Senior Machine Learning Platform Engineer

PrizePicks

Senior ML Platform Engineer building and optimizing ML systems at PrizePicks. Drive real-time decisions across the sports betting platform through advanced machine learning solutions and infrastructure.

Posted 4/27/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $160,000 - $210,000 per yearWebsite

Tech Stack

Tools & technologies

DockerElasticSearchGoKafkaKubernetesPythonRedisRust

About the role

Key responsibilities & impact

Build Scalable ML Systems: Design and build the end-to-end machine learning infrastructure, setup platform for transitioning experimental Data Science models into robust, high-availability production services.
Real-Time Inference at Scale: Build automation for deploying low-latency services to serve model inferences in milliseconds. You will power real-time decisions across the platform, from dynamic oddsmaking and risk analysis to smart deposit defaults.
Feature Engineering & Data Strategy: You will lead the creation and optimization of a centralized feature store required to train complex models across diverse business domains.
End-to-End MLOps: You will work with the Infrastructure team to build and operate core ML platform components for training and experimentation enablement considering developer experience. You will champion best practices for model deployment, monitoring, and CI/CD for ML. You will implement automated retraining pipelines and observability for ML systems to ensure data drift and model degradation are caught and addressed instantly.

Requirements

What you’ll need

5+ years of experience in Platform Engineering, with a proven track record of deploying and maintaining a scalable ML platform in high-traffic production environments.
2+ years of experience owning ML systems end-to-end in production, including on-call and incident response.
Experience with Real-Time Data, proficient in streaming architectures (Kafka/Flink/PubSub) and building low-latency services to serve model inference in <100ms.
MLOps Expertise, deep experience building a platform for managing the full ML lifecycle (training, deploying, monitoring) using tools like SageMaker, VertexAI, Vector DBs, Graph Databases. Managing and scaling caches like Redis or Elasticsearch.
Proficient with Containerization, Docker, Kubernetes, and cluster-level management.
Expert in Python, proficiency in Go. C++, or Rust is a strong plus for building high-performance inference layers.

Benefits

Comp & perks

Company-subsidized medical, dental, & vision plans
401(k) plan with company match
Annual bonus
Flexible PTO to encourage a healthy work/life balance (2 weeks STRONGLY encouraged!)
Generous paid leave programs, including 16-week paid parental leave and disability benefits
Workplace flexibility and modern work schedules focused on getting the job done, not hours clocked
Company-wide in-person events and team outings
Lifestyle enhancement program
Company equipment provided (Windows & Mac options)
Annual performance reviews with opportunities for growth and career development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learning infrastructurereal-time inferencefeature engineeringMLOpsstreaming architectureslow-latency servicescontainerizationPythonGoC++

Soft Skills

leadershipcommunicationproblem-solvingcollaborationincident response