Lead ML Platform Software Engineer

Meet the team:

The Glydways perception team is responsible for designing and implementing a perception system that includes data fusion from state-of-the-art sensors (e.g., LIDAR, RADAR, High definition cameras, Ultra-wide-band radios, etc.) and robust detection, classification, and tracking of any and all obstacles that could present a hazard to the vehicle (Glydcars) in the system. The perception system for Glydways will reason about information from each Glydcar and from regularly spaced sensor pods monitoring the road network.  

The perception team plays a vital role within the Autonomy Software engineering team.  The team’s deliverables include: 

1. Documenting a detailed design for enacting a safe system that can be certified by public transportation authorities.
2. Implementing said design on mature prototype vehicles in demonstrations to customers, potential customers, and investors.
3. Expanding the design to include fail-operational capabilities that extend the overall system to safely give our customers a more comfortable experience.
4. Managing data collection, autonomy testing, and milestone demonstration events that showcase the system’s maturing capabilities, leading to a production system.  

Team members may work remotely and must be self-motivated. 

Roles & Responsibilities:

• Define and own the technical vision and roadmap for ML platforms and perception workflows, aligning cross-functional stakeholders across engineering, product, and operations
• Lead and mentor a team of internal and external engineers in building, maintaining, and scaling ML infrastructure and tooling
• Drive architectural decisions for data extraction, labeling pipelines, and automation frameworks, ensuring scalability, reliability, and maintainability
• Design and own metrics pipelines and monitoring dashboards to track production performance of perception systems, setting standards for observability across the team
• Establish and enforce best practices for reproducibility, traceability, and observability of all Perception workflows across the engineering organization
• Own end-to-end infrastructure for training, validating, and deploying ML models, including third-party labeling integrations
• Serve as the primary technical point of contact for cross-team collaborations, translating ambiguous requirements into executable engineering plans
• Partner with engineering leadership to define hiring needs, conduct technical interviews, and grow team capabilities

Required Skills and Abilities:

• Strong proficiency in Python and/or C++, with a track record of production-grade delivery
• Proven experience leading cross-functional engineering teams in a fast-paced, ambiguous environment
• Deep expertise in cloud ML workflow frameworks (e.g. Ray, Sagemaker, Valohai) as well as relevant Kubernetes frameworks (e.g. Argo, Kubeflow) and ML platform architecture
• Demonstrated ability to define technical strategy and drive execution from vague requirements to shipped product
• Experience mentoring and developing engineers at varying levels
• Strong communication skills with the ability to influence without authority across teams and organizational levels
• Customer-focused problem-solving with a bias toward pragmatic, scalable solutions

Desirable Skills:

• 5+ years of experience in Modern C++ design and development
• Prior tech lead or staff engineer experience in robotics or autonomous driving platforms
• Hands-on experience implementing end-to-end test, metrics, and analytics workflows for AD datasets
• ML model deployment in production-grade C++ software stacks
• Strong command of containerization (Docker), orchestration (Kubernetes), and cloud platforms (AWS)
• Experience building 3D visualization tools for LiDAR point cloud data
• Familiarity with large multimodal dataset curation and management at scale