> Markdown version of [/jobs/ext/464189-machine-learning-platform-engineer](https://www.wearedevelopers.com/jobs/ext/464189-machine-learning-platform-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Platform Engineer

- **Company:** Prizepicks Llc
- **Location:** Boston, MA, United States (Remote available)
- **Salary:** $135,000.0 - $160,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Systems Engineering, Build Automation, C++ (Programming Language), Continuous Integration, Elasticsearch, Experimental Data, Graph Database, High-Frequency Trading, Python (Programming Language), Machine Learning, Redis, Azure Machine Learning, Software Engineering, Feature Engineering, Data Strategy, Build Management, Containerization, Kubernetes, Apache Flink, Real Time Data, Apache Kafka, Machine Learning Operations, Docker
- **Published:** June 7, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=8a415125d66770ec

## About the Role

Do you have experience in Systems engineering?, * 3+ years of experience in Platform Engineering, with a proven track record of deploying and maintaining a scalable ML platform in high-traffic production environments.

 * 1+ 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., * Experience implementing infrastructure while enforcing best practices for the deployment of ML Platform.
 * Background in Daily Fantasy Sports (DFS), oddsmaking, or high-frequency trading.
 * Experience building and scaling "Feature Stores" that successfully bridge batch historical data with real-time event streams.
 * Enabling self-service for ML and Data Science teams for model development and deployment.
 * Enabling AI agents and AI coding for faster and iterative software development., You must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.

## Description

As a ML Platform Engineer, you will contribute to building the ML platform at Prizepicks to scale and productionize our core machine learning capabilities. Your work will directly impact key metrics like Time-to-Bet, Deposit Velocity, and Platform Integrity by integrating robust, low-latency ML models across our sports betting and daily fantasy ecosystems., * 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.


## Related Videos

- [Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)](https://www.wearedevelopers.com/videos/1714-reducing-llm-calls-with-vector-search-patterns-raphael-de-lio-redis)
- [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered)
- [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way)
- [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou)
- [Microservices: how to get started with Spring Boot and Kubernetes](https://www.wearedevelopers.com/videos/242-microservices-how-to-get-started-with-spring-boot-and-kubernetes)
- [Accelerating Authentication Architecture: Taking Passwordless to the Next Level](https://www.wearedevelopers.com/videos/733-accelerating-authentication-architecture-taking-passwordless-to-the-next-level)

## Related Articles

- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)