About the job
Google's Geo team is embarking on a multi-year journey into the future and needs ML/AI engineers to help us navigate to that tomorrow. As a Staff Software Engineer, you will help design and build products using sensor data (e.g., imagery, vehicle telemetry, location data, authoritative data, etc.), large ML models and more to create new structured geospatial data and transformative user experiences. You will work across organizations including Geo's Basemap, Infrastructure, Consumer and Automotive teams along with Cloud and Research product areas to enable Google's future map making technologies. You will step into a technical leadership role, working with junior engineers and generalist colleagues to deliver these products. Come help us build the future of maps.
Responsibilities
Lead the design, development, and deployment of machine learning models to detect road disruptions and maintain road signs from various signals (e.g., traffic data, vehicle telemetry, vehicle sensors, imagery etc.).
Develop and improve ML algorithms for inferring road closures and speed limits, fusing data from multiple sensors and sources like vehicle sensor observations (VSO), location data, Waze, and third-party data.
Build and maintain scalable data pipelines for training, evaluation, and inference using Google's infrastructure.
Collaborate closely with other engineers, product managers, data analysts, and operations teams to define requirements, integrate ML solutions, and drive impact on map data quality.
Provide technical leadership and mentorship to junior engineers on the team.
Qualifications
Minimum
Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
Experience with common machine learning techniques and technologies.
Experience building or interacting with Generative AI or Applied AI applications.
Preferred
Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
One or more research publications in CV/ML conferences (e.g., CVPR, ICCV, ECCV, NIPS, or ICLR).
5 years of experience with one or more of the following: computer vision, reinforcement learning (e.g. sequential decision making), ML infrastructure, or specialization in another ML field.
Experience working in large-sized engineering and cross-functional teams.
Expertise in modeling with TensorFlow, JAX, etc.
Excellent people management and communication skills.