---
title: ML Research Engineer
date: 08.23.24
tags:
  - positions
  - research
  - dev
  - announcements
---
(NYC / Remote, Full-Time)

## About the Role
We're searching for a machine learning research engineer excited to work on problems critical to the next version of [Honcho](https://honcho.dev). In large part, this means exploring novel ways to create and leverage high-fidelity user representations.

This role requires subversive tactics for working with LLMs--approaching from a representation level, top down thinking, emergent abilities, meta-methods, etc. There's a massive under-explored gulf between deterministic, structured LLM use cases and xenocognitive psychoanalysis. Within it lies enormous opportunity to investigate human-LLM coherence and the personalized, positive sum experiences we can build on that substrate. 

We think the lack of study here is heavily to blame for the divorce of ML evals and product metrics. This leads to poor application retention, trust violations, high churn, and avoiding (abandoning?) much of the space of possible AI apps. Especially those characterized by personalization, user coherence, individual alignment, and social acuity.

At Plastic, you'll have the opportunity to work with an interdisciplinary team to discover the best ways to represent human identity, unite ML & product outcomes, train best in class theory of mind models, author open-ended evals & frameworks, publish research, contribute to OS ML, set new user data standards of transparency, and shine a light on overhung capabilities lying latent in large language models.

## About You
- 2-3 years NLP ML experience or equivalent
- Up to date on OS AI community & technologies 
- Comfortable in Unix environment + attendant command line tools (Git, Docker, etc)
- High cultural alignment with Plastic Labs' ethos
- Experience training, fine-tuning, & evaluating LLM performance 
- Experience implementing & maintaining systems based on SOTA research
- Experience or background in alignment & interpretability methods
- Proficiency with some set of popular Python ML libraries (e.g PyTorch, TF, JAX, HF transformers, etc)
- Complementary interest or experience specific to representation engineering, control vectors, prompt optimization, sparse auto-encoders, agentic frameworks, emergent behaviors, theory of mind, identity a plus
- Complementary background in cognitive sciences (cs, linguistics, neuroscience, philosophy, & psychology) or other adjacent interdisciplinary fields a plus

## How to Apply
Please send the following to jobs@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](https://discord.gg/plasticlabs) and introduce yourself or engage with [our GitHub](https://github.com/plastic-labs).

## Research We're Excited About
[Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://arxiv.org/pdf/2102.07350)  
[Theory of Mind May Have Spontaneously Emerged in Large Language Models](https://arxiv.org/pdf/2302.02083v3)  
[Think Twice: Perspective-Taking Improved Large Language Models' Theory-of-Mind Capabilities](https://arxiv.org/pdf/2311.10227)
[Representation Engineering: A Top-Down Approach to AI Transparency](https://arxiv.org/abs/2310.01405)
[Theia Vogel's post on Representation Engineering Mistral 7B an Acid Trip](https://vgel.me/posts/representation-engineering/)  
[A Roadmap to Pluralistic Alignment](https://arxiv.org/abs/2402.05070)  
[Open-Endedness is Essential for Artificial Superhuman Intelligence](https://arxiv.org/pdf/2406.04268)  
[Simulators](https://generative.ink/posts/simulators/)  
[Extended Mind Transformers](https://arxiv.org/pdf/2406.02332)  
[Violation of Expectation via Metacognitive Prompting Reduces Theory of Mind Prediction Error in Large Language Models](https://arxiv.org/abs/2310.06983)  
[Constitutional AI: Harmlessness from AI Feedback](https://arxiv.org/pdf/2212.08073)  
[Claude's Character](https://www.anthropic.com/research/claude-character)  
[Language Models Represent Space and Time](https://arxiv.org/pdf/2310.02207)  
[Generative Agents: Interactive Simulacra of Human Behavior](https://arxiv.org/abs/2304.03442)  
[Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge](https://arxiv.org/abs/2407.19594)  
[Spontaneous Reward Hacking in Iterative Self-Refinement](https://arxiv.org/abs/2407.04549)  
[... accompanying twitter thread](https://x.com/JanePan_/status/1813208688343052639)  


(Back to [[Work at Plastic]])