About the role
Note: Partly is headquartered in Austin, TX with offices in London, UK, Christchurch, NZ and Auckland, NZ. Wherever you're based, we'll connect you with your nearest office for onboarding, and fly you to join the full team for our quarterly "Season Openers" (we cover travel and accommodation). If you're relocating to join us, we can also assist with relocation costs. This position is based in our Christchurch office. đ Our story Partly's mission is to connect the world's parts and we're doing that by building the first global platform for replacement parts, starting with auto parts. Our big vision is to accelerate the world towards a sustainable future where anyone can fix anything. Founded by ex-Rocket Lab engineers, we utilize cutting-edge technology to solve challenging but exciting problems that make a huge impact in a $1.9 trillion industry. We've more than tripled our team over the last 12 months and expect to double in size again over the coming 12 months. We're a global team spanning both Europe and Australasia. We provide a scalable digital infrastructure solution to some of the world's largest businesses and the most exciting startups. Partly's solutions are integrated across hundreds of companies globally, providing the backbone for cataloging and managing parts online. Our investors include Blackbird Ventures (Canva, CultureAmp etc.), Square Peg, Octopus Ventures, Icehouse, Peter Beck (Rocket Lab), Akshay Kothari (Notion Co-Founder) and Dylan Field (Figma Co-Founder). We're continuing to build a world-class team and ensuring Partly is a place where people can do the best work of their lives. We're proud of the culture we've built at Partly, and our values are lived throughout every experience. Want to learn more about the problems we're solving and the culture we're building at Partly? Hear directly from our team here: https://shorturl.at/iAFUX đïž This role As an Intern ML Research Engineer , you'll work alongside our Applied ML team to help build and ship machine-learning and algorithmic solutions to real problems in the vehicle and parts domain. You'll be paired with experienced engineers who will mentor you as you take messy, real-world inputs (noisy data, edge cases, shifting constraints) and help turn them into measurable outcomes. This is a hands-on internship for someone early in their journey who wants to learn by doing. You'll contribute to genuine product work, including our efforts to build a foundational model for the vehicle and parts problem space, and you'll be judged by what you help ship: strong baselines, sound evaluation, and improvements that compound over time. Expect to learn fast, ask good questions, and see your work make it into production. đ» What will you do Contribute to Applied ML solutions. Work on a scoped piece of a real problem area, from framing through to experimentation and, where ready, production rollout, with guidance from your mentor. Help build evaluation that makes progress clear. Assist in creating gold datasets and metrics that reflect real-world performance, so we can tell what's actually working. Learn to blend ML and algorithms pragmatically. Get hands-on with modelling, ranking, classification, retrieval, and heuristic methods, and start developing judgement on when each is the right tool. Build with production in mind. Learn how latency, scale, failure modes, and reliability shape what we ship, not just how a model performs in a notebook. Work across
