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AI Experimentation Platform Engineer

Fynix Studio

Remote · $130k – $160k / year · Full-time · Posted 5 months ago

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About the role

Every ML team runs experiments. Almost none of them can reproduce last month's results. Our experimentation platform tracks every variable — data versions, hyperparameters, code commits, infrastructure configs — so that any experiment can be reproduced exactly, months later, by anyone on the team. You'll own the platform that 14 internal ML teams rely on daily: experiment tracking with automatic lineage capture, hyperparameter optimization services, compute allocation for experiment jobs, and the comparison dashboards that help researchers pick winners from thousands of runs. The current system handles 2,000 experiments per day. We're scaling to 10,000 as three new teams onboard next quarter. You'll need to rethink the storage layer, optimize the tracking API for high-throughput writes, and build the caching infrastructure that keeps dashboards responsive when researchers query across millions of logged metrics.

Fully remote.

Responsibilities

  • Scale the experimentation platform from 2,000 to 10,000 daily experiments
  • Build automatic experiment lineage capture across data, code, and infrastructure
  • Design hyperparameter optimization services with efficient compute allocation
  • Optimize the tracking API and storage layer for high-throughput metric logging
  • Create comparison dashboards for experiment analysis across millions of runs

Requirements

  • 5+ years in ML infrastructure or platform engineering
  • Deep MLflow or equivalent experiment tracking experience
  • Kubernetes and Docker expertise for compute orchestration
  • Strong Python and API design skills
  • Experience scaling data-intensive platforms under high write loads

Benefits

  • Fully remote — US West Coast hours
  • Senior compensation with equity
  • $3,000 annual learning budget
  • Impact on ML productivity across 14 teams

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Categories

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python mlflow docker kubernetes airflow

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$130k – $160k / year

AI Expertise

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python mlflow docker kubernetes airflow aws fastapi

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