Ritual
Machine Learning Engineer
NEWRemoteFull-timeGlobal
š 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
- āExperience as a software engineer
- āExperience building and serving machine learning models
- āFamiliarity with Python
- āFamiliarity with ML frameworks such as PyTorch, TensorFlow and Jax
- āFamiliarity with HuggingFace and open-source ML stacks
- āAbility to reason through machine learning system tradeoffs
- āFamiliarity with TinyML, Triton, CUDA, ROCm, Exo, MLIR or Halide
- āFamiliarity with DataOps, MLOps and ML orchestration pipelines
- āUnderstanding of modern ML architectures and inference performance tradeoffs
- āExperience or interest in open-source ML products
- āInterest in user privacy, computational integrity or censorship resistance
- āExperience at fast-growing companies or startups
Responsibilities
- āScale model inference and run predictions at scale
- āConnect ML models to APIs, browsers, and applications
- āDesign and implement large-scale data and ML pipelines through the full product lifecycle
- āAutomate data ETL pipelines
- āDeploy machine learning models to production
- āCollaborate with cross-functional teams to create and ship products
Benefits & Perks
- āHighly competitive compensation package including annual discretionary bonus
- āOptimized tax structure compared to many web3 startups
- ā100% of premiums covered for high quality healthcare
- āAggressive company 401k match
- āFlexible work arrangement: fully remote or hybrid
- āParticipation in virtual and in-person events
Tech Stack
ETLTritondata pipelineTensorFlowmachine learningCUDAHuggingFacePyTorchDeploymentInference