Lead Machine Learning Research Scientist

About NuroNuro is pioneering self-driving technology with a vision to make autonomy universally accessible. Established in 2016, we are committed to developing the most scalable driving solution, integrating advanced AI with automotive-grade hardware. Our flagship technology, the Nuro Driver™, is licensed to facilitate various applications, including robotaxis, commercial fleets, and personal vehicles. With proven technology from numerous self-driving deployments, Nuro provides automakers and mobility platforms a clear pathway to achieving commercial-scale autonomous vehicles, fostering a safer, more connected future.Role OverviewAs a Lead Machine Learning Research Scientist, you will collaborate with our autonomy teams to address complex perception, prediction, and planning challenges using innovative machine learning techniques. This includes working with foundation models, multi-modal large language models, generative models, out-of-distribution detection, imitation learning, and reinforcement learning. If you thrive on tackling challenging problems and translating solutions into real-world applications, we invite you to join our team!Key ResponsibilitiesResearch and implement cutting-edge machine learning techniques.Address state-of-the-art perception, prediction, and planning issues within our autonomy system.Develop and optimize efficient ML data pipelines.Provide technical leadership and mentorship to a diverse team.Deploy autonomous software solutions on public roads.

About UsNuro is a pioneering self-driving technology firm committed to making autonomy accessible to everyone. Established in 2016, we are developing the world's most scalable autonomous driver by integrating advanced artificial intelligence with automotive-grade hardware. Nuro licenses its proprietary technology, the Nuro Driver™, for diverse applications including robotaxis, commercial fleets, and personal vehicles. With years of proven technology through self-driving deployments, we provide automakers and mobility platforms with a clear pathway to commercial-scale autonomous vehicles, fostering a safer, more interconnected future.Role OverviewAs a Staff ML Research Scientist, you will collaborate with our autonomy teams to tackle perception, prediction, and planning challenges using innovative machine learning techniques. Your expertise in foundation models, multi-modal LLMs, generative models, out-of-distribution detection, imitation learning, and reinforcement learning will be invaluable. If you are passionate about solving complex problems and eager to see your solutions implemented in real-world scenarios, we invite you to join our team!Key ResponsibilitiesConduct research and apply cutting-edge machine learning methodsAddress state-of-the-art challenges in perception, prediction, and planningDevelop and optimize efficient ML data pipelinesLead and mentor a diverse team of professionalsProduce and deploy autonomous software for public deployment

Who We AreNuro is at the forefront of self-driving technology, dedicated to making autonomy accessible for everyone. Founded in 2016, we are developing the most scalable autonomous driving system, merging advanced AI with automotive-grade hardware. Our flagship technology, the Nuro Driver™, is licensed for various applications, including robotaxis, commercial fleets, and personal vehicles. With years of successful deployments, Nuro provides automakers and mobility platforms a clear pathway to commercial-scale autonomous vehicles, paving the way for a safer and more connected future.About the RoleThe Prediction team is tasked with leveraging cutting-edge machine learning techniques to enhance the functionality of the Nuro Driver. As a pivotal member of the Prediction and Smart Agents team, your role will center on developing state-of-the-art models that predict the behavior of surrounding traffic, which are integral to our autonomous systems. These models will be utilized both on-board and off-board in realistic closed-loop simulations.In this position, you will delve into innovative machine learning approaches to address complex challenges in autonomous driving. Your work will involve employing generative sequence modeling techniques to accurately predict intricate, interactive traffic scenarios. This necessitates a deep understanding of the intentions of other road users and their impact on safe driving decisions. You will also explore diverse input modalities, including End-to-End (E2E) strategies, to forecast the behavior of other agents. A crucial aspect of this role is the creation of intelligent, controllable agents to facilitate effective closed-loop training within simulations.If you are driven by the challenge of solving complex problems, leading impactful research, and witnessing your contributions in real-world applications, we invite you to apply!About the WorkDesign and develop scalable, machine learning-based prediction systems to generate multi-modal, realistic, and kinematically feasible trajectories.Engage in pioneering research focused on generative sequence modeling and sequential decision-making, exploring areas such as:Scalable generative sequence modeling techniques.Modeling marginal, conditional, and joint distributions for interactive agents.Transformer-based encoder-decoder frameworks.Large generative models and diffusion techniques.Agent controllability through conditioning, guidance, and various methodologies.Collaborate closely with the Planning team to create realistic and controllable agents that enhance our simulation capabilities.

