This position has been filled. However, we're always looking to meet great candidates. If you like what's listed here, please reach out regardless -- we are growing fast and might have similar positions in the future.
Check out our other open positions here.
(NYC / Remote, Full-Time)
About the Role
We're searching for a full-stack engineer to help build Honcho, our user representation infrastructure, and accelerate the development of a new paradigm of AI personalization.
You should be comfortable working across the stack, from crafting user interfaces to designing APIs and managing deployments. It requires strong foundations both frontend and backend.
This role is a fast-paced opportunity to work alongside a seasoned interdisciplinary team. If you're eager to learn, can tackle diverse challenges quickly, and love building at the edge, get in touch.
About You
- 1-2 years experience or equivalent (new grads OK)
- High cultural alignment with Plastic Labs' ethos
- Primary location +/- 3 hrs of EST
- Impulse for rapid learning & trying new tech at the edge
- Adept with Python &/ Node.js
- Experience building CRUD APIs
- Experience with basic Linux & Shell
- Experience with cloud deployment (AWS, GCP, Azure, Fly, etc)
- Experience with SQL databases
- Experience with Docker containers
- Experience with Github & Git workflows
- Experience leveraging AI tools to argument workflow
- Familiarity with & interest in LLMs
- Interest in security, distributed systems, & identity
- Complementary interest in cognitive sciences (cs, linguistics, neuroscience, philosophy, & psychology) or other adjacent interdisciplinary fields a plus
How to Apply
Please send the following to dev@plasticlabs.ai:
- Resume/CV in whatever form it exists (PDF, LinkedIn, website, etc)
- Portfolio of notable work (GitHub, pubs, ArXiv, blog, X, etc)
- Statement of alignment specific to Plastic Labs--how do you identify with our mission, how can you contribute, etc? (points for brief, substantive, heterodox)
Applications without these 3 items won't be considered, but be sure to optimize for speed over perfection. If relevant, be sure to credit the LLM you used.
And it can't hurt to join Discord and introduce yourself or engage with our GitHub.
(Back to Work at Plastic)