Machine Learning Engineer, Assistant Quality
Glean
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This role on the market
1,093 open learning roles across 232 companies are on ApplySarthi right now, most of them in Bengaluru (62), Hyderabad (25), Delhi NCR (14).
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What learning roles keep asking for: Machine learning (49%), Python (37%), LLMs (23%), PyTorch (23%), Deep learning (17%), AWS (14%), Generative AI (13%) — counted across their open postings here.
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Glean has 131 open roles listed here.
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Counted across 14 company job boards, updated as roles open and close.
Preparing for this interview
Interviews for learning roles keep coming back to Machine learning, Python, LLMs, PyTorch. Practise those questions before you sit with Glean.
Questions you are likely to be asked
- Why do you want to join Glean?
- What is your experience with LLMs? Tell me one thing you learned the hard way.
- Tell me about a time the data was messy or wrong. What did you do?
- How would you explain your model's result to someone who is not technical?
- What would you check first if a model's accuracy dropped after going live?
Prep Sarthi gives you a free mock interview: an AI interviewer asks you questions like these out loud, from your own CV and this job, and shows your score and your weakest answer.
Practise the Machine Learning Engineer, Assistant Quality at Glean interview free →You will work on applied problems across agent quality, evaluation, personalization, retrieval, and orchestration. The ideal person is excited by shipping production systems, not pure research, and wants to help shape how Glean’s assistant gets better over time through stronger signals, tighter feedback loops, and better end-to-end execution quality.
- Build and improve ML and LLM-powered systems that raise the quality of Glean’s AI Assistant and autonomous agents across real user workflows.
- Design evaluation, benchmarking, and monitoring loops to measure assistant quality, model quality, and end-to-end system performance.
- Develop and iterate on signals, prompts, workflows, and model-driven logic that improve reasoning, planning, personalization, and task completion quality.
- Work across areas such as RAG, semantic search, recommendation-style systems, post-training or reinforcement learning, and agent orchestration where they materially improve product outcomes.
- Partner closely with product, design, and engineering teammates to understand customer pain points and ship high-quality production systems quickly.
- Contribute to the data and ML infrastructure needed to support robust experimentation, offline and online evaluation, and continuous model improvement.
- 2+ years of industry experience in machine learning, applied AI, or software engineering with significant ML ownership.
- Strong hands-on coding ability and a track record of shipping production systems, not just prototypes or research projects.
- Experience in one or more of the following areas: LLM applications, NLP, search, retrieval, recommendations, evaluation frameworks, agent systems, or personalization.
- Comfort working across both modeling and product engineering details, including experimentation, quality measurement, and production iteration.
- Proficiency in common ML tooling and strong software engineering fundamentals in languages such as Python, Go, Java, or C++.
- A pragmatic, product-minded approach. You know when to use sophisticated ML techniques and when simple, reliable systems are the better answer.
- A proactive, low-ego working style and excitement about learning quickly in a high-velocity environment.
- This role is hybrid (4 days a week in our San Francisco office)
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Listed on greenhouse · posted 2026-08-03. ApplySarthi collects openings and links to application pages; the role is advertised by Glean, not by us.