Lead ML Platform Software Engineer

Strong Python or C++ skills

Cloud workflow frameworks for ML (e.g. Ray, Argo, Kubeflow)

Building ML platforms and pipelines

Leadership of cross functional teams and collaborations

Experience with large multimodal datasets and their curation.

Self starter - experience developing a solution end-to-end starting from vague requirements in a cross team collaborative environment

Leading efforts by internal and external resources to build, maintain, and improve ML platforms and workflows.

Introducing and customizing automation into data extraction and labeling pipelines.

Design and improve metrics pipelines and create monitoring dashboards to track performance of perception systems in production.

Build and maintain infrastructure and ETL pipelines for training, validating, and deploying ML models.

Build and maintain infrastructure for interfacing with third party labeling companies.

Ensure reproducibility, traceability, and observability of Perception workflows.

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:

Documenting a detailed design for enacting a safe system that can be certified by public transportation authorities.

Implementing said design on mature prototype vehicles in demonstrations to customers, potential customers, and investors.

Expanding the design to include fail-operational capabilities that extend the overall system to safely give our customers a more comfortable experience.

Managing data collection, autonomy testing, and milestone demonstration events that showcase the system’s maturing capabilities, leading to a production system.