Staff Machine Learning Engineer
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.
About the job
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).
Skills
Python, SQL, Kafka, Flink, Pub/Sub, MLflow, Kubeflow, Databricks, SageMaker, GCP, BigQuery, GKE, Vertex Ai, Go, Kubernetes
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