Machine Learning Research Engineer

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.

🚀 Our story

Partly is connecting the world's parts, and we're doing that by building the AI infrastructure layer for the global repair industry, starting with the $2tn automotive market. Our frontier model, Interpreter, is the world's first AI purpose-built to understand vehicle damage and the parts needed to fix it. Thousands of businesses across the global repair supply chain already rely on it.

Founded by ex-Rocket Lab engineers, we've tripled in size in the last 18 months and have recently raised a $50m Series B led by DST Global (Anthropic, Airbnb, Meta, TikTok, Spotify) and including Blackbird Ventures (Canva, CultureAmp etc.), WNDR, Activant Capital, Icehouse Ventures, Square Peg, Airtree, and Ecliptic Venture Capital. We're headquartered in Austin, with offices in New Zealand and London.

We're continuing to build a world-class team ensuring Partly is a place where people can do the best work of their lives. We're proud of the culture we've built, 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

🖍️ 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.

💻 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 the team. Partner with product and engineering so your work connects to real outcomes, and pick up how a high-velocity team operates.

Raise the bar on the basics. Reproducible experiments, clear notes, and thoughtful questions that keep work easy to build on.

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

Strong fundamentals. You're studying or have recently studied computer science, machine learning, mathematics, engineering, or a related field, and you have solid grounding in algorithms and data structures.

Some practical experience. Through coursework, personal projects, research, or prior internships, you've built things with ML or code and can talk through what you did and why.

Curiosity and an evaluation instinct. You like to understand whether something is actually better, not just whether it runs, and you're keen to learn how to measure that properly.

Engineering-minded. You write reasonably clean code, are comfortable picking up new tools, and care about getting things to actually work.

Clear communicator. You can explain your thinking, ask for help when you need it, and take feedback well in a collaborative, low-bureaucracy environment.

Bias for learning. You want to be stretched, you take ownership of your growth, and you're excited to work on hard, real-world problems rather than tidy textbook ones.

(Bonus) Exposure to search, ranking, retrieval, graph-based approaches, LLMs, or working with messy real-world data.

Please note: if you don't have all the skills or experience listed above but believe you could be outstanding in this role, please still consider applying. Many people count themselves out. We'd love the chance to learn more about you and why you're exceptional.

🎡 Benefits

• Healthy, Catered Lunches - Enjoy fresh, healthy lunches every workday in our Auckland, Christchurch, London, and Austin offices. With no meal prep needed, you can eat, connect, and refuel with your team. (And yes, snacks and drinks are always on hand.)

• Healthy Body, Healthy Mind - We care about performing at our peak. Every team member gets a $1,500 annual wellness allowance (or local equivalent) on a Partly-branded card. Use it on things such as gym memberships, rock climbing, physical therapy, massage, doctor visits, prescriptions—anything that you or your family need!

• Family Comes First - Primary caregivers receive 3 months of fully paid parental leave, plus a flexible return-to-work plan (four days a week at full pay for your first three months back).

• Getting Here Is On Us - If you commute to a Partly office or coworking space, choose from a paid 24/7 parking spot or a commuting allowance. One less thing to think about!

• Workspaces That Inspire - Our brand-new, architecturally designed offices are built for collaboration and creativity, with great coffee, social spaces, and some of the best cafes just a few steps away.

• Office-First with Flexibility - In cities where we have an office (Christchurch, Auckland, London, and Austin), we default to working from the office every day. This lets us move faster, make better decisions, and build strong relationships. We also operate with a high-trust environment, so you can manage your time around your life and flex your schedule to do your best work.

• We Celebrate Together - From weekly happy hours and monthly lunches to quarterly season openers and an annual global offsite, we make time to connect, celebrate, and have fun as one team.