Mercury
Senior Machine Learning Operations Engineer
Rol remoto de Machine Learning Operations con fit claro de ubicación del candidato.
Publicado21 jun 2026
Países elegibles1 país aceptado
Señal de senioritySenior
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
Canadá
Resumen del rol
Senior Machine Learning Operations Engineer
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
As part of this role, you will:
- Build and operate the real-time inference service that scores models for the risk decision engine, with low latency and high availability as first-class requirements
- Own model deployment infrastructure — registry and versioning, CI/CD with performance, bias, and consistency checks, shadow mode, and staged rollouts
- Build model observability: availability, latency, and error monitoring, plus drift detection as a retraining trigger
- Partner with Risk Data Science to take models from a clean development-to-production handoff through to production operation under MLP ownership
- Implement experimentation capabilities such as champion/challenger and canary routing, and explainability outputs like SHAP attributions
- Feel a strong sense of product ownership and actively seek responsibility — we self-organize on small and medium projects, and we want someone excited to help shape and build a brand-new platform team
The ideal candidate for the role has:
- 5+ years in machine learning engineering, backend software engineering, MLOps, or a closely related field
- Production ML service experience — deploying, serving, and operating models in low-latency, high-availability contexts
- Strong backend engineering fundamentals in Python, with API frameworks like FastAPI or Flask
- Experience with model deployment and lifecycle tooling: model registries, CI/CD for models, versioning, and staged rollout patterns (shadow, canary, champion/challenger)
- Experience building observability and alerting for production services — latency, errors, and ideally model-specific signals like drift
- Comfort with the data layer ML depends on: SQL, key-value/low-latency stores (Redis, DynamoDB, or equivalent), and streaming pipelines (Kafka, Kinesis, Redpanda, or equivalent)
Nice to have:
- Familiarity with a modern data stack (Snowflake, dbt, Dagster, Airflow, or similar)
- Experience operating in a regulated, audit-sensitive, or compliance-adjacent environment
- Exposure to functional languages or willingness to work across a stack that includes Haskell, React, and TypeScript
Nice to have:
- US employees (any location): $166,600 - $208,300
- Canadian employees (any location): CAD 157,400 - 196,800
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