The Discrete Planning Group is a part of the Autonomy Team for Hivemind Enterprise Product, focused on researching and developing state‑of‑the‑art algorithms that drive intelligent flight behaviors while accounting for an uncertain and dynamic world.
Research, design, and implement state‑of‑the‑art algorithms for optimal task allocation, scheduling and temporal sequencing of heterogeneous teams of autonomous vehicles
Solidify and improve existing C++ based mission planning software applicable across disparate vehicle types and compute platforms
Work with engineers, program managers, and product managers to define a technical roadmap for future autonomy solutions and SDK offerings
Breakdown a mission into assignable tasks based on agent capabilities such as navigating in contested and denied environments and adapting to mission changes in real‑time
Construct feasible and optimal action plans for distributed teams of autonomous vehicles that also minimize human operator workload
Requirements
PhD in Optimization or Operations Research
Master’s degree with 4 or more years of work in the same areas
Seeking a senior software development engineer to develop key features for the autonomous driving platform, focusing on Trajectory planning and Motion Planning.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5.
Responsibilities
Lead the entire feature lifecycle, driving motion planning solutions from initial design through to full productization and deployment.
Model L2, L3, and L4 vehicle behaviors and driving modes by formulating them as optimal-control problems.
Integrate perception, prediction data, and user-comfort preferences through deep collaboration with prediction and behavior planning teams.
Enhance driving behavior and support overall product quality by engaging in continuous issue triage alongside product and user-experience engineers.
Improve optimization solver performance using instruction parallelization and memory optimization techniques.
Requirements
Computer Science
Computer Engineering
Nice to have: Autonomous Driving industry
Nice to have: constrained optimization-based trajectory generation
Develop and refine optimization models for short-term battery dispatch and bidding in multiple wholesale power markets
Locations (4)
New York, New York, United States
Houston, Texas, United States
San Francisco, California, United States
Boston, Massachusetts, United States
Compensation
Pay: Competitive salary ($150,000-185,000) plus equity
Responsibilities
Develop and refine optimization models for short-term battery dispatch and bidding in multiple wholesale power markets
Monitor market prices, operating conditions, and rule changes to identify optimization opportunities and translate market observations into improvements to bidding and dispatch strategies
Software Engineer, App Ads Bidding and Optimization
Develop and optimize advertising technology for Google Ads, focusing on bidding algorithms, machine learning models, and data analysis to improve campaign performance.
Improve the accuracy and stability of the UAC bidder with statistical analysis and new ML/optimization algorithms
Improve/modernize bidding infrastructure and design and implement bidding algorithms for faster cold start for new campaigns
Debug inaccuracies in bidding infrastructure and suggest improvements and launch with design, implementation, experimentation and analysis
Incorporate and engineer input features from ad creatives, publisher pages/content, as well as users’ personalized ad history to improve bidding and modeling accuracy
Improve the efficiency and reliability of ML models by designing novel model architectures and adopting robust development and release processes
Requirements
Bachelor's degree in Computer Science or related technical field
Work with data scientists and optimization engineers to identify performance improvement opportunities and refine bidding and algorithmic strategies to enhance revenue and environmental performance
Enhance methods for handling decision-making under uncertainty and integrate probabilistic market forecasts into optimization workflows
Explore and apply developments in optimization methods that can deliver practical improvements to short-term bidding and dispatch
Requirements
Bachelor’s degree in a related quantitative field (e.g., computer science, operations research, engineering, mathematics, physics, power systems, etc.) or equivalent practical experience
Nice to have: grid-scale battery projects in multiple US power markets
Nice to have: advanced degree (Master’s or PhD) in a related quantitative field
Nice to have: speed performance tradeoffs in optimization
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Software Engineer, App Ads Bidding and OptimizationGoogle · Mountain View, US