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Staff Machine Learning Engineer

Posted 10 days agoViewed

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💎 Seniority level: Staff, 10+ years

📍 Location: Canada, Latin America, Alabama, Arizona, California, Colorado, Connecticut, Florida, Georgia, Illinois, Indiana, Massachusetts, Minnesota, Nevada, New Jersey, New York, North Carolina, Oregon, Pennsylvania, Rhode Island, Tennessee, Texas, Utah, Virginia, Washington

💸 Salary: 140000.0 - 220000.0 USD per year

🔍 Industry: Retail AI

🏢 Company: Lily AI

⏳ Experience: 10+ years

🪄 Skills: PythonKubeflowKubernetesMachine LearningMLFlowPyTorchAzure

Requirements:
  • 10+ years in building large-scale machine learning solutions and ML Ops practices.
  • Working with LLM APIs and serving LLMs in-house at scale.
  • Proficiency in Kubernetes, RDBMS, and API-driven development.
  • Experience in model serving in low-latency, high-throughput use cases.
  • Knowledge of observability, data pipeline design, service scaling, and cost optimization.
  • Strong emphasis on code hygiene, including review, documentation, testing, and CI/CD practices.
  • Proficiency in Python and PyTorch.
  • Extensive experience with the scientific Python ecosystem.
  • Proficiency in cloud-native application development.
  • Action-oriented with the ability to articulate complex concepts.
Responsibilities:
  • Define, design, and maintain scalable Machine Learning data pipelines, training infrastructure, and inference systems.
  • Optimize, benchmark, and productionize deep learning models to extract high-value product attributes.
  • Drive cost efficiency and throughput improvements, owning relevant KPIs.
  • Promote and implement software engineering best practices across the team.
  • Shape and evolve the technical stack to meet business and technical needs.
  • Transition research prototypes into robust, production-ready systems.
  • Deploy, monitor, and continuously improve models in production environments.
  • Optimize model performance, focusing on memory usage and latency.
  • Automate workflows by building efficient pipelines and orchestration frameworks.
  • Develop tools and shared libraries to boost team productivity.
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