Architect Scalable ML Systems: Design and build the end-to-end machine learning infrastructure, transitioning experimental Data Science models into robust, high-availability production services.
Real-Time Inference at Scale: Steer the design and deployment of 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: Partner with Data Science to build scalable logging and data pipelines. 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 Leadership: Champion best practices for model deployment, monitoring, and CI/CD for ML. You will implement automated retraining pipelines and observability tools to ensure data drift and model degradation are caught and addressed instantly.

Requirements

7+ years of experience in Machine Learning Engineering or Backend Engineering, with a proven track record of deploying and maintaining complex ML models in high-traffic production environments.
3+ years of technical leadership, acting as a lead and 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 to serve model inference in <100ms.
MLOps Expertise: Deep experience managing the full ML lifecycle (training, deploying, monitoring) using tools like MLFlow, Kubeflow, Databricks, or SageMaker.
Strong Coding Skills: Expert in Python and SQL; proficiency in Go, C++, or Rust is a strong plus for building high-performance inference layers.
Cloud Native: Deep experience with GCP services (BigQuery, Cloud Functions, GKE, Vertex AI) or AWS equivalents.

Benefits

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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

Machine Learning EngineeringBackend EngineeringReal-Time DataMLOpsPythonSQLGoC++RustFeature Engineering

Soft Skills

Technical LeadershipArchitecture Decisions