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Forward Deployed Engineer

Job in Cambridge, Cambridgeshire, CB21, England, UK
Listing for: Delos Data Inc
Full Time position
Listed on 2026-10-19
Job specializations:
  • Software Development
    AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 115000 - 135000 GBP Yearly GBP 115000.00 135000.00 YEAR
Job Description & How to Apply Below
Forward Deployed Engineer - AI

About us

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.K. 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:

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/RoCE/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 MaaS, observability…

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