Remote ML Platform Engineer: Real-Time Inference

PrizePicks; Atlanta (GA); On-site; USD 155,000 - 185,000; Full time

Benefits offered by this job

Medical, Dental, Vision plans

401(k) with company match

Annual bonus

Flexible PTO

Parental leave

Job summary

PrizePicks is seeking an ML Platform Engineer to build and scale the ML infrastructure that supports real-time inference across our DFS ecosystem. You will work on a production-grade platform that powers dynamic oddsmaking, risk analysis, and smart deposit defaults.

Requirements include 3+ years in platform engineering, 1+ year owning ML systems end-to-end, and strong experience with streaming data, MLOps, and containerized deployments.

Qualifications

• - 3+ years of Platform Engineering experience deploying scalable ML platforms in production.
• - 1+ years owning ML systems end-to-end including on-call/incident response.
• - Real-Time Data experience with streaming architectures and low-latency inference.
• - Proficient with ML lifecycle tools (training, deployment, monitoring) using SageMaker/VertexAI.
• - Experience with containerization and cluster management (Docker, Kubernetes).
• - Python expertise; Go, C++, or Rust is a strong plus for high-performance inference.

Responsibilities

• Design and build end-to-end ML infrastructure and productionize models.
• Deploy low-latency inference services powering decisions across the platform.
• Lead feature store design for training complex models across domains.
• Develop and operate CI/CD, monitoring, and automated retraining pipelines.

Skills

Platform Engineering

ML Platform

MLOps

Real-Time Data

Streaming Architectures

Python

Go

C++/Rust (plus)

Docker

Kubernetes

SageMaker

VertexAI

Vector DBs

Graph DBs

Redis

Elasticsearch

Tools

SageMaker

VertexAI

Vector DBs

Graph DBs

Redis

Elasticsearch

Docker

Kubernetes

Job description

PrizePicks is seeking an ML Platform Engineer to build and scale the ML infrastructure that supports real-time inference across our DFS ecosystem. You will work on a production-grade platform that powers dynamic oddsmaking, risk analysis, and smart deposit defaults.

Requirements include 3+ years in platform engineering, 1+ year owning ML systems end-to-end, and strong experience with streaming data, MLOps, and containerized deployments.