Who We Are Nuro is a pioneering self-driving technology company dedicated to making autonomy accessible to everyone. Established in 2016, we are developing the most scalable autonomous driving solution, merging advanced AI with high-performance automotive hardware. Our flagship technology, the Nuro Driver™, is licensed for various applications including robotaxis, commercial fleets, and personal vehicles. With years of proven self-driving deployments, Nuro provides automakers and mobility platforms with a clear trajectory towards commercial-scale AVs, fostering a safer, more connected future. About the Role Join our learned behavior team where we leverage sophisticated machine learning strategies to expedite software advancements. This role involves close collaboration with software teams to identify challenges and implement innovative machine learning approaches to tackle real-world problems. Your work may include self-supervised learning for robust representation, out-of-distribution detection for addressing long-tail issues, refining reinforcement learning methods for motion planning, trajectory prediction, and enhancing model robustness to address uncertainties. If you are passionate about solving complex problems with practical solutions ready for vehicle deployment, we invite you to be a part of our journey! About the Work Develop scalable machine learning systems for planning and predicting safe, feasible trajectories for autonomous vehicles. Investigate generative sequence modeling and sequential decision-making; backgrounds in Embodied AI for robotics, Causal reasoning, Model interpretability, and Joint prediction and planning are advantageous. Address uncertainties arising from interconnected autonomous systems. Collaborate across autonomy teams to create comprehensive solutions for major autonomy challenges by understanding issues, proposing ideas, prioritizing tasks, and developing effective solutions. Implement practical solutions and deploy them on real-world vehicles. About You You possess extensive expertise and experience in one or more of the following areas: M.Sc. or Ph.D. in Computer Science, Artificial Intelligence, Mathematics, or a related discipline. Strong background in sequential decision-making, Imitation Learning, Deep Reinforcement Learning, generative modeling, and more.

Who We Are Nuro is at the forefront of self-driving technology, dedicated to making autonomous driving accessible to everyone. Established in 2016, we are engineering the world’s most scalable driving solution by merging advanced AI with automotive-grade hardware. Our proprietary technology, the Nuro Driver™, is licensed to support a variety of applications, from robotaxis and commercial fleets to personal vehicles. With years of successful self-driving deployments, Nuro provides automakers and mobility platforms a clear pathway to achieving commercial-scale autonomous vehicles, fostering a safer, more connected future. About the Work Design and enhance state-of-the-art generative models, particularly focusing on diffusion architectures, flow-matching techniques, and energy-based models for autonomous planning. Develop generative models utilizing foundation models. Harness large language models and world foundation models for reasoning, decision-making, and multi-modal generation. Optimize generative models through reinforcement learning to enhance interactive reasoning. Investigate reward modeling and learned verifiers using generative models. Explore joint prediction and planning as well as self-play, and leverage generative models for active learning and world modeling. Create controllable generative models to direct the generation process towards specific goals, conditions, and rewards. Collaborate with autonomy teams to propose and implement holistic solutions to pressing autonomy challenges. Assess issues, suggest solutions, prioritize tasks, and evaluate your findings by deploying models on the Nuro Driver.

About Us Nuro is a pioneering self-driving technology company striving to make autonomous vehicles accessible for everyone. Since our founding in 2016, we have been dedicated to creating the world’s most scalable driver, integrating advanced artificial intelligence with automotive-grade hardware. Our flagship technology, the Nuro Driver™, is licensed to support various applications, including robotaxis, commercial fleets, and personally owned vehicles. With a track record of successful self-driving deployments, Nuro is paving the way for automakers and mobility platforms to realize the commercial potential of autonomous vehicles, fostering a safer, more connected future. Role Overview As a Senior Machine Learning Research Scientist focusing on Vision-Language-Action (VLA) models, you will enhance our onboard Behavior & Planning stack to promote safe and robust decision-making in complex driving scenarios. Your expertise will drive the development of multimodal models that integrate scene understanding, contextual reasoning, and planning-relevant representations for real-world autonomous driving. This position emphasizes advancing cutting-edge VLA models, including model development, large-scale training, fine-tuning, evaluation, and optimization for onboard deployment. You will collaborate closely with teams across behavior, planning, perception, systems, and infrastructure to translate research breakthroughs into practical applications deployed in our vehicles. If you are passionate about creating and implementing state-of-the-art VLA systems in robotics, we encourage you to apply. Key Responsibilities Develop and enhance VLA models for onboard Behavior & Planning in autonomous driving systems. Create multimodal models that facilitate safe decision-making in complex and ambiguous driving situations. Research and implement state-of-the-art techniques in vision-language-action modeling, multimodal representation learning, and foundational models for autonomy. Train, fine-tune, and evaluate large-scale VLAs utilizing diverse and real-world driving datasets to improve model quality and robustness. Optimize models for efficient onboard deployment, focusing on inference speed, memory usage, and runtime performance. Collaborate with various teams to define training, evaluation, and deployment requirements. Design effective evaluation methodologies for multimodal models in safety-critical scenarios. Contribute to scalable model and data pipelines that support rapid experimentation and deployment. Qualifications Proven expertise in machine learning, particularly in vision-language-action frameworks. Experience with multimodal model development and evaluation. Strong background in autonomous driving systems and decision-making processes. Familiarity with large-scale data training and optimization techniques. Excellent collaboration skills to work effectively with cross-functional teams.

