Lead product strategy and roadmap for AI infrastructure stack, defining compiler, runtime, and framework integration features.
Responsibilities
Translate customer, engineering, and business requirements into detailed Product Requirements Documents (PRDs), technical specifications, and feature definitions.
Develop detailed product schedules, milestone plans, and release timelines while managing dependencies across multiple engineering organizations.
Define requirements for the software and AI model compilation toolchain.
Lead product partnerships and technical integrations with major machine learning frameworks and runtimes.
Define performance optimization roadmap for low-level operator libraries.
Requirements
Master of Science (M.Sc.) in Computer Science, Electrical Engineering, Mathematics, or Physics
Doctor of Philosophy (Ph.D.) in Computer Science, Electrical Engineering, Mathematics, or Physics
Nice to have: MBA
Nice to have: open-source AI infrastructure contributions
Lead product management for AI infrastructure stack, defining roadmap for compiler, runtime, and framework integration.
Responsibilities
Translate customer, engineering, and business requirements into detailed Product Requirements Documents (PRDs), technical specifications, and feature definitions.
Develop detailed product schedules, milestone plans, and release timelines while managing dependencies across multiple engineering organizations.
Define requirements for the software and AI model compilation toolchain.
Lead product partnerships and technical integrations with major machine learning frameworks and runtimes.
Define performance optimization roadmap for low-level operator libraries.
Requirements
Master of Science (M.Sc.) in Computer Science, Electrical Engineering, Mathematics, or Physics
Doctor of Philosophy (Ph.D.) in Computer Science, Electrical Engineering, Mathematics, or Physics
Nice to have: MBA
Nice to have: open-source AI infrastructure contributions
Lead research on post‑training methods for generative robot policies, enabling rapid adaptation and continual learning in real‑world robotic systems.
Compensation
The pay range for this position at commencement of employment is expected to be between $158,400 and $227,700 /year for Massachusetts-based roles.
Responsibilities
Design and implement methods for adapting generative robot policies using reinforcement learning, imitation learning, agentic tool use, and related methods.
Develop methods that enable generative robot policies to improve through ongoing interaction with people and the environment, using signals such as demonstrations, interventions, preference feedback, learned rewards, and other human‑driven signals.
Develop rollout‑based learning and exploration strategies that enable robots to efficiently and safely gather useful experience, leveraging multimodal data from demonstrations, teleoperation, interventions, and on‑policy rollouts, in both simulation and on real robot hardware.
Collaborate across teams in perception, control, foundation models, and ML infrastructure to deploy scalable and reproducible research systems.
Publish research outcomes and contribute to the open robotics and embodied AI communities.
Requirements
Ph.D. or M.S. in Robotics, Machine Learning, Computer Vision, or a related field, or equivalent applied research experience.
Lead the design, development and implementation of Responsible AI capabilities across the Commonwealth Bank Group, providing technical leadership and guiding architecture, risk controls, and delivery practices for Generative and Agentic AI systems.
Responsibilities
Provide technical leadership in the design, development and implementation of world‑class evaluation and assurance capabilities for Generative and Agentic AI
Partner with engineering, data science, risk and policy teams in designing and delivering Responsible AI capabilities
Partner with risk and policy teams in building and operating formal risk controls
Establish executive risk reporting for senior leadership
Apply statistical methods and structured problem solving to complex Responsible AI challenges
Requirements
Tertiary qualification in Data Science, Statistics, Computer Science, Engineering, Mathematics or a related discipline
Support the Data Processing & Analytics department as a Digital‑Twin Spacecraft Engineer intern, contributing to digital engineering solutions for space missions.
Compensation
Attractive salary and work-life balance with a 35-hour week (flexitime).
Responsibilities
Support use case for integrating CAD data and mechanical engineering processes into the overall space systems engineering knowledge base in Digital‑Twin platform
Understand user needs and define building blocks to build and evaluate models
Prepare knowledge base by collecting data sources and implementing pipelines
Process and integrate mechanical engineering data into artefacts from thermal and functional engineering domains
Implement dashboards, user interfaces and prompts for presentation
Requirements
Enrolled full‑time student in aerospace engineering, systems engineering, data science, artificial intelligence, computer science, software engineering or equivalent
Internship focused on AI research in recursive self‑improvement and agentic machine learning at Meta’s Monetization Ranking & AI Foundations team.
Locations (2)
Sunnyvale, California, United States
Menlo Park, California, United States
Responsibilities
Conduct state‑of‑the‑art research on recursive self‑improvement, autoresearch, and self‑improving AI agents
Design and evaluate agents that form hypotheses, use tools, run experiments, analyze outcomes, and learn from prior research trajectories
Develop methods to improve the underlying LLM through reinforcement‑learning post‑training and/or optimize the surrounding harness, including prompts, tools, memory, retrieval, and orchestration
Build reliable evaluation and reward signals for long‑running, expensive ML experiments, including safeguards against reward hacking and regressions
Study whether gains compound across multiple self‑improvement rounds and rigorously attribute improvements to model learning versus harness changes
Machine Learning Engineer to drive Persona’s large-scale ML infrastructure and facilitate the robot learning effort.
Compensation
We offer competitive compensation, a performance-based bonus, 100% employer covered medical benefits for most healthcare plans, early-stage equity, competitive PTO, and company-wide paid winter break between December 24th and January 2nd.
Responsibilities
Contribute to Persona’s large-scale ML software stack across data, training, and evaluation.
Design and implement the ML software infrastructure with thoughtful abstraction and scalability.
Work with the ML team to craft and execute a comprehensive plan for the development of machine learning models.
Develop comprehensive test suites to ensure correct and performant execution of the ML pipelines.
Leverage cloud platforms for flexible compute capacity, data management, and task automation.
Ph.D. in Machine Learning, Artificial Intelligence, Computer Science, Information or Multimedia Retrieval, Computer Vision, Natural Language Processing, Reinforcement Learning, Optimization, Computational Statistics, Applied Mathematics, or related technical fields
Research Scientist, Robot Learning - Continual AdaptationNewToyota Research Institute · Cambridge, US
Research Scientist Intern, AI Core Machine Learning (PhD)NewMeta · Sunnyvale, US+1 more
Robotics Machine Learning EngineerPersona AI Inc · Houston, US