• Frame fuzzy product problems as ML problems and pick the right approach: ranking, retrieval, classification, sequence models, LLM agents, or classic stats • Run end-to-end: data exploration, offline evaluation, prototype, online experiment, iteration • Push to the cutting edge when it matters, stay pragmatic when it doesn't • Own offline metrics (NDCG, recall@k, AUC, calibration) and tie them to online metrics (booking lift, retention, GMV) • Ship models to production with our engineering team. Our ML stack is FastAPI, PostgreSQL, BigQuery, AWS App Runner, with retrieval via FAISS and sentence-transformers, and managed LLM APIs (Claude, Gemini) • Build evaluation harnesses and monitoring so we know when models drift • Develop LLM-powered features across HostCopilot (drip campaigns, retention nudges, pricing and content suggestions) and Pal-facing surfaces (AI Concierge, semantic search, recommendations) • Partner with product to size opportunities and translate findings into roadmap decisions • Set the bar for the squad on ML rigor: offline evaluation, experiment design, and writeups

• Work to improve the open-source machine learning ecosystem • Mainly work on existing open-source libraries such as Transformers, Datasets, Pytorch and vLLM • Interact with users and contributors across the broad open-source ML ecosystem • Help foster one of the most active machine learning communities • Collaborate with researchers, ML practitioners, and data scientists every day through GitHub, our forums, and Slack

• work to improve the open-source machine learning ecosystem • mainly work on existing open-source libraries such as Transformers, Datasets, Pytorch and vLLM • interact with users and contributors across the broad open-source ML ecosystem • help foster one of the most active machine learning communities, helping users contribute to and use the tools built • work with researchers, ML practitioners, and data scientists every day through GitHub, forums, and Slack

Role Description As an Open-Source Machine Learning Engineer, you'll work to improve the open-source machine learning ecosystem. You'll mainly work on existing open-source libraries such as Transformers, Datasets, Pytorch, and vLLM, and you'll interact with users and contributors across the broad open-source ML ecosystem. We'll brainstorm with you to put you in a position to do the work that interests you and that is impactful. - Help foster one of the most active machine learning communities. - Assist users in contributing to and using the tools you build. - Collaborate with researchers, ML practitioners, and data scientists through GitHub, forums, and Slack. Qualifications - Strong Python skills, with experience writing clean, well-tested, maintainable library code. - Deep hands-on experience with a modern deep-learning framework, especially PyTorch (JAX or TensorFlow a plus). - Practical experience with the Hugging Face open-source stack (Transformers, Datasets, Accelerate) or comparable ML libraries. - A public track record of open-source contributions, for example merged pull requests to ML or data libraries, that we can review on GitHub. - Solid understanding of modern machine learning and deep learning, including transformer architectures. - Experience collaborating with a technical community in the open (GitHub issues and reviews, forums, Slack or Discord). - Fluent written English for asynchronous collaboration across a distributed, global community. Requirements - Experience maintaining an open-source project (nice to have). - Prior contributions to Transformers, Datasets, Accelerate, or similar libraries (nice to have). - Familiarity with distributed training, inference optimization, or GPU/accelerator performance work (nice to have). - Experience training or fine-tuning models at scale (nice to have). Benefits - Flexible working hours and remote options. - Health, dental, and vision benefits for employees and their dependents. - Parental leave and flexible paid time off. - Reimbursement for relevant conferences, training, and education. - Company equity as part of the compensation package. - Support for remote employees to visit office spaces in NYC and Paris. - Workstation outfitting to ensure success.