Staff ML Software Engineer (L6) — Platform Systems, AIMS Engineering

Accountabilities:

• • Design, build, and operate platform subsystems for observability, evaluation, tooling, and operational automation supporting next-generation ML architectures.
  • Prove new platform capabilities against current production operations, including anomaly detection, root-cause analysis, and automated operational workflows.
  • Build observability infrastructure that provides deep visibility into model behavior, training pipeline health, serving latency, and data quality, enabling teams to detect and diagnose issues before they become incidents.
  • Identify and drive infrastructure cost optimization across ML training and serving, developing increasingly automated frameworks and tools that make compute efficiency a core engineering priority.
  • Architect reliability improvements across the AI/ML stack, reducing operational toil, improving on-call experiences, and establishing strong standards for production excellence.
  • Contribute to the target architecture and migration strategy for a modernized ML platform, coordinating with partner engineering teams and managing technical dependencies.
  • Evaluate emerging infrastructure approaches, model paradigms, and platform capabilities, translating relevant developments into a forward-looking technical roadmap.
  • Develop reusable frameworks and common platform patterns that can be adopted across engineering teams and ML workloads.
  • Lead complex technical programs across organizational boundaries, building alignment and consensus while influencing teams without direct authority.
  • Continuously improve critical systems by balancing long-term platform investments with immediate operational priorities.

  Requirements:

  • Significant experience designing, building, and operating production AI/ML systems at scale, including ML training pipelines and model-serving or online-inference environments.
  • Hands-on experience building infrastructure for advanced agentic or complex model architectures, such as memory, trace, evaluation, replay, orchestration, or routing systems.
  • Strong software engineering fundamentals with deep expertise in Python and working proficiency in at least one JVM language, such as Java or Scala.
  • Proven experience improving the reliability, scalability, operational maturity, and cost efficiency of AI/ML infrastructure.
  • Strong background building observability and monitoring systems for ML workloads, with an understanding of visibility requirements across training, serving, and data pipelines.
  • Deep distributed-systems expertise, including large-scale batch processing and real-time serving infrastructure.
  • Experience collaborating with multiple engineering and partner teams to establish technical direction, manage dependencies, and deliver complex programs.
  • Excellent technical judgment and the ability to identify reusable patterns, determine where investment is valuable, and make pragmatic decisions about scope and priorities.
  • Ability to operate effectively in ambiguous environments, independently defining problems, developing approaches, and adapting as new information emerges.
  • Preferred experience with LLM evaluation, trace, replay, observability, or debugging tools and frameworks for complex model systems.
  • Familiarity with modern ML infrastructure such as feature stores, model-serving platforms, and experimentation frameworks.
  • Experience migrating production AI/ML systems between technology generations and improving their architecture over time.
  • Experience in personalization domains such as recommendation systems, search, or discovery is a plus.

  Benefits:

  • Annual salary range of $600,000–$1,066,000, with compensation determined based on factors such as role, location, background, skills, and experience.
  • Annual compensation structure consisting of salary and stock options, with employees able to determine their preferred mix each year.
  • Comprehensive health plans and mental health support.
  • 401(k) retirement plan with employer matching.
  • Stock option program.
  • Health Savings Accounts and Flexible Spending Accounts.
  • Disability programs and life and serious injury benefits.
  • Family-forming benefits.
  • Paid leave programs.
  • Flexible time off for full-time salaried employees.
  • Fully remote position within the United States.
  • Opportunity to work on highly scalable AI/ML systems with significant technical and organizational impact.