Mercury

1 month ago

Senior Machine Learning Operations Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 9 hours agoStatus: Live

Job Summary

Lead the real-time ML inference and deployment platform for risk decisioning in a fintech setting. Build low-latency production services, manage model registries and CI/CD, and drive observability and experimentation to enable safe, scalable ML deployments.

Location: San Francisco, New York

Workplace: Remote

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•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.

Pay and Benefits

Salary: USD 166,600 - 208,300 annually

Equity and Bonus:Equity

Key Requirements

•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.

Experience:5+ yearsFintechRiskMlops

Skills:OwnershipCollaborationCommunicationProblem-solvingDecision-making

Languages:English

Tech Stack:PythonFastAPIFlaskSQLRedisDynamoDBKafkaKinesisRedpandaSnowflakeDbtDagsterAirflowHaskellReactTypeScript

Company Brief

Mercury

Provides banking and financial infrastructure for startups and small businesses, offering business checking accounts, payments, debit cards, venture debt integrations, and treasury services via a developer-friendly platform.

Industry: Fintech Infrastructure

Company Size: Large (251 to 1,000 employees)

Growth: Scaleup

Valuation: Unicorn (USD 1B+)

Funding: Series C

Headquarters: San Francisco, United States

Founded: 2017

Website LinkedIn

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Mercury

1 month ago

Senior Machine Learning Operations Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 9 hours agoStatus: Live

Job Summary

Location: San Francisco, New York

Workplace: Remote

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•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.

Pay and Benefits

Salary: USD 166,600 - 208,300 annually

Equity and Bonus:Equity

Key Requirements

•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.

Experience:5+ yearsFintechRiskMlops

Skills:OwnershipCollaborationCommunicationProblem-solvingDecision-making

Languages:English

Tech Stack:PythonFastAPIFlaskSQLRedisDynamoDBKafkaKinesisRedpandaSnowflakeDbtDagsterAirflowHaskellReactTypeScript

Company Brief

Mercury

Industry: Fintech Infrastructure

Company Size: Large (251 to 1,000 employees)

Growth: Scaleup

Valuation: Unicorn (USD 1B+)

Funding: Series C

Headquarters: San Francisco, United States

Founded: 2017

Website LinkedIn