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| title | date | tags | ||||
|---|---|---|---|---|---|---|
| Applied ML Engineer | 02.13.25 |
|
(NYC, Full-Time)
About the Role
We're searching for an applied machine learning engineer excited to work on the ML side of Honcho. You'll work alongside our interdisciplinary team to transform novel ideas into production systems that help LLMs understand and align with individual users.
This role requires a strong engineer who can rapidly prototype and ship ML systems. The pace of the LLM space is staggering - we need someone with a hacker mentality who is excited about diving into papers/codebases, implementing novel methods at breakneck speed, and figuring out what actually works. Our team is small and fast-moving, so you'll have the freedom to experiment widely and ship impactful features quickly.
About You
- 2-3 years applied LLM experience or equivalent
- Proficiency with a popular Python ML library (e.g PyTorch, TF, JAX, HF transformers, etc)
- Experience building LLM systems
- Experience with post-training methods & implementing LLM papers
- Comfortable in Unix environment + attendant command line tools (Git, Docker, etc)
- Up to date on OS AI community & technologies
- High cultural alignment with Plastic Labs' ethos
- In NYC or willing to move to NYC
- Complementary interest or experience specific to reinforcement learning, 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 research@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.
Research We're Excited About
s1: Simple test-time scaling
Neural Networks Are Elastic Origami!
Titans: Learning to Memorize at Test Time
Mind Your Theory: Theory of Mind Goes Deeper Than Reasoning
Generative Agent Simulations of 1,000 People
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains
Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm
Theory of Mind May Have Spontaneously Emerged in Large Language Models
Think Twice: Perspective-Taking Improved Large Language Models' Theory-of-Mind Capabilities
Representation Engineering: A Top-Down Approach to AI Transparency
Theia Vogel's post on Representation Engineering Mistral 7B an Acid Trip
A Roadmap to Pluralistic Alignment
Open-Endedness is Essential for Artificial Superhuman Intelligence
Simulators
Extended Mind Transformers
Violation of Expectation via Metacognitive Prompting Reduces Theory of Mind Prediction Error in Large Language Models
Constitutional AI: Harmlessness from AI Feedback
Claude's Character
Language Models Represent Space and Time
Generative Agents: Interactive Simulacra of Human Behavior
Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge
Cyborgism
Spontaneous Reward Hacking in Iterative Self-Refinement
... accompanying twitter thread
(Back to Work at Plastic)