staffing requirements to senior leadership.

• Uphold engineering standards through design reviews, code and model assessments, reproducible experiments, and criteria for release readiness.

⛳️ Requirements

• Bachelor's degree in Computer Science, Robotics, Electrical Engineering, or a related field with over 6 years of professional experience, or a master's degree with more than 4 years of experience.

• At least 2 years of experience in leading and managing engineers, which includes coaching, performance management, and career progression.

• A robust technical foundation in machine learning and computer vision, covering 3D geometry, model evaluation, uncertainty, and modes of perception failure.

• Proven experience in developing and deploying production-ready machine learning systems for autonomous driving, robotics, or other real-world applications.

• Familiarity with multitask learning, object detection, road and lane detection, or 3D occupancy estimation.

• In-depth understanding of lidar sensing, including scan patterns, reflectance, FMCW lidar, and sensor time synchronization.

• Comprehensive experience across the machine learning lifecycle, which includes data curation, model training, controlled experimentation, offline evaluation, system integration, and production validation.

• Proficiency in analyzing data distributions, dataset coverage, long-tail scenarios, and the correlation between training data and model performance.

• Strong skills in Python and PyTorch, along with practical experience in using C++ within production perception or machine learning systems.

• Experience in deploying and optimizing deep learning models with TensorRT.

• A solid understanding of embedded computing platforms and the constraints of real-time perception systems.

• Ability to define technical roadmaps, plan complex machine learning projects, manage cross-functional dependencies, and deliver on program milestones.

• Excellent written and verbal communication skills, capable of articulating technical decisions, results, trade-offs, and risks to both technical teams and senior leadership.

• Bonus: PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related discipline.

• Bonus: Experience with NVIDIA libraries and frameworks such as CUDA, CuDNN, CuBLAS, NPP, and custom TensorRT operations.

• Bonus: Contributions in the form of publications, patents, or open-source projects in machine learning, computer vision, robotics, or autonomous driving.

🏝️ Benefits

• A competitive compensation package that includes a bonus component and stock options.

• 100% coverage of medical, dental, and vision premiums for full-time employees.

• 401K plan featuring a 6% employer match.

• Flexible scheduling options.

• Generous paid vacation available immediately after the start date.

• Company-wide holiday office closures.

• AD+D and Life Insurance coverage.

• Potential sign-on bonuses, relocation assistance, and other forms of compensation may be included in the overall compensation package.