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Inference Platform Technical Lead

London

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. 

In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we 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; we back each other to deliver impact.  

Make Wayve the experience that defines your career!  

The role

As the Tech Lead for our Inference Platform, you will spearhead the development and evolution of our machine learning inference infrastructure, tackling complex challenges around job scheduling, resource efficiency, and platform reliability. You will define and implement technical strategies that ensure optimal utilization of high-performance GPU clusters, facilitating rapid iteration and seamless deployment of cutting-edge ML models.

Your leadership will directly influence the efficiency and scalability of inference services, addressing challenging technical problems such as intelligent workload scheduling, dynamic resource allocation (persistent & burst capacity), low-latency inference delivery, and multi-model inference pipelines. Solving these problems will significantly enhance the productivity of our machine learning engineers and researchers, enabling groundbreaking advancements in ML at scale.

Challenges You Will Own

  1. Technical Leadership & Strategy:
    • Define and drive the technical roadmap for the inference platform.
    • Lead the architecture, design, and implementation of high-performance, scalable inference solutions.
    • Continuously monitor and anticipate advances in the rapidly evolving AI landscape, ensuring our GPU infrastructure evolves proactively to meet emerging model requirements and workloads.
  2. Platform Development:
    • Develop and optimize large-scale GPU inference infrastructure, ensuring high availability, low latency, and optimal resource utilization.
    • Advance smart scheduling, multi-model inference pipelines, and efficient resource distribution (persistent & burst capacity).
    • Ensure seamless integration with Flyte or comparable orchestration frameworks to enable self-service for ML engineers.
  3. Operational Excellence:
    • Implement robust observability, monitoring, and alerting systems to achieve and maintain >99% platform uptime.
    • Provide comprehensive observability into job and infrastructure reliability, resource utilization, and efficiency, empowering data-driven operational decisions.
    • Collaborate closely with SRE teams to automate operations, scaling, failover processes, and incident response.
  4. Talent and Team Development:
    • Mentor and guide team members, fostering a culture of technical excellence, continuous improvement, and cross-team collaboration.
    • Actively participate in recruitment, retention, and career development of engineers on your team.

What We Are Looking For in Our Candidate

Essential:

  • Proven experience (8+ years) in software engineering, including at least 2 years leading or architecting ML or inference platforms.
  • Expertise in managing GPU-accelerated inference platforms at scale, ideally using Kubernetes (AKS or equivalent).
  • Hands-on experience with Flyte, Ray, or similar large-scale orchestration tools.
  • Strong technical skills in system architecture, scalability, performance optimization, and operational reliability.
  • Exceptional communication skills to effectively collaborate across AI research, infrastructure, and platform teams.
  • Demonstrable experience mentoring and developing engineering talent.
  • Bachelor's degree or higher in Computer Science, Engineering, or a related technical field.

Desirable:

  • Track record of delivering systems that support millions of inference hours across multi-region or multi-cloud environments.
  • Experience optimizing data locality, resource allocation, and efficiency of ML workloads.
  • Familiarity with model introspection and monitoring tools such as W&B or MLflow.
  • Proven ability to manage complex technical roadmaps with multiple stakeholders.

This is a full-time role based in office.  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 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.

For more information visit Careers at Wayve. 

To learn more about what drives us, visit Values 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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