Member of Technical Staff, Infrastructure Engineer

Odyssey; Greater London; On-site; GBP 60,000 - 80,000; Full time

Job summary

Odyssey is looking for an engineer in Greater London. You will develop the low-latency model inference platform and scale core data processing infrastructure. This role requires strong programming skills in Python or Go, and experience with Docker and Kubernetes.

The ideal candidate is motivated by building for the future of AI, with a collaborative mindset and excellent communication abilities. Experience in managing large-scale systems with GPU workloads is essential for this groundbreaking work.

Qualifications

• Motivated by building for the frontier in AI technologies.
• Deep experience with containerization and orchestration.
• Proven track record in large-scale GPU computational systems.

Responsibilities

• Develop and operate low-latency model inference platform.
• Engineer and scale core data processing infrastructure.
• Design and maintain GPU-based training clusters.
• Automate infrastructure provisioning and monitoring.
• Drive performance tuning and cost optimization.
• Collaborate with researchers to optimize workflows.

Skills

Strong programming skills (Python, Go)

Experience with Docker and Kubernetes

Building and managing distributed systems

Infrastructure as Code (Terraform)

Collaboration and communication skills

Job description

Who We Are

Odyssey is an AI lab pioneering general world models: causal, multimodal systems that learn to predict and interact with the world over long horizons. This foundational technology promises to revolutionize robotics, science, healthcare, education, gaming, defense, and beyond.

Odyssey’s founders previously pioneered the most complex application of physical AI: self-driving cars. They’ve now brought together a world‑class research team from DeepMind, Tesla, Waymo, Meta, Apple, and Wayve, who have made significant contributions to language models (DeepMind Gemini), video models (DeepMind Veo), world models (Wayve GAIA), and autonomous systems (Tesla FSD).

Odyssey has raised significant venture capital from GV, Amazon, AMD, EQT, NVIDIA, Natural Capital, In‑Q‑Tel, Elad Gil, Jeff Dean, Guillermo Rauch, Garry Tan, Kyle Vogt, and researchers from OpenAI, DeepMind, MSL, Recursive, and Thinking Machines.

What We’re Looking For

We are looking for an engineer who thrives on building the engines that make groundbreaking research and products possible. You think in systems, love performance, and get energy from turning theoretical bottlenecks into beautifully efficient reality. You’re excited to design and support infrastructure not just for scale, but for speed, creativity, and discovery. You want to build the compute substrate that lets Odyssey’s world models imagine, act, and interact in real time.

What You’ll Do

• Develop and operate our low‑latency model inference platform, ensuring high availability, scalability, and efficient resource utilization for Odyssey’s world models.
• Engineer and scale our core data processing infrastructure (e.g., Flyte, Ray with k8s) to handle petabyte‑scale datasets.
• Design, build, and maintain our large‑scale, GPU‑based training clusters for deep learning, focusing on usability, high throughput and reliability.
• Automate infrastructure provisioning, configuration, monitoring, and alerting using Infrastructure as Code (IaC) principles.
• Drive performance tuning, cost optimization, and reliability improvements across the entire stack.
• Collaborate closely with researchers and product developers to understand their requirements, optimize their workflows, and improve platform usability.

Who You Are

• Motivated by building for the frontier: you want to shape the compute and infrastructure foundation of a lab redefining how people create and interact with media.
• Strong programming skills (e.g., Python, Go, or similar) and a solid understanding of software engineering best practices.
• Deep, hands‑on experience with containerization (e.g., Docker), container orchestration (Kubernetes) and Infrastructure as Code (Terraform).
• Proven experience building and managing large‑scale, distributed systems with GPU computational workloads (e.g., compute platforms, data pipelines, or high‑availability services).
• Experienced in designing infrastructure for ML workloads where performance, parallelism, and data movement are critical.
• A collaborative mindset and excellent communication skills, with a passion for building developer‑friendly platforms.