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content/careers/Applied ML Engineer.md

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+---
+title: Applied ML Engineer
+date: 02.13.25
+tags:
+  - positions
+  - product
+  - dev
+  - announcements
+---
+
+(NYC, Full-Time)
+
+## About the Role
+We're searching for an applied machine learning engineer excited to work on the ML side of [Honcho](https://honcho.dev). 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](https://discord.gg/plasticlabs) and introduce yourself or engage with [our GitHub](https://github.com/plastic-labs).
+
+## Research We're Excited About
+[s1: Simple test-time scaling](https://arxiv.org/abs/2501.19393)
+[Neural Networks Are Elastic Origami!](https://youtu.be/l3O2J3LMxqI?si=bhodv2c7GG75N2Ku)
+[Titans: Learning to Memorize at Test Time](https://arxiv.org/abs/2501.00663v1)
+[Mind Your Theory: Theory of Mind Goes Deeper Than Reasoning](https://arxiv.org/abs/2412.13631)
+[Generative Agent Simulations of 1,000 People](https://arxiv.org/abs/2411.10109)
+[DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://arxiv.org/abs/2501.12948)
+[Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains](https://arxiv.org/abs/2501.05707)
+[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)  
+[Cyborgism](https://www.lesswrong.com/posts/bxt7uCiHam4QXrQAA/cyborgism)  
+[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]])