ML Infrastructure Engineer

March 19

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Applied Intuition

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Software solutions to safely develop, test, and deploy autonomous systems at scale

201 - 500

Description

• Experience with building software components to address production, full-stack machine learning challenges. This is not purely a research problem. • Opinions about building a company-wide platform for ML training, evaluation, and deployment. • Knowledge of the open source landscape with judgment on when to choose open source versus build in-house. • Excellent analytical and problem-solving skills. • Nice to have: Experience working with cloud data processing technologies (Apache Spark, ElasticSearch, Presto, SQL, etc.) • Nice to have: Experience with developing, running, and managing orchestration systems like Airflow and Flyte that non engineers can use to build data pipelines. • Nice to have: Experience with ML modeling frameworks (PyTorch, Tensorflow, etc.), and model serving platforms (TorchServe, TensorFlow Serving, NVIDIA Triton inference server, etc.)

Requirements

The salary range for this position is $65,000 USD to $400,000 USD annually. This salary range is an estimate, and the actual salary may vary based on the Company's compensation practices. • Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles. • Applicants will be required to be fully vaccinated against COVID-19 upon commencing employment. Reasonable accommodations will be considered on a case-by-case basis for exemptions to this requirement in accordance with applicable federal and state law. • Applied Intuition is an equal opportunity employer and federal contractor or subcontractor.

Benefits

• Design and implement distributed cloud GPU training approaches for deep learning model training and evaluation. • Build end-to-end machine learning pipelines and integrate them into core product workflows. • Encourage change, especially in support of ML engineering best practices, and maintain a high standard of excellence. • Collaborate with engineers across the entire company to solve complex data problems at scale.