Senior Applied Scientist - Graph Optimization & Trace Alignment

Role description

Responsibilities

• Design and implement scalable algorithms that align, filter, and aggregate large-scale crowd-sourced vehicle traces
• Build and optimize the lane graph itself
• Combine deterministic and learned methods deliberately
• Architect high-performance implementations of these algorithms
• Collaborate with cross-functional teams to integrate trace‑processing and lane‑graph models
• Stay current with the state of the art in trace‑based mapping
• Deploy solutions using Docker containers on cloud platforms

Requirements

• Master’s degree in computer science, robotics, applied mathematics, Engineering, or a related field
• Software Engineer with at least 3-4 years of professional experience
• Strong software engineering skills, particularly Python
• C++ experience for performance‑critical solver and geometry code is a plus
• 3-4 years of hands‑on experience with optimization‑based estimation
• Demonstrated experience processing large-scale vehicle trace or trajectory data
• SLAM and mapping knowledge
• Working knowledge of modern ML applied to geometric or graph problems
• Experience with cloud computing platforms such as Azure and Databricks
• Proficient in deploying applications using Docker containers
• Ability to think end‑to‑end and deliver high‑quality solutions

Expertise in designing and implementing scalable algorithms for processing large-scale vehicle trace data, with strong proficiency in Python and experience in deploying applications using Docker on cloud platforms. Knowledge of SLAM, mapping, and modern machine learning techniques applied to geometric or graph problems is essential.