Fluidstack Logo

Fluidstack

Machine Learning Engineer

Reposted 3 Days Ago
In-Office
New York, NY, USA
224K-344K Annually
Mid level
In-Office
New York, NY, USA
224K-344K Annually
Mid level
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.
The summary above was generated by AI
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.

Similar Jobs

Yesterday
Hybrid
Senior level
Senior level
Financial Services
Lead engineering and ML platform work: design, develop, and maintain secure, scalable production code and ML infrastructure; drive adoption and validation of enterprise AI-assisted development practices; automate remediation, evaluate vendors, and coach teams on responsible AI, resiliency, and security within agile CI/CD workflows.
Top Skills: Aws SagemakerAzure MlGcp Ai PlatformPandasPythonPyTorchScikit-LearnSparkSQLTensorFlow
Yesterday
Hybrid
Junior
Junior
Financial Services
Design, develop, and troubleshoot secure, scalable production code and ML platforms. Drive enterprise adoption of AI-assisted engineering practices, automate remediation, evaluate vendor solutions, apply SDLC/CI-CD, and ensure responsible AI, data sensitivity, resiliency, and operational stability.
Top Skills: Ai-Assisted Development ToolsAws SagemakerAzure MlCi/CdCloud NativeGcp Ai PlatformMl PlatformsPandasPythonPyTorchScikit-LearnSdlcSparkSQLTensorFlow
Yesterday
Hybrid
Mid level
Mid level
Financial Services
Design, develop, deploy, and maintain secure, scalable ML platforms and application code. Drive adoption and validation of enterprise-authorized AI-assisted development practices, automate remediation and operational stability, lead vendor evaluations, and coach teams on responsible AI, resiliency, and secure SDLC practices.
Top Skills: AirflowAws SagemakerAzure MlCloudFormationDatabricksDockerFeature StoresGcp Ai PlatformKubernetesMicroservicesMl Metadata ManagementModel RegistryPandasPythonPyTorchRestful ApisScikit-LearnSnorkel AiSnowflakeSparkSQLTensorFlowTerraform

What you need to know about the NYC Tech Scene

As the undisputed financial capital of the world, New York City is an epicenter of startup funding activity. The city has a thriving fintech scene and is a major player in verticals ranging from AI to biotech, cybersecurity and digital media. It also has universities like NYU, Columbia and Cornell Tech attracting students and researchers from across the globe, providing the ecosystem with a constant influx of world-class talent. And its East Coast location and three international airports make it a perfect spot for European companies establishing a foothold in the United States.

Key Facts About NYC Tech

  • Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
  • Key Industries: Artificial intelligence, Fintech
  • Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
  • Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
  • Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account