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ML Infrastructure

The systems that make machine learning actually work at scale: training pipelines, distributed compute and model serving. Part of our Machine Learning & Generative AI discipline, we work with engineers building ML infrastructure from early-stage labs through to production at scale.

ML Infrastructure jobs

Hiring in ML Infrastructure isn't like other technical hiring.

ML infrastructure hiring sits at the crossover between distributed systems engineering and applied machine learning, and that combination is genuinely rare. Most software engineers haven't built training pipelines at scale, and most ML researchers haven't built production infrastructure.

At Enigma, we track the engineers building and open-sourcing the systems behind today's largest models, so when a search opens up, we're not starting from a keyword search.

MLSys
Model Training
ML Pipelines
PyTorch
GPU's

Many of the teams we support here are also hiring across Platform Engineering and Generative AI, LLMs & RL, so we're used to building the fuller picture of a team, not just filling a single seat.

Start a Search

What a Good Partnership Looks Like

We work best as a close, informed extension of your team, not a CV-forwarding service.

Define the Brief

Align on what "great" actually looks like, whether that's a first ML infrastructure hire or a platform lead.

Map the Market

Understand who's active in ML infrastructure right now, across foundation model labs, applied AI startups and adjacent platform teams.

Focused Search

Target the right people, not more people, drawing on our experience building out research labs with Research Engineers and ML Software Engineers.

Guide the Process

Keep momentum, clarity and alignment from first call through to offer stage.

Deliver & Refine

Secure the hire, then refine the approach for next time so every search gets sharper.

Trusted across the ecosystem

Trusted across the ML Systems ecosystem Trusted across the Model Ops ecosystem Trusted across the Applied Compute ecosystem

FAQs

There's real overlap, but ML infrastructure engineers specifically build and optimise the systems training and serving models: distributed training, data pipelines, inference serving. Platform engineering, our related specialism, tends to cover the broader engineering platform a company runs on. We work out which your role actually needs before we start sourcing.

Strong systems experience without ML context is common, and often not enough on its own. The best candidates understand both why a training run is expensive and how to make it cheaper. We screen for that combination specifically, rather than treating it as a pure infrastructure hire.

We look past "worked with GPUs at scale" and ask candidates to walk through a specific training run they owned: what broke, what they optimised, and what the actual before-and-after numbers were. Vague claims about scale don't hold up to that kind of questioning.

No. Plenty of teams doing applied AI or fine-tuning existing models still need strong ML infrastructure. Efficient data pipelines, cost-effective inference and reliable serving all matter well below foundation-model scale.

Yes, this is a common use case for our Contractor Hire and Statement of Work services, bringing in a specialist for a defined infrastructure project rather than a permanent headcount.

Not ready to submit a brief?

Let's just talk it through. Tell us what you're building and we'll help you work out what the hire actually looks like.

Book a Call

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Locations

UK Office
St Albans
Eleanor Ormerod House,
Victoria St, AL1 3LT

US Office
Austin, TX
9901 Brodie Lane,
Suite 160 PMB1553

Contact Us

UK +44 2080 890 188
US +1 737 225 3237
hello@enigma-rec.ai

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