Machine Learning Platform Engineer

PrizePicks

ML Platform Engineer scaling production machine learning for PrizePicks, a daily fantasy sports platform. Building real-time inference, feature stores, MLOps, and low-latency services for sports betting ecosystems.

Posted 8/4/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSenior💰 $155,000 - $185,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining scalable machine learning platforms, with a strong focus on low-latency inference services and MLOps best practices. Proficient in deploying automated pipelines and monitoring systems to ensure high availability and performance in production environments.

Highest-signal resume keywords

Platform Engineering ExperienceMLOps ExpertiseLow-Latency Inference ServicesContainerization with Docker and KubernetesProficiency in Python and Go

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning InfrastructureModel DeploymentAutomated Retraining PipelinesStreaming ArchitecturesFeature Store ManagementRedis ManagementElasticsearch ManagementPython ProgrammingGo ProgrammingC++ or Rust Proficiency

Tools & Technologies

SageMakerVertex AIKafkaFlinkPub/SubDockerKubernetesVector DatabasesGraph Databases

Industry Keywords

Daily Fantasy SportsOddsmakingHigh-Frequency Trading

Tech Stack

Tools & technologies

DockerElasticSearchGoKafkaKubernetesPythonRedisRust

About the role

Key responsibilities & impact

Design and build end-to-end machine learning infrastructure to transition experimental Data Science models into robust, high-availability production services
Build automation to deploy low-latency inference services serving model predictions in milliseconds
Power real-time decisions for dynamic oddsmaking, risk analysis, and smart deposit defaults
Lead creation and optimization of a centralized feature store for complex models across business domains
Build and operate core ML platform components for training and experimentation with the Infrastructure team
Champion model deployment, monitoring, and ML CI/CD best practices
Implement automated retraining pipelines and observability to detect data drift and model degradation

Requirements

What you’ll need

3+ years of Platform Engineering experience deploying and maintaining scalable ML platforms in high-traffic production environments
1+ years owning ML systems end-to-end in production, including on-call and incident response
Proficiency in streaming architectures such as Kafka, Flink, or Pub/Sub
Experience building low-latency inference services serving models in under 100 milliseconds
Deep MLOps experience across training, deployment, and monitoring
Experience with SageMaker, Vertex AI, vector databases, and graph databases
Experience managing and scaling Redis or Elasticsearch caches
Proficiency with containerization, Docker, Kubernetes, and cluster-level management
Expert Python skills and proficiency in Go
Must be authorized to work for any employer in the U.S.; employment visa sponsorship is unavailable
Experience with C++ or Rust is a strong plus
Background in Daily Fantasy Sports, oddsmaking, or high-frequency trading is a plus
Experience building and scaling feature stores is a plus

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