Backend & Applied ML Engineer

About The Role

The team is seeking a creative backend engineer to join the team behind CoCounsel. You will help build applications that interact with state-of-the-art Large Language Models (LLMs) that deliver cutting-edge and time-saving solutions to attorneys all around the country. CoCounsel is already capable of writing legal research memos, summarizing and reviewing documents, and red-lining contracts, just to name a few capabilities--all in minutes, not days or weeks. And we're just getting started. As a Software Engineer, Backend and Applied ML, you will develop the systems that orchestrate our users’ interactions with LLMs, smaller ML models, and other critical services. Working closely with prompt engineers and other subject matter experts in the company, the systems you develop will unlock much of the value presented by LLMs. Our ideal candidate is a dependable backend engineer who is fluent in Python, HTTP APIs, and SQL who also has at least some experience working with and around machine learning systems.

As the Software Engineer, Backend & Applied ML, You Will...

Orchestrate LLM-driven experiences that delight our customers
Write Python API server code that handles requests from a front-end client
(or many front-end clients)
Develop novel LLM client code suitable for a high-volume production environment
Utilize the latest and greatest LLM models and services
Empower prompt engineers and other SMEs by developing intuitive prompting frameworks.
Write SQL queries and occasionally design schema
Participate in and contribute to high level architecture discussions within the Applied ML team and across other teams
About You 
At least three years of production level experience in software engineering on an engineering/product team, preferably at a startup.
Strong Python, HTTP API, and SQL skills are required.
Experience using large language models through code (such as model services from OpenAI, Anthropic, or Cohere; and/or open source models such as Llama and Mistral).
Experience with any stages in the ML development lifecycle (e.g. data labeling, data curation, data cleaning, model training, model evaluation, model serving, LLM prompting, etc)
Exhibits strong software engineering judgment.
Excellent analytical and problem-solving skills.
Ability to work both independently and in a collaborative environment.