Senior Machine Learning Engineer - Search Relevance

What You'll Be DoingJoin Moveworks as a Senior Machine Learning Engineer focused on developing advanced ML infrastructure for deploying and managing Large Language Models (LLMs). This pivotal role involves the design, optimization, and scaling of comprehensive machine learning systems that drive our AI solutions. The ML Infrastructure team is responsible for a range of critical functions, including distributed training and inference pipelines, model evaluation, and latency optimization for LLMs. Our work supports numerous ML and NLP models currently in production, impacting millions of enterprise users. You will tackle real-world challenges related to service scalability and algorithm optimization.Your collaboration with our machine learning team, data infrastructure team, and various cross-functional experts will directly enhance our customers' AI experience. This is an opportunity to play a vital role in the long-term scalability of our core AI product and the success of the company. If you seek a high-impact, fast-paced environment to elevate your career, we want to hear from you!Design, develop, and enhance scalable machine learning infrastructure for training, evaluating, and deploying LLMs.Create abstractions to automate diverse steps in various ML workflows.Collaborate with interdisciplinary teams of engineers, data analysts, machine learning specialists, and product managers to introduce innovative features.Utilize your expertise to promote best practices in machine learning and data engineering.

Waymo is at the forefront of autonomous driving technology, striving to become the world's most trusted driver. Originating from the Google Self-Driving Car Project in 2009, Waymo has been dedicated to developing the Waymo Driver—The World’s Most Experienced Driver™—with the goal of enhancing mobility access while aiming to reduce the tragic loss of lives due to traffic accidents. The Waymo Driver not only fuels our fully autonomous ride-hailing service but is also adaptable across various vehicle platforms and applications. With over ten million trips solely for riders and extensive experience from autonomously navigating over 100 million miles on public roads, alongside tens of billions of miles in simulation across more than 15 U.S. states, we are leading the charge in this transformative technology.The Large Model Evaluation team plays a pivotal role in advancing Waymo's AI vision. As we integrate cutting-edge Large Language Models (LLMs) and Vision-Language Models (VLMs), our aim is to construct sophisticated AI systems capable of addressing the multifaceted challenges of real-world driving. Central to our achievements is the ability to accurately measure our progress. In a landscape where robust evaluation serves as a critical barrier to deploying large models, the intricacies of this task at Waymo are particularly complex and safety-sensitive. We seek quantitatively driven engineers to innovate and propose novel methodologies for evaluating the ML models utilized in the Waymo Driver.

Waymo is at the forefront of autonomous driving technology, striving to become the world’s most reliable driver. Originating from 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 prevent countless lives lost to traffic accidents. Our cutting-edge Waymo Driver is the backbone of our fully autonomous ride-hailing service and can be implemented across various vehicle platforms and applications. Having completed over ten million rider-only trips, our technology has accumulated experience from autonomously driving over 100 million miles on public roads and tens of billions of miles in simulations across more than 15 U.S. states.The Large Model Evaluation team is pivotal to Waymo's AI mission. As advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs) continue, we are constructing state-of-the-art AI systems capable of addressing the full spectrum of real-world driving complexities. Central to our success is our capacity to measure progress effectively. Robust evaluation is crucial for deploying any large model, and at Waymo, the challenge is particularly intricate and safety-critical. We seek quantitatively-minded engineers to innovate and propose new methods for assessing the machine learning models integrated into the Waymo Driver.

Waymo is at the forefront of autonomous driving technology, striving to become the world's most trusted driver. Originating from the Google Self-Driving Car Project in 2009, Waymo is dedicated to advancing the Waymo Driver—The World's Most Experienced Driver™—to enhance mobility and save lives that are often lost to traffic accidents. The Waymo Driver not only powers our fully autonomous ride-hail service but is also adaptable across various vehicle platforms and use cases. With over ten million rider-only trips completed, our technology has driven autonomously for more than 100 million miles on public roads, supported by tens of billions of miles in simulation across 15+ U.S. states.Our Compute Team plays a pivotal role in delivering the compute platform that runs the software stack of fully autonomous vehicles. We specialize in designing high-performance custom silicon, developing cutting-edge system-level compute architectures that maximize performance, power efficiency, and minimize latency. Our multidisciplinary team thrives on collaboration, working closely with various engineering teams to ensure both hardware and software are optimized for peak performance. We are looking for passionate and talented individuals to join us in developing one of the highest-performing automotive compute platforms in the world.In this hybrid role, you will report to a Hardware Engineering Manager.

