Example Company
Software engineer
- Location
- San Francisco
- Type
- Full time
- Compensation
- $150k-$200k
- Posted
- 2 days ago
Join a team building useful software. Work closely with engineering and design to turn new ideas into products.
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Research Engineer
- Location
- Remote
- Type
- Full time
- Posted
- 1MO AGO
About the role
Dynamiq builds the platform enterprises use to put AI agents into production: chat, voice and workflow agents for banks, governments, telecom providers and hospitals, deployed in their cloud or data center. The quality of those agents comes down to research work most companies never get right: models fine-tuned for the job, retrieval that actually retrieves the right thing, and conversational systems that hold up under real use.
We are looking for a Research Engineer to do that work. You would fine-tune open-source LLMs, build and improve our Retrieval-Augmented Generation systems, and turn research into features that ship, not papers that sit on a shelf.
What you will do
- Fine-tune open-source LLMs, customizing and optimizing them for our use cases
- Build Retrieval-Augmented Generation systems that make agent responses more accurate and grounded
- Develop the conversational applications that use these models
- Track new developments in LLMs, deep learning and natural language processing, and propose and build approaches worth adopting
- Work closely with product and engineering to get models into production at the scale and reliability we require
What you bring
- A Master's or PhD in Computer Science, Artificial Intelligence, Machine Learning or a related field
- A strong grounding in deep learning and natural language processing
- Hands-on experience building with large language models
- Strong Python skills, including ML and NLP libraries such as TensorFlow, PyTorch or Hugging Face Transformers
- Strong problem solving and a genuine interest in the research
- The ability to explain complex technical ideas to non-technical stakeholders
Good to have: experience building Retrieval-Augmented Generation systems, publications or open-source contributions in machine learning or NLP, and time spent in a fast-moving startup.
How we work
Research here is judged by what reaches production: agents running for regulated customers, with evals to prove the work holds up, not novelty for its own sake. You will work closely with product and engineering rather than in a separate research group cut off from what ships.
How we hire
- Intro call. A conversation about your research and engineering background, and the role.
- Working session. A technical conversation grounded in a real problem from our roadmap.
- Decision. We move quickly and tell you where we stand either way.
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