At a Glance
- Tasks: Lead a team of engineers to enhance our machine learning platform and support model training.
- Company: Join Wayve, a pioneering AI company transforming urban driving with cutting-edge technology.
- Benefits: Competitive salary, equity, flexible hours, and bespoke learning opportunities.
- Other info: Enjoy a vibrant workplace with a private chef, socials, and a culture of collaboration.
- Why this job: Shape the future of autonomy while tackling one of the biggest challenges of our time.
- Qualifications: Experience in MLOps, infrastructure management, and embodied AI development required.
The predicted salary is between 80000 - 100000 £ per year.
We’re building artificial intelligence capable of complex driving using end-to-end deep learning; one which can scale across diverse urban environments. Wayve is building a full driving software system which is data-driven at every layer, learning to drive. Our unique end-to-end machine learning approach learns to drive in complex, never-seen-before urban environments. We learn to drive with computer vision by both observing human driving, and by using reinforcement learning. This is one of the world’s hardest and most impactful problems to solve. Which is why we’re building a diverse, world-class team of people who are motivated by the opportunity to work with brilliant people on challenging problems that leave a positive impact on society.
We are seeking an Engineering Manager for our Machine Learning Platform (MLP) team, responsible for leading a team of over six engineers. This role is pivotal in supporting our Science teams’ model training and cloud infrastructure efficiency. The ideal candidate will foster a collaborative and innovative culture, aligning team goals with company objectives and driving growth in AI technology.
Impact expected:
- Lead a growing team of 6+ engineers and enabling company wide goals.
- Deliver work needed to support all Science teams to train models and run cloud infrastructure efficiently.
- Provide a safe space for the team members to challenge each other and grow in their careers by intentional coaching and development.
- Partner with leadership, program managers and other peer EMs to maintain a culture of collaboration, impact, innovation, and health.
- Ensure goals of the team are aligned to company goals, and the team is set up for success.
- Contribute to KPIs and metrics of the team, systems and tooling they provide currently.
- Intentionally invest in improvements that move the needle on ML engineers’ productivity.
- Provide technical guidance where needed and rely on senior engineers on the team where necessary to make the decisions that drive the team and company forward.
- Anticipate the needs of the business 6-24 months out, identify areas where additional resources are needed or we need to grow new domain expertise, and pitch this to leadership for investment.
This role will be based in our London King’s Cross HQ, but we are open to discussing hybrid roles for the right candidate.
What we are looking for in our candidate:
Essential:
- Prior experience as a manager of MLOps and Infra teams.
- Passionate about fostering personal and professional growth in individual team members, building inclusive high performing teams.
- Industry experience with embodied AI development with real-world product impact.
- Experience with roadmap planning, stakeholder management, requirements gathering and alignment with peers towards milestones and deliverables.
- Prior hands-on Cloud Infrastructure and/or MLOps experience.
Desirable:
- Experience working in a project-based ('matrix') operating environment.
- Bachelors in Computer Science, Engineering, or similar experience (e.g STEM).
- Strong knowledge of Machine Learning and/or Cloud Infrastructure areas, such as Deep Learning, Natural Language Processing, Computer Vision, or Kubernetes, Azure/GCP/AWS etc.
What we offer:
- Competitive compensation with salary and equity.
- Immersion in a team of world-class researchers, engineers and entrepreneurs.
- A unique position to shape the future of autonomy and tackle one of the biggest challenges of our time.
- Bespoke learning and development opportunities.
- Flexible working hours – we trust you to do your job well, at times that suit you and your team.
- Private onsite chef, in-house bar, lots of socials, and more!
Engineering Manager - Machine Learning Platform employer: Wayve
Wayve is an exceptional employer, offering a dynamic work environment in the heart of London at our King’s Cross HQ. We prioritise employee growth through bespoke learning opportunities and foster a collaborative culture where innovation thrives. With competitive compensation, flexible working hours, and unique perks like a private onsite chef, we empower our team to tackle complex challenges in AI while making a positive societal impact.
StudySmarter Expert Advice🤫
We think this is how you could land Engineering Manager - Machine Learning Platform
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects, especially those related to MLOps and AI. This gives you a chance to demonstrate your expertise and passion for the field.
✨Tip Number 3
Prepare for interviews by brushing up on technical concepts and leadership scenarios. Practice common interview questions and think about how your experience aligns with the role of Engineering Manager. Confidence is key!
✨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. Plus, it shows you’re genuinely interested in joining our awesome team at Wayve.
We think you need these skills to ace Engineering Manager - Machine Learning Platform
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Engineering Manager role. Highlight your MLOps and infrastructure management experience, as well as any leadership roles you've held. We want to see how you can contribute to our mission!
Craft a Compelling Cover Letter:Your cover letter is your chance to show us your passion for AI and team growth. Share specific examples of how you've fostered collaboration and innovation in previous roles. Let us know why you're excited about joining our diverse team at Wayve!
Showcase Your Technical Skills:Don’t shy away from detailing your technical expertise in machine learning and cloud infrastructure. Mention any hands-on experience you have with tools like Kubernetes or AWS. We’re looking for someone who can provide technical guidance and drive our team forward!
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 shows us you’re keen on being part of our journey at Wayve!
How to prepare for a job interview at Wayve
✨Know Your Stuff
Make sure you brush up on your MLOps and cloud infrastructure knowledge. Be ready to discuss your hands-on experience with machine learning platforms and how you've managed teams in the past. This role is all about technical acumen, so show them you know your way around the tech!
✨Show Your Leadership Style
Prepare to talk about how you foster growth and collaboration within your team. Think of specific examples where you've coached team members or led projects that required stakeholder management. They want to see your vision for building a high-performing team.
✨Align with Company Goals
Understand Wayve's mission and how the Engineering Manager role fits into their objectives. Be ready to discuss how you would align your team's goals with the company's vision, especially in terms of driving innovation and impact in AI technology.
✨Ask Insightful Questions
Prepare thoughtful questions that demonstrate your interest in the company and the role. Inquire about their current challenges in model training or cloud infrastructure, and how they envision the future of their Machine Learning Platform. This shows you're not just interested in the job, but also in contributing to their success.