Staff Engineer-Machine Learning

Key Responsibilities

• Design, develop, and maintain AI‑native software platforms that orchestrate and evaluate agent‑based workflows.
• Drive software architecture and design decisions to build scalable, modular systems with clear abstractions, robust interfaces, and sustainable engineering practices.
• Architect automation and evaluation frameworks to validate correctness, robustness, performance, cost, and stability of agentic systems.
• Build and own benchmarking pipelines that enable repeatable, comparable evaluation across models, agents, configurations, and releases.
• Apply strong engineering judgment around agentic system trade‑offs, including non‑determinism, feedback loops, tool‑invocation failures, and error propagation.
• Establish best practices for production‑grade AI software, including modular design, observability, fault isolation, and reproducibility.
• Collaborate with software, ML, systems, and infrastructure teams to improve reliability and testability.
• Drive technical direction, engineering rigor, and code quality through design and code reviews.
• Mentor engineers and influence architecture through technical leadership.

Required Skills & Experience

• 8+ years of professional software engineering experience with ownership of complex, multi‑component systems.
• Strong proficiency in Python and experience building large, maintainable codebases and frameworks.
• Hands‑on experience designing automation, evaluation, or testing frameworks for complex systems.
• Strong understanding of agentic system architectures, common failure modes, and operational pitfalls.
• Experience building CI/CD pipelines with automated quality, evaluation, and release gates.
• Solid grasp of distributed systems or platform‑scale software engineering concepts.
• Ability to reason about non‑deterministic systems and design reliable evaluation strategies.
• Strong debugging skills and ability to collaborate across teams.

Preferred Qualifications

• Experience working with AI agents, orchestration frameworks, or multi‑step AI workflows.
• Exposure to benchmarking platforms, leaderboards, or evaluation harnesses.
• Familiarity with MLOps and observability platforms
• Familiarity with building scalable data pipelines
• Experience with HPC, heterogeneous compute, or large‑scale execution environments.
  • MS or PhD in Computer Science, Electrical Engineering, or related field.