Senior AI Engineer — Quality & Agent Systems (Hybrid)

Glean; Mountain View (CA); Hybrid; USD 200,000 - 280,000; Full time

Benefits offered by this job

Home office stipend

Education stipend

Wellness stipend

Lunch provided

Job summary

Glean in Mountain View/San Francisco is hiring a Senior AI/ML Engineer to advance our agentic AI stack. You’ll work at the intersection of research, quality, and product engineering to ship reliable, measurable improvements across models and the agent harness.

You’ll own eval design, post-training fine-tuning, and RL environments, collaborating with product and customers to turn eval insights into better enterprise outcomes.

Qualifications

• 5+ years of industry experience in AI or ML engineering.
• Hands-on post-training LLMs experience including adapting open-weight models.
• Experience building evals for AI assistants and agent systems.
• Proficient in Python or Go and ML frameworks; track record shipping production ML.
• Product-minded with ability to turn problems into experiments.

Responsibilities

• Use eval results and production traces to improve the stack, balancing quality, latency, and cost.
• Work on post-training models with SFT and preference-based optimization, including data generation.
• Build RL environments for enterprise workflows and verifiers for hill-climbing and benchmarking.
• Improve evalsets, LLM judges and verifiers, calibrating them to human judgments for releases.
• Collaborate with product, platform, and research teams to land quality improvements in production.

Skills

5+ years AI/ML engineering

LLM post-training experience

evals for AI assistants

Python and/or Go

strong ML framework

production ML systems

product-minded

Job description

Glean in Mountain View/San Francisco is hiring a Senior AI/ML Engineer to advance our agentic AI stack. You’ll work at the intersection of research, quality, and product engineering to ship reliable, measurable improvements across models and the agent harness.

You’ll own eval design, post-training fine-tuning, and RL environments, collaborating with product and customers to turn eval insights into better enterprise outcomes.