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Sr. Machine Learning – Infrastructure Engineer
| Company | Peloton |
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| Location | New York, NY, USA |
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| Salary | $200740 – $271000 |
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| Type | Full-Time |
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| Degrees | Bachelor’s |
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| Experience Level | Senior |
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Requirements
- B.S. Degree in Computer Science/Engineering and 5+ years of experience in Software Engineering.
- Strong understanding of software engineering principles and fundamentals including data structures and algorithms.
- Experience writing code in Python, Java, Kotlin, Go, C/C++ with documentation for reproducibility.
- Strong experience deploying and managing services on AWS, GCP, or Azure.
- Experience with message queues (Kafka, RabbitMQ, SQS) to handle real-time event-driven architectures.
- Proficiency in debugging, profiling, and tuning backend services (e.g., using flame graphs, p99 latency tracking).
- Proficiency with relational and non-relational databases such as Postgres, MySQL, DynamoDB, Redis.
- Experience with designing RESTful API.
- Experience working with gRPC and designing microservices from ground up.
- Experience with concurrency and parallelism in Python and/or Java.
Responsibilities
- Design and build Python microservices that power Peloton’s content recommendations.
- Work closely with ML engineers to productionize models efficiently, ensuring seamless integration with backend services.
- Productionize, deploy and monitor Personalization/machine learning services.
- Ensure high availability and performance of backend services through load balancing, caching strategies, and auto-scaling techniques.
- Assume technical responsibility for Personalization services, and work with engineers to scale and improve testability of our production systems.
- Conduct capacity planning and performance tuning to support high-traffic personalization workloads.
- Collaborate and work closely with our platform teams to leverage their tools and infrastructure to rapidly iterate on ideas that drive delightful personalized experiences for millions of users.
Preferred Qualifications
- Experience/Interest working in at least one of following ML disciplines: recommender systems, natural language processing or computer vision.
- Experience with Redis, Memcached, and CDN strategies to optimize recommendation response times.
- Experience analyzing cloud costs and optimizing infrastructure spend without sacrificing reliability.
- Experience with building real-time ML applications with NVIDIA Triton/TorchServe.
- Experience working with Graph Databases (Neo4j).
- Have experience in building secure consumer facing APIs.
- Have experience in leading backend development and engineering.
- Experience writing and speaking about technical concepts to business, technical, and lay audiences and giving data-driven presentations.
- Experience working with docker, orchestration platforms like kubernetes, CI/CD workflows like GHA and other IaC tools like Terraform, cloud formation etc.
- Experience with ML Monitoring and Observability tools like Datadog, MLFlow, Grafana etc.
