# Staff Machine Learning Engineer

**Company:** [PrizePicks](https://hotfix.jobs/companies/prizepicks)
**Location:** Remote
**Role:** ML Engineering
**Salary:** $220k – $280k/yr
**Experience:** 7+ years
**Skills:** Python, SQL, Kafka, Flink, Pub/Sub, MLflow, Kubeflow, Databricks, SageMaker, GCP, BigQuery, GKE, Vertex Ai, Go, Kubernetes
**Posted:** 2026-04-16

> Leads development and productionization of scalable ML systems, real-time inference services, feature stores, and MLOps pipelines to enhance betting metrics and platform integrity. Requires 7+ years ML/Backend experience, streaming architectures, and GCP expertise.

## Job Description

## Responsibilities

- Architect scalable ML systems: Design and build end-to-end machine learning infrastructure, transitioning experimental Data Science models into robust, high-availability production services.
- Real-time inference at scale: Design and deploy low-latency services to serve model inferences in milliseconds for dynamic oddsmaking, risk analysis, and smart deposit defaults.
- Feature engineering & data strategy: Partner with Data Science to build scalable logging and data pipelines; lead creation and optimization of a centralized feature store.
- End-to-end MLOps leadership: Champion best practices for model deployment, monitoring, and CI/CD for ML; implement automated retraining pipelines and observability tools.

## Requirements

- 7+ years of experience in Machine Learning Engineering or Backend Engineering, with proven track record of deploying and maintaining complex ML models in high-traffic production environments.
- 3+ years of technical leadership, driving architecture decisions for consumer applications or scalable backend platforms.
- Experience with real-time data: Proficient in streaming architectures (**Kafka**, **Flink**, **PubSub**) and building low-latency services (<100ms inference).
- MLOps expertise: Deep experience managing full ML lifecycle using tools like **MLFlow**, **Kubeflow**, **Databricks**, or **SageMaker**.
- Strong coding skills: Expert in **Python** and **SQL**; proficiency in **Go**, **C++**, or **Rust** a strong plus.
- Cloud native: Deep experience with **GCP** services (**BigQuery**, **Cloud Functions**, **GKE**, **Vertex AI**) or AWS equivalents.

## Nice-to-Haves

- Experience implementing reinforcement learning or complex probabilistic models for dynamic pricing, risk management, or fraud detection.
- Background in Daily Fantasy Sports (DFS), oddsmaking, or high-frequency trading.
- Experience building and scaling feature stores bridging batch historical data with real-time event streams.

## Compensation

- Typical salary range: $220,000 - $280,000 (varies by role, level, location, skills, experience, education).

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**Apply:** https://hotfix.jobs/jobs/c1a6d9d5-8ff4-4dc7-b808-8f5d053430dc
**Canonical:** https://hotfix.jobs/jobs/c1a6d9d5-8ff4-4dc7-b808-8f5d053430dc