Staff Machine Learning Engineer, ML Platform

Braze
Chicago
Workplace: HybridFull timeUSD 184,000 - 314,000 annuallyFunction: Data Science & Machine LearningExperience: 8+ yearsSkills: ["Technical leadership","Communication","Incident response","Reliability focus","Mentorship"]

Own the ML platform that powers personalized customer experiences at global scale, from distributed training pipelines to high-throughput prediction APIs. Drive complex production infrastructure initiatives—queueing/orchestration, deployment and identity, and multi-region model serving—while improving reliability, incident response, and cost. Lead cross-team initiatives, raise engineering quality through reviews and production readiness, mentor senior engineers and data scientists, and connect technical decisions to measurable customer and business outcomes.

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FursaFursa
Braze
Braze
4 days ago

Staff Machine Learning Engineer, ML Platform

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Last checked: 2 hours agoStatus: Live

Job Summary

Own the ML platform that powers personalized customer experiences at global scale, from distributed training pipelines to high-throughput prediction APIs. Drive complex production infrastructure initiatives—queueing/orchestration, deployment and identity, and multi-region model serving—while improving reliability, incident response, and cost. Lead cross-team initiatives, raise engineering quality through reviews and production readiness, mentor senior engineers and data scientists, and connect technical decisions to measurable customer and business outcomes.
Location: Chicago
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Identify and drive initiatives that improve how ML runs in production, including replatforming queueing/orchestration, deployment and cloud identity, and retiring infrastructure.
  • •Design, build, and ship ML platform infrastructure initiatives end-to-end, including multi-region model serving and CI/deployment tooling.
  • •Set the technical vision and quality bar for how models are trained, deployed, served, and observed; lead incident response and reliability/cost improvements.
  • •Execute initiatives spanning teams that rely on shared infrastructure, deployment tooling, and data systems owned with partner teams.
  • •Improve engineering quality through design/code reviews and production readiness, and mentor other senior engineers and data scientists.

Pay and Benefits

Salary: USD 184,000 - 314,000 annually
Equity and Bonus:Equity
Perks:Health InsuranceDentalVisionLife InsuranceDisabilityPaid LeaveLearning BudgetEquityHybrid Work

Key Requirements

  • •8+ years building and operating distributed systems in production with depth in deployment and operations.
  • •Hands-on experience with ML workloads in production, including training pipelines, model serving, feature systems, or ML platform tooling.
  • •Experience owning CI/CD and infrastructure as code, and running services under production load for scalability and reliability.
  • •Technical leadership: owned direction for a team and led multi-quarter initiatives across team boundaries while maintaining high personal output.
  • •Deep working knowledge of Kubernetes and cloud infrastructure, including identity/access management, networking, and cost profiling.
Experience:8+ yearsMachine learningCloud infrastructureDistributed systems
Skills:Technical leadershipCommunicationIncident responseReliability focusMentorship
Languages:English
Tech Stack:PythonRuby on RailsMongoDBRedisKubernetesCeleryRabbitMQKafkaRayMLflow

Company Brief

Braze
Provides a customer engagement platform that helps brands create personalized messaging and lifecycle campaigns across mobile, web, email, and other channels to drive retention, engagement, and revenue.
Industry: Enterprise Software
Company Size: Enterprise (1,001+ employees)
Revenue: USD 100M to 250M
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: New York, United States
Founded: 2011
WebsiteLinkedIn