ML Research Engineer Intern/Graduate, NZ

Icehouseventures; Christchurch; On-site; NZD 50,000 - 70,000; Full time

Benefits offered by this job

Competitive base salary + equity

Flexible working hours

Focus Days for deep work

Parental leave and flexible return to work

Job summary

Icehouseventures is seeking an Intern ML Research Engineer for their Christchurch office. In this role, you will collaborate with the Applied ML team to develop machine-learning solutions that tackle real-world problems in the vehicle and parts domain.

The internship emphasizes learning through practice, as you will be guided by experienced engineers. You will contribute to building foundational models and participate in genuine product work.

Ideal candidates have a background in computer science or related fields and possess strong problem-solving skills.

Qualifications

• Strong fundamentals in computer science and algorithms.
• Practical experience in ML through projects or internships.
• Desire to learn and grow in a collaborative environment.

Responsibilities

• Contribute to Applied ML solutions in solving real problems.
• Assist in creating datasets and metrics for performance evaluation.
• Work across teams to connect ML work to meaningful outcomes.

Skills

Computer science fundamentals

Machine learning experience

Data structures

Curiosity and evaluation

Clean code writing

Education

Relevant degree or coursework in computer science, mathematics, engineering

Tools

Python

Machine Learning frameworks

Job description

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.

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.

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.

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

• High trust, low process and no bureaucracy. We hire exceptional people whose judgment we trust. This means we proactively remove any process or rules that slow us down (for example, our expense policy is simply the “red face test”).

• Competitive base salary + equity. We offer competitive salaries and generous equity options for all full‑time employees, ensuring everyone shares in the financial upside when we win.

• Flexible working hours. Choose when to work based on what time you’re most effective (no mandatory or set hours). We combine flexibility with an office‑first approach (in cities where we have critical mass, i.e. Texas, London, Christchurch, Auckland).

• Focus Days. Two days per week, with zero meetings, dedicated solely to uninterrupted deep work.

• Take time when you need it. We don’t ask questions or care if people have a negative leave balance. We work extremely hard and trust our team to take the time they need to recharge.

• Offices in Christchurch CBD. We invest heavily in our offices (standing desks, healthy snacks, quality coffee, drinks on tap) to ensure they’re places people are excited by, where they build relationships and get their best work done.

• Learn from the best. Whether it’s during a ‘Lunch n Learn’ or hearing from a unicorn CEO at a Fireside chat, you’ll have the opportunity to constantly learn from the world’s best.

• Quarterly season openers & annual global offsite. Connect regularly at the nearest centralised location for a week of collaboration, big‑picture planning and team events.

• Team connection. Monthly team lunches, celebrating our wins, happy hours and more!

• Parental leave and flexible return to work. Do what works for you. Primary carers can return with 4‑day weeks (on 100% pay for the first 12 weeks). Secondary carers get 10 days full pay.

• Payroll Giving. We encourage generous giving and donate to the high‑impact charities you support.