Senior, ML Engineer - Tracking

torcrobotics; Stati Uniti; Remoto; USD 177.000 - 213.000; Tempo pieno

Vantaggi offerti da questo lavoro

Stock options

Bonus

Medical, dental, and vision premiums 内

Descrizione del lavoro

torcrobotics is seeking a senior engineering leader for its autonomous trucking tracking team to build ML models and state estimation for real-time tracking of road actors. The role spans research to production with sensor fusion across cameras, LiDAR, radar and other vehicle sensors.

You will lead design and deployment of multi-object tracking, sensor fusion, and perception software using PyTorch, C++, and Python, mentoring through reviews and code quality.

Competenze

• 6+ years in industry, or 3+ with a Master's, or 1+ with a PhD.
• Experience with autonomous vehicle perception systems (tracking, detection, SLAM).
• Strong ML deployment experience for perception or sensor fusion.
• Proficiency with PyTorch and ML tooling for training/inference.

Mansioni

• Design, develop, and deploy production ML models for multi-object tracking and sensor fusion.
• Build scalable training/evaluation workflows using PyTorch and large datasets.
• Improve tracking and sensor fusion algorithms for pose and kinematics.
• Analyse large-scale vehicle data to characterize performance and failure modes.
• Develop production software in C++ and Python across lifecycle.
• Define evaluation/verification strategies for tracking quality and safety.
• Collaborate with perception, mapping, planning, controls, and platform teams.

Conoscenze

PyTorch

Python

C++

Robotics basics

Sensor fusion

3D geometry

Probabilistic estimation

ML deployment

Large multimodal data

Formazione

Bachelor's in CS/SE/Robotics or related field

Master's degree

PhD with 1+ year

Strumenti

Ray

Kubernetes

Distributed training infra

Descrizione del lavoro

Role overview

A senior engineering role on the tracking team for an autonomous trucking programme, building machine learning models and state estimation algorithms that track other road actors in real time. The work spans research through production deployment and covers sensor fusion across cameras, LiDAR, radar, and other vehicle sensors. It is a hands-on, technically leadership-oriented position contributing directly to safe and efficient autonomous vehicle behaviour.

Responsibilities

• Design, develop, and deploy production ML models for multi-object tracking, association, learned pose, kinematic estimation, and sensor fusion
• Build scalable training and evaluation workflows using PyTorch, distributed training infrastructure, and large real-world datasets
• Improve tracking and sensor fusion algorithms for robust vehicle pose and kinematics
• Analyse large-scale vehicle data to characterise performance and identify failure modes
• Develop production software in modern C++ and Python across the full lifecycle
• Define evaluation, verification, and validation strategies for tracking quality and safety
• Collaborate with perception, mapping, planning, controls, and platform teams on integrated driving capabilities
• Provide technical leadership through design reviews, code reviews, and mentoring

Requirements

• Bachelor's in Computer Science, Software Engineering, Robotics, or related field with 6+ years of relevant industry experience; or Master's with 3+ years; or PhD with 1+ year
• Experience with autonomous vehicle or robotics perception systems (tracking, detection, SLAM, scene modelling)
• Strong experience developing and deploying ML models for perception, localisation, or sensor fusion
• Proficiency with PyTorch and modern ML tooling for training, inference, and optimisation
• Solid understanding of 3D geometry, probabilistic estimation, coordinate transforms, and robotics fundamentals
• Experience working with large multimodal datasets and building scalable processing pipelines
• Strong software engineering fundamentals in Python and C++, including algorithms, data structures, testing, debugging, and performance optimisation

Nice to have

• State estimation techniques such as factor graphs, Kalman filtering, and nonlinear optimisation
• Distributed computing tools such as Ray or Kubernetes
• Embedded and real-time constraints for on-vehicle deployment
• Simulation, synthetic data generation, and uncertainty-aware ML
• Contributions to open-source robotics, perception, or ML frameworks
• Familiarity with ISO 26262 and functional safety processes

Benefits and work setup

• US hiring range of $177,300 to $212,800 USD
• Competitive compensation package including bonus and stock options
• 100% paid medical, dental, and vision premiums for full-time employees
• 401(k) plan with a 6% employer match
• Flexible schedule and generous paid vacation available from the start date
• AD+D and life insurance