Waymo is at the forefront of autonomous driving technology, dedicated to becoming the world's most trusted driver. Established in 2009 as the Google Self-Driving Car Project, Waymo has developed the Waymo Driver—The World’s Most Experienced Driver™—aimed at enhancing mobility access and saving countless lives from traffic-related accidents. Our technology empowers a fully autonomous ride-hail service and is adaptable across various vehicle platforms and product applications. With over ten million rider-only trips facilitated by the Waymo Driver and more than 100 million miles driven autonomously on public roads, we are paving the way for safer transportation.The Perception team is responsible for developing systems that interpret the spatial-temporal representations and semantic meanings of the environment surrounding our autonomous vehicles. Our collaborative efforts with downstream teams focus on optimizing and integrating these systems within the Waymo Driver. We engage in innovative research to tackle real-world challenges and work closely with research teams at Alphabet. Our engineers have access to extensive driving data from diverse sensors, allowing us to (1) create efficient learning methods from vast real-world datasets, (2) build and train models at scale, (3) analyze real-world behaviors, and (4) optimize models for both onboard and offboard hardware.In this hybrid role, you will report to a Technical Lead Manager.

Waymo, an innovator in autonomous driving technology, is dedicated to becoming the world's most reliable driver. Originating as the Google Self-Driving Car Project in 2009, our focus has been on creating the Waymo Driver—The World’s Most Experienced Driver™—to enhance mobility access and prevent traffic-related fatalities. The Waymo Driver supports our fully autonomous ride-hail service and can be integrated into various vehicle platforms for diverse applications. With over ten million rides provided and experience from driving more than 100 million miles on public roads combined with billions of miles in simulation across 15+ U.S. states, we’re at the forefront of a transportation revolution.The Perception team is responsible for developing systems that learn spatial-temporal representations and their semantic meanings within the autonomous vehicle's environment. Collaborating closely with other teams for optimization and integration into the Waymo Driver, we engage in research to tackle practical challenges and work with Alphabet's research teams. With access to millions of miles of diverse driving data, our engineers can (1) create methods for efficient and continuous learning from large-scale real-world data, (2) build and train models at scale, (3) analyze real-world behavior to manage complex interactions, and (4) optimize models for our onboard and offboard hardware.In this hybrid role, you will report directly to a Technical Lead Manager.

Waymo is a pioneering force in autonomous driving technology, dedicated to becoming the world's most trusted driver. Originating from the Google Self-Driving Car Project in 2009, Waymo focuses on developing the Waymo Driver—The World's Most Experienced Driver™—to enhance mobility access and significantly reduce traffic-related fatalities. The Waymo Driver powers our fully autonomous ride-hailing service and is adaptable across various vehicle platforms and applications. With over ten million rider-only trips completed and extensive experience from driving more than 100 million miles on public roads, complemented by simulations totaling tens of billions of miles across over 15 U.S. states, we are at the forefront of our industry.The Large Model Evaluation team plays a crucial role in advancing Waymo's AI ambition. As we leverage advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs), our goal is to create cutting-edge AI systems that navigate the complexities of real-world driving scenarios. Our progress hinges on our ability to measure performance effectively. With robust evaluation being essential for deploying large models, the challenges we face are particularly intricate and safety-critical. We are seeking engineers with a quantitative mindset to explore and establish innovative methods for assessing the machine learning models utilized in the Waymo Driver.

The Role Join Moveworks as a Senior Machine Learning Engineer II, where you will play a pivotal role in enhancing our Natural Language Understanding (NLU) and Agentic AI capabilities. Your expertise in software engineering and machine learning will help create seamless user experiences and optimize our generative and conversational AI platform. As part of the NLU team, you will leverage cutting-edge tools in Natural Language Processing (NLP) and Natural Language Generation (NLG), including advanced Large Language Models (LLMs), multimodal foundation models, and hybrid vector databases. You'll have all the necessary infrastructure at your fingertips to fine-tune, evaluate, and deploy your models into production. Our data-centric approach is supported by a world-class annotation team that ensures the creation of high-quality, privacy-conscious datasets for model training and evaluation. Your mission will extend beyond mere model training; you will strive for exceptional AI performance across all metrics, including accuracy, latency, reliability, and the overall functionality of user-facing systems. Our ideal candidates are driven not only by the desire to develop high-performing models but also by the ambition to design and enhance comprehensive AI systems and their components. We are focused on accelerating our operations, tackling complex engineering challenges, and maximizing the value we deliver to our customers. Your contributions will be crucial in achieving our objectives of resolving enterprise issues and establishing the most dependable AI co-pilot ever seen, in collaboration with various teams at Moveworks.

