Senior, Machine Learning Engineer - End-to-End

Torc Robotics; Ann Arbor (MI); Hybrid; USD 226,400 - 271,700; Full time

Benefits offered by this job

100% paid medical, dental, and vision premiums

401K with 6% employer match

Generous paid vacation

Job summary

Torc Robotics in Ann Arbor, MI is seeking a Senior Machine Learning Engineer to develop and scale learning-based systems that connect multi-modal perception inputs to driving behavior, enabling safe, efficient, and human-like autonomy. Responsibilities include developing End-to-End ML models, training using large datasets, and collaborating with cross-functional teams. Successful candidates will have significant experience in ML, strong programming skills in Python and PyTorch, and a relevant degree. A hybrid work model is available.

Qualifications

• 6+ years of experience in Machine Learning, Robotics, or related field.
• Experience developing and deploying ML models.
• Strong programming skills with ability to write production-quality ML code.

Responsibilities

• Own development and delivery of End-to-End ML models.
• Train and evaluate models using large-scale datasets.
• Collaborate with various teams to ensure alignment across the autonomy stack.

Skills

Machine Learning

Python

PyTorch

Robotics

Data analysis

Education

Bachelor’s degree in Machine Learning or related field

Master’s degree or PhD in a related field

Tools

Distributed training frameworks (e.g., Ray)

Job description

Position Overview

As a Senior Machine Learning Engineer – End-to-End (E2E), you will develop and scale learning-based systems that connect multi-modal perception inputs to driving behavior, enabling safe, efficient, and human-like autonomy for real-world freight operations.

What You’ll Do

• Own development and delivery of End-to-End ML models that map multi-modal sensor inputs (camera, LiDAR, radar, maps) to driving-relevant outputs (trajectories, cost functions, or intermediate representations)
• Train and evaluate models using large-scale datasets from fleet logs, simulation, and synthetic data
• Analyze model performance, identify failure modes, and drive data‑driven improvements in robustness and generalization
• Design and refine training pipelines, data workflows, and evaluation strategies to improve iteration speed and model quality
• Contribute to model architecture decisions, including approaches such as imitation learning, reinforcement learning, transformers, and vision-language-action (VLA) models
• Collaborate closely with Perception, Prediction, Planning, and Simulation teams to ensure alignment across the autonomy stack
• Support integration of E2E models into simulation and on‑vehicle systems for closed‑loop validation
• Improve tooling, experimentation workflows, and reproducibility across the team
• Mentor junior engineers and contribute to team‑level best practices and technical discussions

What You’ll Need To Succeed

• Bachelor’s degree with 6+ years, Master’s with 4+ years, or PhD with 0–2 years of experience in Machine Learning, Robotics, Computer Science, or a related field with a track record of publications in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, CoRL)
• Experience developing and deploying ML models for autonomous systems, robotics, or complex decision‑making environments
• Strong programming skills in Python and PyTorch, with ability to write production-quality ML code
• Experience training and evaluating models using large-scale datasets and distributed compute environments
• Solid understanding of ML architectures used in E2E systems, such as Transformers, BEV models, VLA/VLM approaches, or diffusion models
• Proven ability to debug model behavior, analyze performance metrics, and drive iterative improvements
• Experience contributing to or influencing model architecture and training strategies
• Ability to work cross-functionally and integrate ML systems into larger autonomy pipelines

Bonus Points

• Experience developing End-to-End or mid-to-end models for autonomous driving or robotics
• Experience with vision-language models (VLMs) or vision-language-action (VLA) systems
• Familiarity with closed-loop simulation and evaluation frameworks
• Experience with reinforcement learning or imitation learning in real-world systems
• Experience with distributed training frameworks (e.g., Ray)
• Understanding of vehicle dynamics, motion planning, or multi-agent systems

Work Location

We are open to hiring in Ann Arbor, MI (U.S.) office in a hybrid capacity. We also open to hiring Remote in the United States.

Benefits

• 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 and generous paid vacation (available immediately after start date)
• Company-wide holiday office closures
• AD+D and Life Insurance

Our compensation reflects the cost of labor across several geographic markets. Pay is based on a number of factors and may vary depending on job-related knowledge, skills, and experience. Torc's total compensation package will also include our corporate bonus and stock option plan. Dependent on the position offered, sign-on payments, relocation, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and other benefits.

At Torc, we’re committed to building a diverse and inclusive workplace. We celebrate the uniqueness of our Torc’r and do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, veteran status, or disabilities.

Even if you don’t meet 100% of the qualifications listed for this opportunity, we encourage you to apply.

Job ID: 102665

Hiring Range: $226,400—$271,700 USD