Senior Machine Learning Engineer, Platform

block; United States; Remote; USD 140,000 - 210,000; Full time

Benefits offered by this job

Remote work

Flexible time off

Medical coverage

Retirement savings

Family planning benefits

Job summary

block is seeking an experienced Platform Engineer to design and operate shared ML infrastructure that helps teams build, evaluate, deploy, and monitor models and automated decision systems. You will contribute to a hybrid cloud platform that enables scalable ML workflows and reliable production systems.

You will partner with applied ML teams, own critical components, mentor engineers, and help shape the roadmap for feature stores, inference services, and training pipelines while balancing

Qualifications

• At least eight years of software development or machine learning experience, with a focus on platforms or infrastructure.
• Practical experience building and operating ML infrastructure, with depth in one or more areas such as feature stores, inference, rules engines, or training systems.
• Strong understanding of the ML lifecycle, including data collection, training, evaluation, MLOps, and production deployment for classical ML and newer model types.
• Experience designing and operating large-scale distributed systems, including performance, capacity, data correctness, and failure management.
• Background operating mission-critical services, including on-call response and reliability improvements.
• Ability to lead complex technical work from an ambiguous problem through design, collaboration, delivery, and evaluation.
• Experience with a major cloud provider and strong communication with technical and nontechnical partners.

Responsibilities

• Design and operate platform components such as feature stores, rules engines, model hosting and inference, training systems, and offline evaluation tools.
• Lead distributed system design and delivery, balancing latency, throughput, data consistency, availability, and operating cost.
• Work with internal ML customers to identify common needs and inform platform priorities and roadmap decisions.
• Improve service reliability through automated testing, monitoring, capacity planning, and safe deployment practices.
• Join on-call rotations, lead incident response and reviews, and address root causes to prevent repeat issues.
• Build self-service interfaces, libraries, tools, and documentation; mentor engineers and guide design reviews.

Skills

Distributed systems

ML platform

MLOps

On-call support

Cross-team collaboration

Tools

Kotlin/Java, Go, Python, TypeScript, gRPC, AWS, Spark-based platforms

Job description

Role overview

Build and operate shared machine learning infrastructure that helps teams develop, evaluate, deploy, and monitor models and automated decision systems. The role combines platform engineering, large-scale distributed systems, and close partnership with applied ML teams to turn recurring needs into reliable, reusable capabilities.

Responsibilities

• Design and operate platform components such as feature stores, rules engines, model hosting and inference, training systems, and offline evaluation tools.
• Lead distributed system design and delivery, balancing latency, throughput, data consistency, availability, and operating cost.
• Work with internal ML customers to identify common needs and inform platform priorities and roadmap decisions.
• Improve service reliability through automated testing, monitoring, capacity planning, and safe deployment practices.
• Join on-call rotations, lead incident response and reviews, and address root causes to prevent repeat issues.
• Build self-service interfaces, libraries, tools, and documentation; mentor engineers and guide design reviews.

Requirements

• At least eight years of software development or machine learning experience, with a focus on platforms or infrastructure.
• Practical experience building and operating ML infrastructure, with depth in one or more areas such as feature stores, inference, rules engines, or training systems.
• Strong understanding of the ML lifecycle, including data collection, training, evaluation, MLOps, and production deployment for classical ML and newer model types.
• Experience designing and operating large-scale distributed systems, including performance, capacity, data correctness, and failure management.
• Background operating mission-critical services, including on-call response and reliability improvements.
• Ability to lead complex technical work from an ambiguous problem through design, collaboration, delivery, and evaluation.
• Experience with a major cloud provider and strong communication with technical and nontechnical partners.

Nice to have

• Familiarity with technologies such as Kotlin or Java, Go, Python, TypeScript, gRPC, AWS, Spark-based platforms, model serving, or accelerated inference systems.

Benefits and work setup

• Remote work, flexible time off, medical coverage, retirement savings, and family planning benefits are mentioned in the source.