About the role
Note: Partly is headquartered in the UK, with a Product and Engineering HQ in Christchurch, New Zealand, and an early presence in San Francisco, USA . If you are based outside of a Hub, we will fly you to the nearest Hub for 1 week per quarter for our “Season Openers” (we pay for your travel and accommodation). 🚀 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 toward a sustainable future where anyone can fix anything Founded by ex-Rocket Lab engineers, we utilise 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 cataloguing and managing parts online. Our investors in 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. 🖍️ This role The Senior ML research engineer will build and ship machine-learning and algorithmic solutions to real problems in the vehicle and parts domain. You’ll report to Abram Spamers and work closely with engineering and product partners to take ambiguous inputs (noisy data, edge cases, shifting constraints) and turn them into measurable, production-grade outcomes. A core part of this role will be helping us build a foundational model for the vehicle and parts problem space - one that can be adapted across multiple downstream tasks and product surfaces, and that improves over time as we expand data and evaluation. This role is for someone who wants to be judged by what ships: strong baselines, strong evaluation, reliable systems, and improvements that compound over time. 💻 What will you do Ship Applied ML solutions end-to-end. Own a problem area from framing through to production rollout, monitoring, and iteration. Design evaluation that makes progress undeniable. Build gold datasets, and metrics that reflect real-world performance. Blend ML and algorithms pragmatically. Use the right tools: modelling, ranking, classification, retrieval, graph/heuristic methods, LLMs, and domain-specific algorithms where they outperform learning. Build for production constraints. Consider latency, scale, failure modes, observability, and safe rollout plans as part of the core deliverable. Work across teams to drive adoption. Partner with product and engineering so the solution actually changes outcomes, not just metrics. Raise technical standards by example. Reproducible experiments, crisp docs, thoughtful reviews, and clear trade-offs that keep velocity high without breaking reliability. 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 🥷 Your skills Proven track record shipping ML into production. You’ve delivered systems used by others, and you understand monitoring, regressions, and operational realities. Strong algorithmic thinking. You’re comfortable with classical algorithms and data structures, and know when they beat ML. Excellent applied modelling fundamentals. You can build strong baselines, choose sensible methods, and evaluate correctly. Evaluation-first mindset. You instinctively build the evaluation framework before you over-invest in complexity, and you can articulate failure modes. Engineering-minded execution. You write maintainable code, work effectively with services/pipelines, and care about performance and reliability. Clear communicator and collaborator. You can align stakeholders, document trade-offs, and keep delivery moving in a low-bureaucracy environment. Experience in messy, weakly-supervised domains. You’ve worked where ground truth is imperfect and success requires clever measurement and iteration. (
