Machine Learning Engineer

Posted 24 Days Ago
4 Locations
In-Office
224K-279K Annually
Mid level
Artificial Intelligence • Software
The Role
Design, build, and own ML/LLM systems for operations: forecasting, schedule risk detection, document extraction, and agentic systems. Handle end-to-end model lifecycle, deploy and evaluate in production, and integrate predictions into operational tools.
Summary Generated by Built In
About Fluidstack

We exist to make humanity more free. For most of human history, you farmed or you starved. Technology gave people more time for the things they wanted to do, instead of things they had to do. Powerful AI will be the biggest lever for human choice we've ever built - but only if models are aligned with what humanity actually wants. There are groups building AI who don't share these goals. Whoever deploys frontier compute infrastructure fastest will decide whether AI expands human freedom or shrinks it.

We're singularly focused on delivering 10 to 100s of GWs of compute faster than anyone else, rethinking every layer of the stack. We acquire power, design and build data centers, and operate them - with teams spanning hardware and software. Speed and scale are our key differentiators. Come be a part of building civilization-scale infrastructure for AI.


We hire people who care deeply about this problem space. If that is you, please apply!

How We Operate
  • Extreme ownership. Full autonomy. Own things end to end often taking on scope outside your core role without being asked to get things done.

  • Velocity. We drive everything forward as fast as possible.

  • First principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins.

  • Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward.

Role Scope
  • Build ML and LLM systems that run inside the company's operations: forecasting build timelines, flagging schedule risk, and extracting structure from vendor documents.

  • Own models end to end, from problem framing and data through deployment, evaluation, and iteration in production.

  • Ship agentic systems with real guardrails, authorization, audit, and evals, so agents act on company systems instead of just advising.

  • Partner with data engineering and product pods to put predictions in the tools people already use.

What We're Looking For
  • The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would.

  • You've shipped ML or LLM features to production and owned them after launch.

  • You've built evaluation harnesses that told you the truth about model quality before users did.

  • You reach for the simplest model that works and can defend the choice.

  • You've worked hands-on with LLM APIs, fine-tuning, or retrieval systems on real business problems.

  • You write production-quality code and work fluently with AI coding tools.

  • Bonus: Forecasting or scheduling problems. Document extraction at scale. Agentic frameworks and MCP. Temporal or workflow engines.

We are committed to pay equity and transparency.

Fluidstack is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Fluidstack will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law.

You will receive a confirmation email once your application has successfully been accepted. If there is an error with your submission and you did not receive a confirmation email, please email [email protected] with your resume/CV, the role you've applied for, and the date you submitted your application-- someone from our recruiting team will be in touch.

Skills Required

  • Shipped ML or LLM features to production and owned them after launch
  • Built evaluation harnesses that assess model quality before users
  • Own models end-to-end from problem framing and data through deployment and iteration
  • Worked hands-on with LLM APIs, fine-tuning, or retrieval systems on real business problems
  • Write production-quality code and work fluently with AI coding tools
  • Ship agentic systems with guardrails, authorization, audit, and evaluations
  • Collaborate with data engineering and product teams to integrate predictions into tools
  • Forecasting or scheduling problem experience
  • Document extraction at scale experience
  • Familiarity with agentic frameworks and MCP
  • Experience with Temporal or other workflow engines
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The Company
HQ: London
30 Employees
Year Founded: 2017

What We Do

Instantly reserve dedicated clusters of NVIDIA H200s and GB200s for any scale to supercharge your training and inference workflows.

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