RESPONSIBILITIES
- Contribute to ML model R&D by prototyping, validating and iterating on existing and new model architectures towards an online mapping system.
- Own development of new ML models end-to-end from data strategy, initial development, optimization, production platform validation, and fine tuning based on metrics and on road performance.
- Lead multi-person projects and influence the overall AD/ADAS online mapping architecture and technical direction.
- Enable and help other engineers on the team to be more effective through coaching and leading by example when it comes to writing high-quality code, providing high-quality code and design document reviews and delivering rigorous reports from ML experiments.
- Work in a high-velocity environment and employ agile development practices.Team player and “get things done” mentality
- Collaborate closely with stakeholders in downstream customer teams to define interfaces and requirements for the Perception stack.
- Closely collaborate with Perception subteams to develop end-to-end solutions.
MINIMUM QUALIFICATIONS
- MS, or higher degree, in CS/CE/EE, or equivalent industry experience.
- 3+ years of experience with ML frameworks such as PyTorch, Caffee, Tensorflow
- 3+ years of experience in machine learning workflows: data sampling and curation, pre-processing, model training, ablation studies, evaluation, deployment, inference optimization.
- Extensive experience with machine learning and classification problems.
- Strong programming skills in Python or C++.
- Passionate about applying ML methodology to advance self driving technology.
- Strong communication skills and ability to communicate concepts clearly and precisely.
- Experience in production-level software development best practices.
NICE TO HAVES
- Experience developing vision centric online mapping solutions.
- Experience with commercially available automotive maps.
- Hands-on experience with building a perception stack for autonomous systems.
- Experience in runtime optimization for runtime-critical systems on Linux and UNIX-like real-time operating systems.
- Experience with CUDA or OpenCL.
- Experience with automotive grade compute platforms.
- Experience with building safety-critical software architectures.
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