Machine Learning Engineer, Foundation Models
Sunnyvale
Applications have closed
Wayve
Learn how Wayve is leading the way in autonomous driving with their innovative embodied AI technology.Wayve is at the cutting edge of embodied intelligence, developing advanced AI with end-to-end deep learning to navigate the complexities of diverse environments. Our innovative strategy integrates the latest in machine learning, sidestepping conventional methods, to understand and adapt to urban landscapes like never before. We're dedicated to solving one of the most formidable challenges of our time, gathering a diverse team of exceptional minds driven not just by the work but by the opportunity to leave a profound impact on the world through innovative technology.
At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law.
About us
Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.
Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving. Join our world-class team as we tackle today's most complex challenges and pave the way for a smarter, safer future.
At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment. Make Wayve the experience that defines your career!
About the Role
Our team is seeking a talented Engineer to propel our ambitious research forward. We're not just another team; we're a dynamic blend of Applied Scientists, Machine Learning Engineers, and Software Engineers united together to apply state of the art research to the road. From pioneering advancements in Offline Reinforcement Learning (RL) and Reward Learning from Human Feedback (RLHF) to developing groundbreaking, large-scale, embodied Foundation Models, our projects are designed to dramatically enhance our product's capabilities. But it's not just about what we do—it's how we do it. We believe in the power of cross-functional collaboration, rigor in engineering, and a relentless pursuit to innovate.
In this role, you might:
- Collaborate with Applied Scientists and Machine Learning Engineers on advanced, multimodal, embodied Foundation Models, enhancing your Machine Learning Engineering (MLE) skills.
- Develop and manage comprehensive datasets and data engineering pipelines, supporting complex research initiatives.
- Craft and refine tools for rapid exploration and detailed visualizations, pushing the boundaries of research efficiency.
- Drive observability, monitoring, and performance optimizations to elevate system reliability and performance.
- Create tools specifically designed to expedite solving research problems, showcasing your problem-solving capabilities.
- Work seamlessly with platform teams, facilitating integration and leveraging shared resources for broader impact.
You’d be a great match for this role if:
- You champion engineering best practices, ensuring solutions are scalable, efficient, and maintainable. You prioritize code quality, readability, and reusability, understanding that these qualities are key to long-term success.
- You excel in ambiguous, fast-paced environments, adept at navigating and thriving amidst change.
- You get excited about optimizing pre-training runs, for example, including data pre-processing, CUDA optimization, model quantization and optimization, increasing throughput of training jobs (e.g., FP-8).
- (A plus) You have experience with MLOps or ML Infrastructure, reflecting your ability to streamline machine learning workflows.
Essentials:
- 2+ years of experience with a BS or MS in Computer Science, Engineering, or related discipline, or equivalent experience.
- Solid experience with Python or proficiency in a systems/backend programming language with the ability to quickly adapt to Python.
- Demonstrated experience in system design, capable of architecting robust, scalable solutions.
- Proven track record of working in teams to successfully deliver open-ended projects.
- Ability to work cross-functionally, bridging gaps between teams to drive collective goals
We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply.
This is a full-time role based in our office in Sunnyvale. At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. We operate core working hours so you can determine the schedule that works best for you and your team.
For more information visit Careers at Wayve.
DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.
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* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Computer Science CUDA Deep Learning Engineering Excel Machine Learning ML infrastructure MLOps Pipelines Python Reinforcement Learning Research RLHF
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