Principal Software Engineer, Team Lead - Perception Platforms

Mission Summary:

On our Perception team, you have the opportunity to work with world-class ML engineers and research scientists to make self-driving vehicles a reality. The Perception Platform team is central to this mission, operating at the intersection of ML Infrastructure and Perception. We build the foundational frameworks, evaluation pipelines, and tooling that enable our Perception team to scale their work on the tech stack responsible for perceiving the dynamic world around our robo-taxi.

These foundational systems include our large-scale data pipelines,  which are critical for efficient end-to-end ML development (e.g. data transformation, model training, and model evaluation) of our on-car and off-car Perception solutions. It also includes our evaluation and visualization tools, which provide intuitive insights into dataset distributions, model behavior, system performance, and edge-case scenarios. Finally, our active learning loop which continuously selects and incorporates high-value data to improve model accuracy and robustness over time.

What you’ll be doing: 

• Build, lead and manage a high-performing team in the creation, maintenance, and optimization of our internal data, training, and evaluation pipelines specific to perception modelling team.
• Provide technical guidance, mentorship, and career development opportunities to team members
• Define team goals, priorities, and roadmap in alignment with company objectives
• Work closely with ML engineers to proactively identify and resolve technical challenges , while also contributing directly to the implementation of those improvements. This includes troubleshooting and optimizing machine learning workflows for maximum efficiency and performance, architecting and executing cutting-edge technologies/methodologies for accelerating ML development end-to-end from concept to deployment & maintenance.
• Lead design and code reviews, setting a high bar for quality and ensuring the adoption of best practices.
• Foster cross-functional collaboration ensuring alignment with broader organizational goals.
  
  

What We’re Looking for:

• 7+ years of professional software engineering experience, with at least 3+ years in a formal or informal leadership or mentorship role.
• 2+ years of experience developing, operating, or supporting distributed machine learning training across clusters of GPU nodes.
• BS or MS in Computer Science or a related technical field.
• A passion for remaining hands-on and a proven ability to contribute high-quality, production-level code to a shared codebase.
• Fluency in Python and familiarity with data processing libraries (e.g., NumPy, Pandas)
• Proven experience in backend development and leading the architecture of complex systems.
• A deep understanding of ML or Perception topics to strategically guide pipelines/tooling.
• Demonstrated experience in designing and delivering data visualization tools or internal engineering dashboards that drive insights and action.
• Expert knowledge of software engineering principles, with a track record of leading teams in software design, source control management, build processes, code reviews, and testing methods.
• Excellent communication and interpersonal skills, with the ability to articulate a technical vision and motivate a team
  
  

Bonus Points:

• Expertise in concurrency, parallel processing, and database systems to inform architectural decisions.
• Experience with distributed computing frameworks like K8s and Ray.
• A proven track record of developing and deploying Perception systems for autonomous vehicles or robotics.
• A Ph.D. in a relevant field, or equivalent industry experience that demonstrates a commitment to continuous learning.
• Proficiency in modern C++ (C++14/17 or later)
• Experience with building products with current LLMs and MCP protocols.
• Experience with GPU-accelerated data processing.
• Familiarity with Pytorch, TensorFlow or other modern ML development platform
• Experience in project management and a history of successfully delivering complex technical projects.

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