At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.
A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. Now a part of the Daimler family, we are focused solely on developing software for automated trucks to transform how the world moves freight.
Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.
Meet the Team:
At the forefront of autonomous driving, we have gathered vast amounts of valuable sensor data that provide a deep insight into various driving environments and scenarios. Within the AI Foundation Department our strategic response is to integrate foundational machine learning techniques to harness its full potential and drive its capabilities further ahead to adhere to future needs.
We are searching for a Senior Machine Learning Engineer with Reinforcement Learning or strong control background deploying our next-generation modular and differentiable AV stack, bridging the gap between traditional model-based modularity and modern deep learning practices.
What you'll do:
Design and deployment of our innovative modular and differentiable AV stack, focusing on prediction, planning, and control
Collaborate closely with our research and development teams, ensuring the seamless incorporation of model-based planning and control algorithms with advancements in differentiable optimization
Drive the continuous enhancement of our AV architectures, championing the harmony between modularity and end-to-end neural networks
Spearhead the evaluation process of our AV stack, ensuring marked improvements in both open-loop and closed-loop planning metrics
Advocate for and ensure the interpretability, reusability, and efficiency of our AV solutions, while maximizing the capabilities of deep learning and differentiable optimization
What you'll need to Succeed:
MS, or PhD in Machine Learning, Computer Science, Robotics, or related fields with at least the years working experience or equivalent
Comprehensive understanding of modular AV system components combined with proficiency in end-to-end neural network designs
Hands-on experience with differentiable optimization and its applications in deep learning models
Demonstrable history of deploying neural network architectures for practical applications, with a preference for those within the autonomous vehicles domain
Expertise in Python and familiarity with major deep learning frameworks like PyTorch or TensorFlow
Bonus Points!
Scholarly publications related to differentiable optimization, neural network architectures, or autonomous vehicle systems in high tier conferences as CVPR, ICCV, ICRA, IROS, NeurIPS or comparable
Prior experience in developing or collaborating on AV stacks, especially in prediction, planning, and control sectors
Knowledge of contemporary strategies for testing and deploying deep learning models in real-world settings
Perks of Being a Full-time Torc'r
Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers:
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
Hiring Range for Job Opening
US Pay Range
$177,300-$212,800 USD
At Torc, we're committed to building a diverse and inclusive workplace. We celebrate the uniqueness of our Torc'rs 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.
