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- Tasks: Optimise large-scale training jobs and enhance AI model efficiency.
- Company: Wayve, a leader in Embodied AI technology with a diverse culture.
- Benefits: Competitive salary, inclusive environment, and opportunities for professional growth.
- Other info: Collaborative atmosphere with a focus on performance optimisation.
- Why this job: Join a pioneering team shaping the future of automated driving.
- Qualifications: Experience in optimising GPU training jobs and strong Python coding skills.
The predicted salary is between 36000 - 60000 £ per year.
Join to apply for the Machine Learning Engineer - Pre-Training role at Wayve. At Wayve, we are committed to creating a diverse, fair, and respectful culture that is inclusive of everyone regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis 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 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.
The Role
We are seeking skilled engineers to join our Training Tech team, working on optimizing large-scale training jobs to scale our models through the next order of magnitude. The successful candidate will increase the efficiency of training jobs to allow Wayve to train larger models faster.
Key Responsibilities
- Profile training jobs to identify bottlenecks, e.g. using NVIDIA Nsight Systems.
- Design and implement efficiency improvements to maximize MFU, e.g. tensor parallelism, model compilation, mixed precision.
- Design and implement observability tools, e.g. to track MFU.
- Collaborate closely with Research teams to integrate training efficiency improvements and create a culture of performance optimization.
About You
Essential qualifications and experience:
- Experience optimizing large-scale training jobs on GPU compute clusters.
- Experience working in platform teams and with research teams.
- Experience reporting and tracking benchmarked performance over time in an open and accessible way.
- Ability to write high-quality, well-structured, and tested Python code.
- BS or MS in Machine Learning, Computer Science, Engineering, or a related technical discipline, or equivalent experience.
Desirable skills
- Solid experience working with concurrent, parallel, and distributed computing.
- Experience using NVIDIA Nsight Systems.
- Experience implementing GPU kernels.
- Knowledge of computing fundamentals—what makes code fast, secure, and reliable.
Machine Learning Engineer - Pre-Training employer: Wayve
Contact Detail:
Wayve Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer - Pre-Training
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to machine learning and optimising training jobs. It’s a great way to demonstrate what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice coding challenges and be ready to discuss your past experiences in detail.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people at Wayve. Plus, we love seeing candidates who take that extra step!
We think you need these skills to ace Machine Learning Engineer - Pre-Training
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with GPU compute clusters and any relevant projects that showcase your skills in optimising training jobs.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how your background aligns with Wayve's mission. Don't forget to mention any collaborative experiences with research teams!
Showcase Your Technical Skills: When filling out your application, be specific about your technical skills. Mention your proficiency in Python and any experience with tools like NVIDIA Nsight Systems. We want to see what makes you stand out!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Wayve
✨Know Your Stuff
Make sure you brush up on your knowledge of machine learning concepts, especially those related to optimising large-scale training jobs. Be ready to discuss your experience with GPU compute clusters and any specific projects where you've improved training efficiency.
✨Showcase Your Coding Skills
Since high-quality Python code is essential for this role, prepare to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so practice writing clean, efficient code and be ready to explain your thought process.
✨Understand the Company Culture
Wayve values diversity and inclusivity, so it’s important to show that you align with their culture. Be prepared to discuss how you’ve contributed to a positive team environment in the past and how you can bring that mindset to their team.
✨Prepare Questions
Interviews are a two-way street! Prepare thoughtful questions about the role, the team dynamics, and how they measure success in training efficiency. This shows your genuine interest in the position and helps you assess if it's the right fit for you.