Senior Machine Learning Engineer

Who you are

• 5+ years of direct experience as a Machine Learning Engineer, ML Platform Engineer, MLOps Engineer, Applied Scientist or similar role building and operating production ML systems
• Strongly preferred: degree in STEM field such as Computer Science, Statistics, Economics, Mathematics, Engineering, Physics, Operations Research, or a related quantitative field
• Demonstrated ability to apply critical thinking, abstract reasoning, and sound engineering judgment to complex, ambiguous technical and business problems
• Strong expertise deploying, serving, monitoring, and operating ML models in production—including feature engineering systems, training/serving parity, retraining, and model performance diagnostics
• Experience building low-latency online model serving (e.g., gRPC/microservices, ideally with a service mesh such as Istio) for real-time decisioning
• Hands-on MLOps experience: pipelines, CI/CD for ML, containerization (Docker), orchestration (Kubernetes), infrastructure-as-code (e.g., Terraform), and workflow schedulers (e.g., Airflow)
• Experience with model versioning, reproducibility, and safe progressive rollout (shadow, canary, champion-challenger) of models in production
• Demonstrated fluency directing AI coding agents (e.g., Claude Code, Cursor, or similar) to build, operate, and debug real ML systems—with experienced judgment on verifying their work
• A track record of replacing manual, repetitive ML workflows with durable automation
• Experience building the infrastructure behind high-impact applied ML use cases such as credit decisioning, risk modeling, fraud, churn, or consumer behavior modeling
• Familiarity with model explainability, auditability, and the compliance considerations of regulated decisioning (a plus for credit/fintech contexts)
• Strong software and platform engineering fundamentals
• Strong Python coding skills, with the ability to build pipelines, services, and production-quality tooling from scratch; experience with a systems or backend language such as Go or Rust is a plus
• Experience with distributed computing and GPU-accelerated workloads (e.g., Spark, Ray, Dask, or distributed training/inference), including scaling data and model pipelines across clusters
• Strong SQL skills and experience with cloud data warehouses and operational databases (e.g., BigQuery, Spanner), including working with large, messy, real-world datasets
• Experience with observability tools such as Prometheus, Grafana, or Datadog
• Experience with cloud computing services or platforms; GCP preferred
• Willingness to roll up your sleeves and wear multiple hats across engineering, infrastructure, and ML execution based on business needs
• Strong written and verbal communication skills, including the ability to explain technical topics to both technical and non-technical audiences

What the job involves

• Build, deploy, and operate the production ML systems at the core of our EWA product, with a focus on reliability, performance, and fast, high-quality execution
• Build and improve the pipelines and serving infrastructure behind our predictive models across consumer decisioning, fraud, churn, transaction intelligence, and other business-critical use cases
• Own the production side of the model lifecycle: feature pipelines, deployment, CI/CD, monitoring, and automated retraining
• Build and maintain reusable modeling pipelines, feature engineering systems, model-serving infrastructure, and production-quality code, deployed via Terraform and CI/CD into our GCP + Kubernetes environment
• Instrument models and pipelines with monitoring, alerting, and automated retraining—defining the metrics and dashboards (e.g., Prometheus/Grafana) that surface drift and degradation and give us confidence to ship
• Direct AI coding agents as a force multiplier to write, test, and ship infrastructure and pipeline code—and apply strong judgment about when to trust their output and when to verify it yourself
• Automate manual, repetitive steps in the ML lifecycle so the team can move faster without sacrificing reliability
• Partner with data scientists to give them fast, safe paths to deploy, iterate on, and retrain models in production
• Collaborate with analysts, platform engineers, product managers, and business stakeholders to deliver ML systems with quality, efficiency, and precision
• Identify new areas where platform improvements, automation, and MLOps tooling can improve product velocity and business outcomes