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Ritual

Machine Learning Engineer

NEW
RemoteFull-timeGlobal
šŸ  Hybrid
RemoteRemote work position availableActivePosted within the last 30 days

Job Description

[AI-summarized by JobStash]

You will work on applied machine learning projects that automate data ETL pipelines, deploy and scale machine learning models, and ship customer-facing products. You will connect models to APIs, browsers, and applications, design and implement large-scale data and ML pipelines, and collaborate with cross-functional partners to deliver production-ready systems.

Requirements

  • ā—Experience as a software engineer
  • ā—Experience building and serving machine learning models
  • ā—Familiarity with Python
  • ā—Familiarity with ML frameworks such as PyTorch, TensorFlow and Jax
  • ā—Familiarity with HuggingFace and open-source ML stacks
  • ā—Ability to reason through machine learning system tradeoffs
  • ā—Familiarity with TinyML, Triton, CUDA, ROCm, Exo, MLIR or Halide
  • ā—Familiarity with DataOps, MLOps and ML orchestration pipelines
  • ā—Understanding of modern ML architectures and inference performance tradeoffs
  • ā—Experience or interest in open-source ML products
  • ā—Interest in user privacy, computational integrity or censorship resistance
  • ā—Experience at fast-growing companies or startups

Responsibilities

  • ā—Scale model inference and run predictions at scale
  • ā—Connect ML models to APIs, browsers, and applications
  • ā—Design and implement large-scale data and ML pipelines through the full product lifecycle
  • ā—Automate data ETL pipelines
  • ā—Deploy machine learning models to production
  • ā—Collaborate with cross-functional teams to create and ship products

Benefits & Perks

  • ā—Highly competitive compensation package including annual discretionary bonus
  • ā—Optimized tax structure compared to many web3 startups
  • ā—100% of premiums covered for high quality healthcare
  • ā—Aggressive company 401k match
  • ā—Flexible work arrangement: fully remote or hybrid
  • ā—Participation in virtual and in-person events

Tech Stack

ETLTritondata pipelineTensorFlowmachine learningCUDAHuggingFacePyTorchDeploymentInference
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