Full Stack Engineer, Machine Learning
At Continua, we've built an AI agent that users can invite into group conversations to make planning, coordination, and information retrieval effortless. It's early days for this specific Continua product, but we think we're on to something.
We've seen rapid growth over the last quarter, and we’re looking for an engineer who can move fluently between machine learning and full‑stack development, who can bring new ideas to life from model to interface.
This role is based in our New York City office (on‑site 3+ days per week).
What You’ll Do:
Design, fine‑tune, and optimize LLM‑based conversational models to enhance agent performance and personalization
Build and scale backend systems and APIs (Python, Go) that power AI‑driven features
Collaborate with ML and product teams to integrate models into production and deliver delightful user experiences
Develop fast, intuitive React frontends (with Tailwind CSS) that showcase the best of what our AI can do
Architect data flows and memory systems that enable context retention and personalized conversations
Make key technical decisions that balance innovation, performance, and product intuition
Who You Are:
A flexible engineer comfortable working across the stack, from model training to frontend interfaces
Deeply familiar with the challenges of deploying and optimizing LLMs or RAG systems
Experienced writing scalable backend code in Python (and ideally Go)
Proficient in building web applications using React and modern CSS frameworks
Strong understanding of data storage, SQL, and database optimization (Postgres preferred)
Pragmatic: you can balance quick iteration with craftsmanship in a fast‑moving environment
Able to work independently, thrive in ambiguity, and bring a sense of ownership to the product
Nice‑to‑Haves:
Experience integrating ML models into production systems
Familiarity with Google Cloud Platform (GCP) and infrastructure tools (e.g., Terraform)
Background in NLP or conversational AI
Prior startup experience and comfort building products from 0→1
Average salary estimate
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