Senior Machine Learning Engineer, Platform

New

BlockML infrastructure

Location: Bay Area, CA, United States of America; Position open to remote: TrueFull-TimeSenior

Salary228,700 - 343,100 USD per year

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Job Details

Experience: 8+ years of experience in software development and/or machine learning experience with a focus on internal platforms or infrastructure.
Required Skills: PythonCloud ComputingJavaKotlinGoMLOpsDistributed Systems

Have 8+ years of software development and/or machine learning experience focused on internal platforms or infrastructure.
Have hands-on experience building and operating ML infrastructure, with depth in one or more areas such as feature stores, rules engines, model hosting and inference, or training infrastructure.
Understand architectures for ML-backed solutions and the ML lifecycle, including data gathering, training, model evaluation, MLOps, and productionizing classical ML, deep learning, and LLM models.
Have experience designing and operating large-scale distributed systems, including failure modes, data correctness, performance, and capacity.
Have experience operating mission-critical services, including on-call, incident response, and reliability improvements.
Have led complex technical work from problem definition and testing ideas through design, planning with other teams, delivery, and results assessment.
Have experience working with platform users, explaining technical choices and trade-offs, and mentoring engineers.
Have experience using a major cloud vendor for high-scale production use cases.
Communicate effectively with technical and non-technical stakeholders.

Design, build, and operate shared ML infrastructure, including feature stores, rules engines, model hosting and inference, training infrastructure, and offline model evaluation.
Lead the design and delivery of large-scale distributed systems, balancing response time, throughput, data consistency, availability, and cost.
Work with applied machine learning teams and internal customers to identify common needs and turn them into reusable platform capabilities.
Use customer feedback to help set the platform roadmap and priorities.
Build reliable services through automated testing, monitoring, capacity planning, and safe deployments.
Participate in the on-call rotation, lead incident response and reviews, and address underlying causes of incidents.
Build self-service interfaces, libraries, tools, and documentation for developing, productionizing, and monitoring models.
Mentor engineers, guide design reviews, and share practices for maintaining reliable systems.

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