Staff ML Engineer — Enterprise AI Systems and Production

Rippling; San Francisco (CA); On-site; USD 198,000 - 330,000; Full time

Job summary

Rippling, based in San Francisco, is building an autonomous operating system for work and enterprise AI. As a Staff Machine Learning Engineer, you will own the end-to-end ML lifecycle—from problem framing to production deployment—and guide ML initiatives across the AI org, ensuring agents are reliable and continually improving.

You will lead research-to-production efforts, design self-improving systems, and collaborate with Product and Platform teams in a deeply hands-on role that shapes

Qualifications

• 8+ years of software engineering experience with 5+ years focused on ML in production.
• Deep expertise in modern ML including LLMs, transformers, RLHF.
• Hands-on with ML frameworks and production ML infra.
• Experience leading cross-functional technical initiatives.

Responsibilities

• Own the end-to-end ML lifecycle for high-impact AI initiatives.
• Design and implement novel ML architectures (LLMs, RAG, reward models).
• Build robust evaluation and experimentation infra; model monitoring and training pipelines.
• Lead research-to-production efforts and drive frontier techniques into production.
• Design self-improving systems with feedback loops and automated retraining pipelines.
• Collaborate closely with Product and Platform teams; mentor engineers.

Skills

Software engineering

ML production

LLMs / transformers

PyTorch / JAX

Cross-functional leadership

Clear communication

Tools

PyTorch

JAX

Job description

Rippling, based in San Francisco, is building an autonomous operating system for work and enterprise AI. As a Staff Machine Learning Engineer, you will own the end-to-end ML lifecycle—from problem framing to production deployment—and guide ML initiatives across the AI org, ensuring agents are reliable and continually improving.

You will lead research-to-production efforts, design self-improving systems, and collaborate with Product and Platform teams in a deeply hands-on role that shapes