Machine Learning Lead
4 weeks ago
We're a team of machine learning engineers training task-specific generative models for psychology. Our goal is to build an AI therapist to help people change their mind and their lives in the ways that they want to. We partner with organizations around the globe and power use cases, including AI-assisted crisis text response, while securing best-in-class datasets to power our models.
Success to us means every human being in need of support having somewhere to go. We're a well-funded, seed-stage startup backed by top-tier tech investors involved in Huggingface, Runway, Notion, Stability, Scale, and other best-in-class AI companies.
We're building a powerful team by empowering our engineers with the autonomy, flexibility, and resources to do their best work. We dream big and iterate fast. If that sounds like home, we'd love to hear from you.
The Role
As ML Lead, you'll collaborate with the founders on ML research, data collection and curation, and be responsible for training large models from start to finish, in collaboration with our founding ML engineers. You'll be able to work at a faster pace than almost anywhere else, while writing high-quality code and producing meaningful scientific insights.
You'll be primarily working with 7-70B parameter language models. You'll be involved in data collection, data curation, supervised fine-tuning, and preference optimization. Our research state-of-the-art research in reinforcement learning for language models. You will be responsible for reading papers and identifying state-of-the-art techniques for us to learn from. Our application backend is written in Kotlin and our ML stack (PyTorch) utilizes modern tooling in the ML space, including some that we've developed in-house. We write high-quality, typed, Zen code.
About you:
- You have personally kicked off scripts for training language models on a multi-GPU setup.
- 5+ years developing deep learning models in PyTorch, TensorFlow or JAX, include 3+ years in a production environment.
- You have a demonstrated track record of excellence, including coming up with new ideas or improving upon existing ideas in machine learning, ideally demonstrated by first-author publications or equivalently impressive projects.
- You also have experience with software engineering best practices and have a deep appreciation for what good code looks like.
- You're fast-paced and pragmatic. You'd rather prove out an idea through quick MVP code than present a slide deck to explain it.
- You can explain complex ideas to non-technical people
- You understand why deep learning is magic.
- Competitive compensation
- Hybrid environment, highly collaborative, fast-paced culture
- Work with a crazy passionate team that cares deeply about the impact of our work on mental health, especially in a post-AGI world
- Bias towards action
- Authenticity
- Strong opinions, loosely held
- We're nowhere near the end of history
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