Woodside AI

Develop and prototype ML algorithms, evaluate models, and contribute to scalable ML solutions. | Strong ML background, proficiency in Python and ML frameworks, experience with end-to-end ML solutions, and familiarity with multimodal models. | Woodside AI is a venture studio based in Palo Alto, focused on crafting category-defining AI companies in partnership with world-class entrepreneurs and industry market leaders. Founded by industry titans and a top AI researcher from OpenAI, Woodside AI combines a proven track record of venture building and technical innovation. Our team members have shaped companies such as TSMC, Canva, and DRW, as well as backing disruptors like Anduril, Instacart, and Palantir in their early days. Our unique model of venture building has produced industry shapers such as Digital Asset (raised $135M Series E in 2025), Novaprime (now partnering with Fannie Mae and Freddie Mac), ErisX (acquired by CBOE in 2022), and Hashnote (acquired by Circle in 2025). We are fully self-funded and focused on tackling complex industry challenges unlikely to be solved by traditional startups. Our unique approach gives founders and early teams immediate market access, domain expertise, and ample funding to take bold swings with real impact, while saving precious time and energy by staying laser-focused on delivering valuable solutions. The Opportunity As a Machine Learning Engineer at Woodside AI, you’ll be part of a small, high-caliber applied research group focused on turning cutting-edge ideas into real-world systems. You’ll work closely with our CTO and applied research lead to investigate new techniques, prototype solutions, and integrate ML-driven capabilities into early product concepts across our venture portfolio. This is a hands-on role for engineers who enjoy ambiguity, intellectual ownership, and working across the full lifecycle of applied ML, from research and experimentation through implementation and evaluation. What You’ll Do Create or adapt novel machine learning algorithms and techniques to solve practical, real-world problems. Rapidly prototype and implement ML solutions, collaborating closely with product and engineering partners. Proactively identify relevant research, tools, and approaches, and contribute new ideas for experimentation and discussion. Design evaluation workflows to measure performance and guide iteration. Architect how ML approaches scale and contribute to long-term data and model flywheels. Occasionally contribute to technical blog posts, patent disclosures, or other public-facing technical work. Participate in regular research syncs and technical discussions with the applied research and engineering teams. What We’re Looking For Strong foundations in machine learning, with hands-on experience building and implementing ML systems. Proficiency in Python and modern ML frameworks such as PyTorch and Transformers. Experience with vector databases and applied ML tooling (e.g., LangChain, DSPy, MCP, or similar). Background in computer science, statistics, or a related field. Experience building end-to-end ML solutions, ideally with exposure to data engineering or applied data science. Familiarity with multimodal models (e.g., text, image, audio), including computer vision or signal processing. Comfortable working in fast-paced, ambiguous environments and taking initiative without detailed direction. Strong curiosity, practical mindset, and ability to learn and build quickly. Nice to Have Experience with data science or time-series forecasting in large-scale or enterprise environments. Exposure to data engineering workflows or infrastructure. Familiarity with MLflow, evaluation pipelines, or experiment tracking. Some academic publications or credible research output. Experience contributing to open-source projects. Working knowledge of TypeScript or collaborating closely with full-stack engineering teams.