About Nuro Nuro is at the forefront of self-driving technology, dedicated to making autonomy accessible to everyone. Established in 2016, we are developing the world’s most scalable autonomous driver, integrating state-of-the-art AI with high-quality automotive hardware. Our proprietary technology, the Nuro Driver™, is licensed for a diverse array of applications, including robotaxis, commercial fleets, and personal vehicles. Proven through extensive self-driving deployments, Nuro provides automakers and mobility platforms a clear pathway to commercial-scale autonomous vehicles, paving the way for a safer, more connected future. Role Overview As a Senior Machine Learning Research Scientist in our Perception & Behavior team, you will harness cutting-edge machine learning research to tackle complex real-world robotics challenges. Your primary focus will be on advancing the ML landscape and large-scale learning applications for autonomous vehicles, steering towards a holistic end-to-end driving system. This role involves developing and refining large-scale models for perception and behavior, staying abreast of the latest architectural innovations, collaborating with cross-functional teams to assess data and infrastructure needs, and enhancing model optimization and inference speeds. Your expertise in applied research will be critical in the creation and deployment of ML models within our autonomous vehicles. If you're passionate about solving fundamental AI challenges and seeing your solutions come to life in real-world robotics, we invite you to join our team! Key Responsibilities Develop and enhance state-of-the-art end-to-end ML models for autonomous vehicles, processing raw sensor data (camera, LiDAR, radar) to inform vehicle behavior. Rapidly prototype and iterate on solutions using extensive real-world driving datasets and robust computational resources. Investigate and implement innovative solutions to tackle some of the most pressing issues in AV technology, including handling rare events, ensuring safety, and providing comfortable user experiences. Collaborate closely with autonomy teams to identify critical challenges and devise comprehensive solutions through learned approaches. Partner with infrastructure, data, and evaluation teams to create efficient data handling, training, assessment, and validation processes. Deliver practical, high-performance solutions that can be deployed to the NuroDriver in live environments.

Waymo is at the forefront of autonomous driving technology, dedicated to becoming the world's most trusted driver. Originating from the Google Self-Driving Car Project in 2009, our focus has been on developing the Waymo Driver—The World’s Most Experienced Driver™—to enhance mobility access and save lives currently lost in traffic incidents. With over ten million rider-only trips facilitated by our technology, and more than 100 million miles driven autonomously on public roads, we are transforming transportation across 15+ U.S. states.The Predictive Planning team (PrePlan) is responsible for designing and implementing advanced machine learning solutions that forecast future world states and plan the Waymo Driver’s actions. Our goal is to harness Waymo’s extensive driving data to create robust, adaptable, and efficient deep neural networks that empower our autonomous vehicles to navigate complex environments safely.In this hybrid position, you will collaborate closely with our PrePlan team and be matched with your team after completing onsite interviews.Your Responsibilities Will Include:Developing cutting-edge ML-driven prediction and planning systems to boost the capabilities of our ML driver and facilitate the rapid growth of Waymo’s services.Converting open-ended, real-life challenges into well-defined ML problems, researching and applying state-of-the-art ML techniques, such as foundation models and reinforcement learning, to enhance autonomous vehicle planning and prediction.Collaborating with top-tier researchers, engineers, and product managers to ensure safe and smooth planning behaviors for all road users, aligning with product specifications.Creating and assessing large-scale models, integrating them into Waymo’s production planning software for real-world applications in close cooperation with the Planner and Research teams.

