Machine Learning Engineer

FluidStack
Austin, New York, San Francisco, Seattle
Workplace: OnsiteFull timeUSD 269,000 - 317,000 annuallyFunction: Data Science & Machine LearningSkills: ["Extreme ownership","Autonomy","Velocity","First principles","Long-hour intensity"]

Build ML and LLM systems that run inside operations, including 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 production iteration. Ship agentic systems with guardrails, authorization, audit, and evals, and partner with data engineering and product teams to deliver predictions in tools people already use.

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FursaFursa
FluidStack
FluidStack
1 month ago

Machine Learning Engineer

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Source: Company careers pageValidated by: Fursa AI
Last checked: 2 hours agoStatus: Live

Job Summary

Build ML and LLM systems that run inside operations, including 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 production iteration. Ship agentic systems with guardrails, authorization, audit, and evals, and partner with data engineering and product teams to deliver predictions in tools people already use.
Location: Austin, New York, San Francisco, Seattle
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning

Key Responsibilities

  • •Build ML and LLM systems for operational use, including 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 production iteration.
  • •Ship agentic systems with real guardrails, authorization, audit, and evals so agents act on company systems.
  • •Partner with data engineering and product pods to integrate predictions into tools people already use.
  • •Drive ML/LLM system improvements using feedback from evaluation and real production outcomes.

Pay and Benefits

Salary: USD 269,000 - 317,000 annually

Key Requirements

  • •You’ve shipped ML or LLM features to production and owned them after launch.
  • •You’ve built evaluation harnesses that measure model quality before users do.
  • •You work 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.
  • •You can choose and defend the simplest model that works; bonus experience includes forecasting/scheduling, document extraction at scale, agentic frameworks and MCP, or Temporal/workflow engines.
Experience:MLLLM
Skills:Extreme ownershipAutonomyVelocityFirst principlesLong-hour intensity
Tech Stack:Machine learningLarge language modelsLLM APIsFine-tuningRetrieval systemsEvaluation harnessesAgentic systemsGuardrailsAuthorizationAuditEvalsMCPTemporalWorkflow engines

Company Brief

FluidStack
Builds and deploys large-scale GPU cloud infrastructure for AI labs, enterprises and governments, providing high-performance AI training and inference capacity and rapid data-center deployment services.
Industry: Cloud Computing
Company Size: Medium (51 to 250 employees)
Growth: Growth Stage Startup
Funding: Series A
Headquarters: New York, United States
Founded: 2017
Glassdoor
Glassdoor: 4.7
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