Waymo is pioneering the future of autonomous driving technology with a vision to become the world's most trusted driver. Originating from the Google Self-Driving Car Project in 2009, Waymo has dedicated itself to creating the Waymo Driver—The World’s Most Experienced Driver™—which aims to enhance mobility access and save countless lives lost in traffic accidents. Powering Waymo’s fully autonomous ride-hailing platform, the Waymo Driver is adaptable across various vehicle types and applications. With over ten million rider-only trips achieved and a remarkable track record of driving more than 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states, Waymo is at the forefront of transforming transportation.The Driver Understanding and Evaluation team at Waymo is crucial in developing an in-depth understanding of the Waymo Driver's behavior. With an impressive capacity of over one million driverless miles each week, it is essential for Waymo to comprehend and evaluate all vehicle behaviors—both in real-world scenarios and simulations—through automated algorithms. The learned metrics team is a strategic initiative employing machine learning to scale and meet Waymo's ambitious goals. We work collaboratively across various teams to transition machine learning into production systems and establish Waymo’s reward function. Our focus is on building and maintaining large-scale machine learning and data systems, simulation workflows, and analytical tools. By merging expert human insights with cutting-edge machine learning models, we provide training and evaluation data for the Waymo driver. We seek enthusiastic researchers and software engineers who are passionate about creating robust production-grade machine learning systems for our autonomous vehicles and possess an unwavering commitment to enhancing our technology stack's performance.

Waymo is at the forefront of autonomous driving technology with a vision to become the world's most reliable 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™—which aims to enhance mobility access while significantly reducing traffic-related fatalities. The Waymo Driver is the backbone of our fully autonomous ride-hail service and is adaptable across various vehicle platforms and applications. To date, the Waymo Driver has successfully conducted over ten million rider-only trips, leveraging its experience from over 100 million miles driven on public roads and billions of miles in simulation across more than 15 U.S. states.We are in search of passionate and skilled engineers to join our team and spearhead the post-training process for large language models (LLMs) in the realm of autonomous driving. You will collaborate with an exceptional team of researchers and engineers to innovate and elevate the next generation of advanced AI models.

Waymo is a pioneering force in autonomous driving technology, dedicated to becoming the world's most trusted driver. Originating from the Google Self-Driving Car Project in 2009, Waymo's mission focuses on creating the Waymo Driver—The World’s Most Experienced Driver™—with the goal of enhancing mobility access and significantly reducing traffic-related fatalities. The Waymo Driver is the backbone of our fully autonomous ride-hailing service and is adaptable across various vehicle platforms and applications. With over ten million rider-only trips completed and the experience of driving autonomously for over 100 million miles on public roads, alongside tens of billions of miles in simulation across more than 15 U.S. states, we are committed to transforming the future of transportation.The Simulator Team at Waymo is at the forefront of innovation, developing advanced simulations that replicate realistic environments for the testing, training, and validation of the Waymo Driver. Our team comprises a diverse, collaborative group of machine learning (ML) engineers, software engineers, and ML research engineers, who are dedicated to crafting industry-leading simulation solutions. Utilizing cutting-edge generative and reconstructive ML algorithms, we model the real world, including realistic agents, road systems, traffic dynamics, weather conditions, and a comprehensive sensor suite (Camera, Lidar, Radar).In this exciting role, you will be instrumental in enhancing the fidelity, scalability, controllability, and richness of our simulations by exploring the latest advancements in 3D world modeling. By leveraging state-of-the-art ML technologies trained on extensive datasets, you will help us create dynamic, semantically rich virtual worlds that have a direct impact on the development and validation of the Waymo Driver.You will report directly to a Senior Staff Engineering Manager.Responsibilities:Lead the design, development, and deployment of innovative 4D world models and generative systems aimed at generating ultra-realistic and controllable sensor outputs and semantics for simulation applications at Waymo.Architect and implement scalable, robust ML pipelines for the training, evaluation, and deployment of large-scale generative models within our simulation framework, employing techniques like model distillation and quantization.Develop and scale production-ready video generation methodologies (e.g., Diffusion, Flow Matching) to create dynamic and interactive simulation environments.Utilize Vision Language Models (VLMs) to enhance semantic comprehension and control within our world simulation products.Collaborate with leading research teams across Waymo and Alphabet to integrate state-of-the-art research in 4D world modeling and generative AI into scalable, production-ready solutions.Provide mentorship and technical guidance to fellow engineers on the team.

Waymo seeks a Senior Machine Learning Engineer with a focus on Large Language Model (LLM) and Vision-Language Model (VLM) distillation. This role is based in Mountain View, California, and centers on advancing the machine learning models that drive Waymo’s autonomous vehicles. Collaboration with both data science and engineering teams is a key part of the work, with an emphasis on refining and optimizing model performance. Key responsibilities Design and implement algorithms to distill LLMs and VLMs for greater efficiency. Work with large datasets to support learning and adaptation in self-driving systems. Partner with cross-functional teams to bring optimized models into production environments. Continually improve the performance and efficiency of models that power autonomous driving technology. Requirements Hands-on experience with machine learning, with a strong background in LLM and VLM distillation. Proven problem-solving skills and the ability to apply machine learning creatively to practical challenges. Interest in tackling real-world problems related to autonomous vehicles.