About UsNuro is pioneering self-driving technology with a mission to make autonomy accessible for everyone. Established in 2016, we are developing the world's most scalable driver, integrating advanced AI with automotive-grade hardware. Our flagship technology, the Nuro Driver™, is licensed to facilitate diverse applications, ranging from robotaxis to commercial fleets and personal vehicles. With years of proven deployment in self-driving environments, Nuro offers automakers and mobility platforms a viable pathway to commercial-scale autonomous vehicles, creating a safer, more connected future.Role OverviewAs a Senior/Staff Machine Learning Research Scientist, you will work closely with multidisciplinary teams, focusing on generative modeling to solve complex planning challenges in autonomous driving. You will utilize cutting-edge techniques—including diffusion models, flow matching, and energy-based models—to create innovative solutions that enable safe and efficient driving behavior in real-world scenarios. Additionally, you will manage the complete lifecycle of your models, transitioning them into robust applications for global autonomous driving deployments.Key ResponsibilitiesDesign and enhance state-of-the-art generative models, particularly focusing on diffusion architectures, flow-matching methods, and energy-based models, aimed at autonomous plan generation.Integrate large language models and world foundation models to facilitate reasoning, decision-making, and multi-modal generation.Employ reinforcement learning to optimize generative models for interactive reasoning, and investigate reward modeling and self-play methodologies.Create controllable generative models that steer the generation process towards specific goals and conditions.Collaborate with various autonomy teams to develop comprehensive solutions for key challenges in autonomous technology, ensuring rigorous evaluation through deployment on the Nuro Driver.

OverviewAt Google DeepMind, we believe that Artificial Intelligence is one of the most transformative inventions for humanity. Our team comprises scientists, engineers, and machine learning specialists dedicated to advancing cutting-edge AI technologies for the benefit of society and scientific progress, with a strong emphasis on safety and ethical considerations.The Gemini Safety team is responsible for ensuring the safety and fairness of our latest Gemini models. We are seeking a Research Scientist who will leverage data and innovative algorithms to enhance the performance of our user-oriented models. This role demands a fast-paced, highly collaborative work environment where support and teamwork are paramount.About UsGoogle DeepMind is at the forefront of AI innovation, focusing on using our technologies for meaningful public impact and exploring scientific frontiers. We work closely with various partners to tackle significant challenges while prioritizing ethical standards and safety protocols.Position OverviewWe are in search of a dynamic Research Scientist who excels in both exploring novel research inquiries and implementing technical solutions. Our team's mission is to enhance the safety and fairness of state-of-the-art AI models, contributing foundational technology to multiple product areas including Gemini App, Cloud API, and Search.Key Responsibilities:Optimize post-training and instruction tuning of state-of-the-art LLMs across various modalities including text-to-text and image/video/audio-to-text.Investigate data-driven, reasoning, and algorithmic strategies to ensure Gemini Models remain safe, highly effective, and accessible to all users.Enhance Gemini’s resilience against adversarial threats, particularly concerning high-risk abuse scenarios.Develop and uphold high-quality evaluation protocols to identify model behavior gaps and opportunities related to safety and fairness.Create and implement experimental plans to bridge identified gaps or develop new capabilities.Foster innovation and deepen our understanding of Supervised Fine Tuning and Reinforcement Learning fine-tuning at scale.

Waymo is at the forefront of autonomous driving technology, committed to becoming the world's most trusted driver. Originating from the Google Self-Driving Car Project in 2009, we have dedicated ourselves to developing the Waymo Driver—The World’s Most Experienced Driver™. Our mission is to enhance mobility access while significantly reducing traffic-related fatalities. With over ten million rider-only trips powered by the Waymo Driver, we have accumulated extensive experience, having driven more than 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.The Waymo AI Foundations team is dedicated to creating machine learning solutions that tackle complex challenges in autonomous driving. Our goal is to ensure the safe operation of Waymo vehicles in various cities and under diverse driving conditions. We actively collaborate with other research teams within Alphabet, focusing on areas such as reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation.This position follows a hybrid work schedule and reports directly to a Staff Research Scientist / Tech Lead Manager.

