AI Infrastructure Engineer in Cambridge

We are a startup building foundational technology to address performance, scalability, and resiliency challenges in large-scale AI data center clusters.

We are backed by top-tier VC firms and notable angel investors.

The company is led by experienced builders and operators who have founded companies, taken them to scale, and exited successfully.

We work with a strong sense of unity and shared responsibility, and we expect trust, integrity, and respect in how we collaborate and make decisions.

We hold ourselves accountable to one another and to the quality of the work we deliver.

Headquartered in Silicon Valley, we operate across a mix of remote and on-site locations in the U.

S., Canada, and the U.

We aim to create an environment where people are treated fairly, supported in their growth, and are empowered to do meaningful work alongside others who take the craft seriously.

Some Recent Press

https://www. reuters. com/business/delos-data-chip-startup-founded-by-intel-veterans-raises-100-million-ai-networks-2026-09-15/

We are looking for

We are looking for a talented Forward Deployed Engineer to be embedded with one of our strategic customers running our software in production.

In this role, you will work hands‐on inside the customer's AI data center environment, solving hard deployment and operational problems and turning what you learn into new product features alongside our engineering team.

While most of your time will go to this customer, you will also bring your field experience to future customer pursuits, contributing hands‐on to RFPs, technical evaluations, and new deployments.

We are looking for a "builder" who has built and run AI infrastructure at scale, ideally at a neocloud or frontier AI lab, and who thrives on solving real problems in production.

Key Responsibilities

• Serve as the dedicated technical owner for a strategic customer's production deployment of our software.
• Work hands‐on in the customer's AI data center environment, including GPU/XPU clusters, high‐speed fabrics, schedulers, and orchestration layers.
• Lead integration, troubleshooting, performance tuning, and root‐cause analysis for production issues.
• Guide the customer through software upgrades, cluster expansion, and new feature rollouts.
• Build runbooks, integration guides, and automation tailored to the customer's environment.
• Build deep working relationships with the customer's infrastructure, SRE, and platform engineering teams.
• Turn customer problems into clear technical requirements and work with our engineering team to design and deliver new features.
• Write and test code (Python, Bash, APIs) to prototype fixes, integrations, and tooling in the field.
• Feed structured insights on bugs, feature gaps, and competitive needs back into product and engineering, and align customer milestones with our product release cycles.
• Contribute hands‐on technical work to RFPs, RFIs, technical evaluations, and proof‐of‐concept deployments for new customers.
• Help build reference architectures, demo environments, and technical collateral, and represent the company at customer briefings, technical workshops, and industry events as needed.
• Required Skills and Qualifications
• 5+ years in a hands‐on infrastructure engineering role (forward deployed engineer, SRE, platform engineer, solutions architect, or similar) with direct customer or internal‐customer exposure.
• Proven AI data center experience: deploying and operating GPU/XPU clusters with high‐speed interconnects (Infini Band/Ro CE/Ethernet/NVLink), job scheduling, and training/inference workloads.
• Strong software problem‐solving skills, including debugging complex distributed systems and writing production‐quality scripts and tools in Python and Bash.
• Hands‐on experience with AI infrastructure software such as Kubernetes, Slurm, Tinkerbell, Canonical Maa S, observability stacks, or infrastructure management platforms.
• Deep proficiency with Linux, containers, and APIs.
• Track record of working with engineering teams to define and ship features based on field needs.
• Clear communicator, comfortable whiteboarding with SREs and briefing senior infrastructure leaders.
• Willingness to work on‐site with the customer and travel as needed.
• Desired Skills
• Experience as a forward deployed or embedded engineer dedicated to a single large customer.
• Experience taking early‐stage or pre‐launch products into hyperscale, neocloud, or AI lab environments.
• Familiarity with inference serving and performance optimization (e. g., KV‐cache behavior, disaggregated serving, GPU utilization analysis).
• Background with observability stacks (Prometheus, Grafana, Open Telemetry) and data pipelines for infrastructure analytics.
• Prior experience at an AI infrastructure software vendor, GPU cloud provider, networking vendor, or hyperscaler.

Education

• Bachelor's or Master's degree in Computer Engineering, Computer Science, Electrical Engineering, or a related field, or equivalent practical experience.
• Location

This role is on‐site at customer's office based in Cambridge, UK.

Compensation

Target base salary for this role is £115k–£135k per year + equity. Our salary ranges are determined by role, level, experience, and location.

We are an equal opportunity employer.

We value a range of perspectives and experiences and make employment decisions based on merit and business needs.

We do not discriminate on the basis of legally protected characteristics.