Staff Machine Learning Engineer - ML Frameworks

About the Company

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:  

Torc's virtual driver software utilizes cutting-edge deep learning techniques to perceive the vehicle's environment, predict the movements of other vehicles, and execute accurate driving decisions. We are actively seeking a highly experienced staff machine learning engineer to join the Machine Learning Frameworks team. This is an exceptional opportunity for you to have a significant impact on the future of the autonomous vehicle industry by enhancing AI performance.

The ML Frameworks Team is hiring a Staff Machine Learning Engineer that will focus on our next generation ML training framework components for large scale, distributed model training in the cloud. The new engineer will focus on building a new distributed training architecture based on Ray and PyTorch Lightning as well as on the migration of existing, legacy implementations at Torc towards this new architecture. This new training framework utilizes heterogenous cloud resources for fast and highly resource efficient model training and will consequently be used to train large, multitask architectures for various perception and planning functions of the autonomous truck. Furthermore, the new engineer will participate in general tasks within the frameworks team, including building tooling for various parts of the ML lifecycle, the maintenance of a large, shared ML codebase and the continuous support of the internal user base.

What you'll be doing:

• Mature and optimize machine learning workflows

• Take a significant role in implementing and rolling out our new Ray-based framework for distributed, large scale machine learning training, deployment as well as data transformation pipelines

• Lead the design and implementation of Ray cluster configurations, resource management strategies, and multi-tenancy solutions to support multiple ML teams

• Maintain a large code base in which all machine learning projects at Torc are hosted

• Collaborate with researchers and engineers to maintain and improve their machine learning projects

• Engage with the data and compute interfaces of the team to ensure optimal tooling impact to product deliveries

• Stay abreast of the latest advancements in PyTorch, Ray, Daft, and Lightning; maximizing their potential for cloud execution

• Collaborate with machine learning engineers to develop innovative and performant deep learning solutions

• Analyze and optimize deep learning training using profiling and optimization tools, identifying and eliminating performance bottlenecks

• Contribute to the development of internal tools and libraries to further enhance deep learning performance on the target hardware

• Document your work clearly and concisely, sharing knowledge effectively with team members

What you need to succeed:

• Bachelor's degree in computer science, data science, artificial intelligence or related field with 6+ years of professional experience

• 3+ years of hands-on experience with Ray in production environments, including:

  • Deploying and managing Ray clusters at scale (100+ nodes)

  • Building and maintaining Ray-based ML training pipelines for multiple teams

  • Troubleshooting and optimizing Ray performance issues in distributed settings

  • Implementing resource allocation and scheduling strategies for multi-team Ray deployments

  • Creating standardized Ray workflows and best practices for team adoption

  • Providing technical leadership and support for Ray users across an organization

• Mastery of Python and PyTorch, with the ability to write efficient and maintainable code for both performance and flexibility

• In-depth knowledge of AWS or other cloud providers

• Excellent understanding of parallel computing (GPGPU) and high-performance (HPC) concepts

• Excel at working in a highly collaborative environment

  • Familiarity with AGILE development practices

  • Comfortable using collaborative development tools such as Git and Jira

  • Ability to adhere to company coding standards

• Proven dedication to writing production-quality code that is robust, efficient, portable, maintainable, and bug-free

Bonus Points!

• Experience with Ray on Kubernetes or other orchestration platforms

• Contributions to Ray open source or experience with Ray internals

• Experience migrating legacy distributed training systems to Ray

• Experience with relevant NVIDIA libraries and frameworks, such as CUBLAS, CuDNN, and NPP

• Knowledge of other Deep Learning frameworks such as TensorFlow or Caffe

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 

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.