Waymo is a pioneering company in autonomous driving technology, dedicated to becoming the world's most trusted driver. Originating from the Google Self-Driving Car Project in 2009, Waymo has been on a mission to develop the Waymo Driver—The World’s Most Experienced Driver™—which aims to enhance mobility access and prevent the thousands of fatalities caused by traffic accidents. The Waymo Driver is the backbone of our fully autonomous ride-hail service and is adaptable to various vehicle platforms and applications. With more than ten million rider-only trips completed, our technology has successfully navigated over 100 million miles on public roads and simulated tens of billions of miles across more than 15 U.S. states.The Perception team is responsible for creating the systems that learn the spatial-temporal representation and semantic meanings of the environment surrounding the autonomous driving vehicle (ADV). Our work involves close collaboration with downstream teams to optimize and integrate these systems into the Waymo Driver. We actively conduct research to solve real-world challenges and partner with research teams at Alphabet. With access to millions of driving data miles collected from a variety of sensors, our engineers have the opportunity to (1) devise methods for continuous learning from large-scale real-world data, (2) build and train models at scale, (3) analyze real-world behavior and develop systems to manage complex interactions with the environment, and (4) refine models for our onboard and offboard hardware.In this hybrid role, you will report to a Technical Lead Manager.

Waymo, the leader in autonomous driving technology, aims to be the world's most trusted driver. Originating from the Google Self-Driving Car Project in 2009, Waymo is dedicated to developing the Waymo Driver—The World's Most Experienced Driver™—to enhance mobility access and significantly reduce traffic-related fatalities. With over ten million rider-only trips facilitated by our technology, which has autonomously navigated more than 100 million miles on public roads and countless simulations across more than 15 U.S. states, we are at the forefront of autonomous transportation.The Waymo Applied Research team is devoted to crafting machine learning solutions that tackle key challenges in autonomous driving, focusing on safely operating Waymo vehicles across diverse cities and under varying driving conditions. We thrive on collaboration, fostering partnerships with other research teams within Alphabet to push the boundaries of innovation.In this hybrid role, you will report to a Technical Lead Manager.

Waymo is at the forefront of autonomous driving technology, dedicated to becoming the world's most trusted driver. Originating from the Google Self-Driving Car Project in 2009, our mission centers on the Waymo Driver—The World’s Most Experienced Driver™—aimed at enhancing mobility access while significantly reducing traffic-related fatalities. The Waymo Driver currently underpins our fully autonomous ride-hailing service and is versatile enough to be integrated into various vehicle platforms and applications. With over ten million rider-only trips completed, our technology has autonomously navigated more than 100 million miles on public roads and executed tens of billions of miles in simulation across over 15 states in the U.S.The Waymo AI Foundations team is committed to advancing machine learning solutions that address the challenges of autonomous driving, enabling the safe operation of Waymo vehicles in numerous cities and under diverse driving conditions. We actively pursue collaborations with other research teams within Alphabet. Our current focus areas encompass reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation.In this hybrid role, you will report directly to a Principal Scientist.

Waymo is at the forefront of autonomous driving technology, striving to be the world's most trusted driver. Originating as the Google Self-Driving Car Project in 2009, Waymo has dedicated itself to developing the Waymo Driver—The World’s Most Experienced Driver™—to enhance mobility access and save countless lives that are lost to traffic accidents. The Waymo Driver powers our fully autonomous ride-hailing service and can be integrated into various vehicle platforms and applications. With over ten million rider-only trips completed and extensive experience gained from autonomously driving over 100 million miles on public roads and tens of billions of miles in simulation across more than 15 states, we are leading the way in transforming transportation.The Waymo Applied Research team is committed to pioneering machine learning solutions that tackle significant challenges in autonomous driving, with the ultimate aim of safely operating Waymo vehicles across numerous cities and diverse driving conditions. Our team actively initiates and nurtures collaborations with other research teams within Alphabet to drive innovation.In this hybrid role, you will report directly to a Technical Lead Manager.

Join Waymo as a Tech Lead Manager for Machine Learning Optimization, where you will spearhead innovative projects to enhance our self-driving technology. In this role, you will lead a team of talented engineers and data scientists, guiding the development of advanced algorithms and optimization techniques that drive performance and reliability. You will collaborate with cross-functional teams to ensure the successful integration of ML models into our systems, pushing the boundaries of autonomous vehicle technology.

Join Waymo as an Applied Research Scientist focusing on Perception LLM/VLM technologies. In this role, you will contribute to cutting-edge research and the development of advanced algorithms that enhance our self-driving technology.

