Senior Software Engineer, AI/ML

Posted 7 Days Ago
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San Francisco, CA
Senior level
Software
The Role
Design and implement AI/ML features, focusing on large language models and RAG systems while collaborating across teams to optimize performance and scalability.
Summary Generated by Built In

About Glue


Glue is a well-funded startup working on the next generation of work communication tools. We believe that today’s work chat is noisy, unstructured, and not designed for productivity. We’re drawing from decades of communication innovation in structured notes, email, groups, and real-time messaging to create a tool that gets out of the way and lets teams exchange information and make decisions in a more natural way.


Opportunity


We’re looking for an AI/ML Engineer with a strong foundation in large language models (LLMs) and retrieval-augmented generation (RAG) to help us build smart, useful, and scalable AI features. You’ll work across the stack—from prototyping prompts and pipelines to shipping production systems—and play a key role in evaluating model performance and behavior. You'll collaborate across engineering, product, and design to ship experiences that feel magical—but are grounded in robust infrastructure and measurable outcomes.

About You

  • Have shipped across the stack to production—from data ingestion and model integration to serving and monitoring—and understand what it takes to build resilient ML-powered features.
  • Have dealt with issues like latency, cost, edge cases, and prompt brittleness, and you’ve built systems that people rely on
  • Understand the nuances of RAG systems, including text chunking, hybrid retrieval (semantic + keyword), vector store tuning, and relevance optimization.
  • A self-starter, a builder, a doer. You know how to architect a project the “right way”, but also know when to ask about requirements and make tradeoffs depending on the maturity of the project
  • Care about details but know when to avoid getting lost in the weeds and prioritize the immediate outcomes needed.
  • Want to be part of a scrappy, early team who is building something ambitious and exciting.

What will help you succeed

  • 5+ years of engineering experience, ideally including ML, NLP, or applied AI experience
  • 2+ years hands-on with LLMs, transformers, and production-grade generative AI systems
  • 2+ years of experience with a server-side language such as Go, TypeScript, Python, etc.
  • Experience building RAG systems using frameworks like LangChain, LlamaIndex, or custom stacks
  • Experience with multi-modal inputs, fine-tuning open models, or orchestrating agent-style systems
  • Familiarity with evaluation techniques, tooling (e.g., Braintrust, Promptfoo, etc.) and quality metrics
  • Independent and self motivated—maintaining side projects or libraries a major plus

Benefits


We offer a competitive salary in addition to significant equity, a generous healthcare package, and whatever equipment you need to excel at your job.

Flexible vacation policy—we’re moving quickly but want a sustainable culture.

This role is based in our San Francisco headquarters.


Glue is committed to providing equal employment opportunities for all applicants and employees. Glue doesn’t discriminate on the basis of any protected characteristic, including race, color, ancestry, national origin, religion, creed, age, disability, sex, gender, sexual orientation, gender identity, gender expression, medical condition, genetic information, family care or medical leave status, marital status, domestic partner status, military and veteran status, or any other characteristic protected by US federal, state or local laws, or the laws of the country or jurisdiction where you work.

Top Skills

Go
Langchain
Large Language Models
Llamaindex
Python
Retrieval-Augmented Generation
Typescript
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The Company
San Francisco, California
20 Employees
On-site Workplace
Year Founded: 2021

What We Do

We believe it should be easy to share the right information with the right people, both inside and outside of your team. Say goodbye to workspace switching, channel fatigue, noisy notifications, and complex setup to connect with colleagues

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