Machine Learning Engineering, Platform at Equinox in New York, NY

We’re looking for a Machine Learning Engineer (platform) to help us design, build and scale data products to power personalized content discovery and data-driven features for our members. You will be working in a cross-functional team alongside data scientists, analysts, engineers, and product managers to deploy systems that make automated decisions at a massive scale that enable our member base to pursue their fitness journey across multiple platforms (app, web, TV, connected hardware, in-club). Your team has a high degree of autonomy and works on some of the most interesting problems at the intersection of fitness and machine learning. Experience building and scaling ML-based recommendation systems is a major plus.

What you’ll do:

• Lead the architectural design and deployment of end-to-end pipelines to power ML models, algorithms, A/B testing, and other data products at scale
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• Collaborate with data scientists and data engineers to refine and scale recommendation systems to power personalized content discovery
• Partner with data scientist and analysts to build tooling and dashboards to monitor model performance
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• Partner with product managers and data scientists to design and develop new data-driven features across multiple platforms
• Think outside of the current technology/stack limitations to push the boundaries on what is possible and deliver feasible solutions collaboratively
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• Help lead and scale the ML Engineering platform team in a fast paced, high-growth startup

Qualifications

What you’ll need:

• Expertise in leading serverless and containerized deployments (AWS certification preferred)
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• Excellent proficiency in Python, shell scripting, git, and the ability to perform analysis and write complex transformations in SQL
• You have a history of building scalable batch and real-time data pipelines, leveraging messaging technologies
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• (e.g., Snowflake, Spark, Presto, Databricks)
• Experienced with CI/CD and infrastructure as code frameworks (CloudFormation, Terraform, etc)
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• Knowledge of relational databases, big data concepts and distributed computing frameworks
• Familiarity with ML model development frameworks, ML orchestration and pipelines with experience in either Airflow, Kubeflow or MLFlow
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• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn)
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• Familiarity with data science and machine learning techniques (e.g., regression, classification, clustering, time series, etc.)
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• Excellent communication skills with demonstrated experience to influence technical decisions to line up with the company’s strategy
• Excellent critical thinking skills to help transform data into products and insights
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• Bonus: Experience building recommendation systems is a major plus
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• Bonus: An interest in the fitness industry and biometric data