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 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.

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.

Join Our TeamAre you passionate about pioneering projects that harness advanced AI technology to unlock significant benefits from meetings and conversations? Become a vital member of our core AI team at Otter.ai, where you will collaborate with seasoned scientists and engineers dedicated to machine learning. As a Senior Machine Learning Engineer, your expertise in software engineering will be crucial in scaling and refining our machine learning systems. You will play a key role in transforming innovative research into production-ready features that enhance Otter’s summarization and conversational intelligence products.Your ContributionsDesign, construct, and advance extensive SID / ASR / NLP / LLM systems that drive essential product experiences, including summarization, chat, and speech comprehension for millions of interactions.Lead the creation and execution of training, fine-tuning, post-training, and inference methodologies for large language and speech models utilizing PyTorch and/or JAX while balancing quality, latency, cost, and dependability.Innovate and enhance model architectures, loss functions, decoding techniques, and training methodologies for speech and language models, guided by research insights and production restrictions.Oversee the entire ML system lifecycle, from initial research prototyping to production deployment, continuous monitoring, iteration, and long-term maintenance.Collaborate closely with product and infrastructure teams to translate groundbreaking research into scalable, production-grade systems that yield measurable user and business outcomes.Champion system-level enhancements in model performance, robustness, observability, and operational excellence using real-world conversational data.Establish technical direction and best practices for ML infrastructure, data pipelines, evaluation frameworks, and deployment workflows within a cloud environment.Diagnose and resolve complex issues related to model behavior, data quality, scalability, and system interactions, often preemptively before they affect users.Mentor and uplift fellow engineers, contributing to team standards, evaluating designs, and fostering a culture of sound technical decision-making and execution.

Coupang is hiring a Senior Staff II, Machine Learning Engineer (Tech Lead) for the Search team in Mountain View, USA. This position centers on building and enhancing the core technology behind search for one of South Korea’s largest e-commerce platforms. Role overview This role focuses on designing, developing, and refining machine learning systems that directly impact how millions of people shop online. As a technical lead, expect to work on projects that shape the search experience and improve the accuracy and speed of results for Coupang’s customers. Team and culture Coupang encourages teams to launch new initiatives and solve complex problems. Employees regularly see the results of their work, both in personal growth and in the company’s progress. The culture blends the drive of a startup with the stability and resources of a global public company. What makes this role unique Work on technology that influences how millions shop online. Join a team that prioritizes innovation and ongoing improvement. Contribute to a company known for transforming e-commerce in South Korea. Experience the energy of a startup with the support of a global organization. This is a chance to help define the future of commerce as part of Coupang’s technology-driven Search team.

Waymo is looking for a Senior Machine Learning Engineer focused on Runtime and Serving to help advance autonomous vehicle technology in Mountain View, CA. Role overview This position centers on developing and improving machine learning systems that support safe and efficient vehicle operation. The work involves optimizing algorithms and increasing the performance of deployed models in real-world conditions. What you will do Lead the design and implementation of machine learning systems used in autonomous vehicles Work on runtime and serving infrastructure to ensure reliable and efficient model deployment Contribute expertise to optimize algorithms for safety and operational performance Impact Your contributions will directly affect how Waymo's vehicles interpret and respond to their environment, supporting safer and more reliable autonomous driving.

About UsNuro is at the forefront of self-driving technology, dedicated to making autonomy available to everyone. Established in 2016, we are developing the world's most scalable driver by merging innovative AI with robust automotive hardware. Our flagship technology, the Nuro Driver™, is licensed to facilitate a variety of applications, from robotaxis to commercial fleets and personal vehicles. With years of successful self-driving deployments, Nuro is paving the way for automakers and mobility platforms to achieve commercial-scale autonomy, enabling a safer, more interconnected future.Role OverviewOur Sensor Data and Calibration team is expanding, and we are seeking a Senior Software Engineer with a strong background in robotics and machine learning. You will be responsible for designing both online and offline unstructured (targetless) sensor calibration algorithms. The ideal candidate will have extensive hands-on experience in the research, development, and application of machine learning methods utilizing various sensor data types (IMU, camera, lidar, radar, etc.). Familiarity with classical state estimation techniques, such as non-linear least-squares optimization and Lie algebra, is a plus.Key ResponsibilitiesDevelop cutting-edge state estimation and calibration algorithms leveraging both traditional robotics and modern machine learning techniques.Evaluate and characterize the accuracy and performance metrics of state estimation and calibration algorithms.Integrate calibration pipelines into the autonomy system while monitoring onboard performance and resource usage.Collaborate with cross-functional teams to enhance ML model training and overall performance assessment.Address critical inquiries regarding sensor data and overall autonomy effectiveness.