About Bespoke LabsBespoke Labs is a leading applied AI research lab at the forefront of data and reinforcement learning (RL) environment curation for training and evaluating intelligent agents. We have recently curated Open Thoughts, recognized as one of the premier open reasoning datasets utilized by numerous advanced research labs. Our innovations include the development of state-of-the-art models such as Bespoke-MiniChart-7B and Bespoke-MiniCheck, alongside training agents to effectively perform multi-turn tool calling through reinforcement learning.Bespoke Labs is uniquely positioned to capture a significant market share in the realm of data and RL environment curation.About The RoleWe are seeking an RL Environment Research Engineer to propel our efforts in crafting, assessing, and benchmarking training environments for AI agents. You will devise systematic methodologies for environment design, pinpoint agent failures, and transform those insights into high-quality training data and benchmarks.This position marries research intuition with practical execution. A deep understanding of agent behavior is essential—identifying reward hacking, analyzing failure modes, and discerning why certain environments yield superior training outcomes. You will convert this understanding into repeatable processes and benchmark suites that can be externally showcased.You thrive on both analytical tasks (reviewing agent rollouts, uncovering patterns in failures) and creative endeavors (designing environments, establishing evaluation pipelines). You can oscillate between exploring the science behind effective environments and actually producing them at scale.What You'll DoDevelop systematic strategies and frameworks for creating high-quality RL environments that proficiently train and evaluate agents.Investigate how large language models (LLMs) and agents fail across various task types, identifying patterns that inform superior environment design.Create benchmark environments that assess specific agent capabilities, preparing them for external release on our evaluation platform.Validate environment quality through hands-on testing—training small-scale agents, checking for reward hacking, and scrutinizing training dynamics.

About EarnInEarnIn is a trailblazer in the field of earned wage access, dedicated to creating innovative solutions that provide real-time financial flexibility for individuals navigating the challenges of living paycheck to paycheck. Our community empowers members to access their earnings as they earn them, offering options to spend, save, and invest without mandatory fees, interest charges, or credit inquiries.With a highly experienced leadership team and backing from esteemed partners such as A16Z, Matrix Partners, DST, and Ribbit Capital, EarnIn is in a strong position with a thriving core business and significant growth potential. We are rapidly expanding and eager to onboard world-class talent to help us shape the future of our organization.POSITION SUMMARYWe are looking for a forward-thinking and experienced Director of Machine Learning to spearhead our machine learning initiatives across the organization. As a fintech leader, machine learning is central to our business strategy and enhances user experience. We rely on advanced, scalable ML systems to make impactful decisions and provide outstanding customer value. Our goal is to revolutionize success stories through the application of generative AI and cutting-edge machine learning algorithms, resulting in significant business and societal outcomes.The Director of ML will prioritize operational excellence by crafting and executing a strategy that transitions ML models from research to production, ensuring optimal performance, reliability, and maintainability. The ideal candidate will have a demonstrated history of deploying ML models at scale, particularly in dynamic startup settings. A strong coding background, familiarity with production-level ML engineering, and the ability to connect theoretical frameworks with practical execution are essential.The base salary range for this full-time role in Mountain View is $414,000 to $506,000, accompanied by equity and benefits. Salary ranges are determined by role, level, and location. This is a hybrid position, requiring in-office work two days a week.

Waymo is at the forefront of autonomous driving technology, dedicated to becoming the world's most trusted driver. Originating from the Google Self-Driving Car Project in 2009, Waymo has developed the Waymo Driver—The World’s Most Experienced Driver™—to enhance mobility access while significantly reducing traffic-related fatalities. The Waymo Driver powers our fully autonomous ride-hail service and is adaptable for various vehicle platforms and applications. With over ten million successful rider-only trips and more than 100 million miles driven autonomously on public roads, complemented by tens of billions of miles in simulation across 15+ U.S. states, we are transforming the future of transportation.The Perception team is pivotal in developing the system that learns spatial-temporal representations and their semantic meanings in the environment surrounding our autonomous driving vehicles (ADVs). We collaborate closely with downstream teams for optimization and integration into the Waymo Driver and conduct our own research to tackle real-world challenges. Access to extensive driving data from a variety of sensors enables our engineers to (1) create methods for continuous learning from large-scale real-world data, (2) develop scalable models and model training strategies, (3) analyze real-world behaviors and create systems to handle complex real-world interactions, and (4) optimize models for both onboard and offboard hardware.In this hybrid role, you will report to a Technical Lead Manager.