Senior ML Research Engineer, NZ

Location: Christchurch
Employment Type: Full time
Location Type: On‑site
Department: Product & Engineering

Partly is building the first global platform for replacement parts, starting with auto parts. Founded by ex‑Rocket Lab engineers, we use cutting‑edge technology to solve challenging problems in a $1.9 trillion industry and grow rapidly.

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.

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• Ship Applied ML solutions end‑to‑end. Own a problem area from framing through to production rollout, monitoring, and iteration.
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• Design evaluation that makes progress undeniable. Build gold datasets and metrics that reflect real‑world performance.
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• 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.
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• Build for production constraints. Consider latency, scale, failure modes, observability and safe rollout plans as part of the core deliverable.
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• Work across teams to drive adoption. Partner with product and engineering so the solution actually changes outcomes, not just metrics.
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• Raise technical standards by example. Reproducible experiments, crisp docs, thoughtful reviews and clear trade‑offs that keep velocity high without breaking reliability.
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• Proven track record shipping ML into production. Delivered systems used by others and understands monitoring, regressions and operational realities.
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• Strong algorithmic thinking. Comfortable with classical algorithms and data structures, and knows when they beat ML.
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• Excellent applied modelling fundamentals. Builds strong baselines, chooses sensible methods and evaluates correctly.
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• Evaluation‑first mindset. Instinctively builds the evaluation framework before over‑investing in complexity, and can articulate failure modes.
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• Engineering‑minded execution. Writes maintainable code, works effectively with services/pipelines, and cares about performance and reliability.
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• Clear communicator and collaborator. Aligns stakeholders, documents trade‑offs and keeps delivery moving in a low‑bureaucracy environment.
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• Experience in messy, weakly‑supervised domains. Worked where ground truth is imperfect and success requires clever measurement and iteration.
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• (Bonus) Experience with search/ranking/retrieval or graph‑based approaches. Built systems that combine multiple signals into reliable outputs.
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• Healthy, Catered Lunches – Fresh lunches every workday in offices.
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• Healthy Body, Healthy Mind – $1,500 annual wellness allowance (or local equivalent) on a company‑branded card.
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• Family Comes First – Primary caregivers receive 3 months of fully paid parental leave plus flexible return‑to‑work.
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• Getting Here Is On Us – Paid 24/7 car park or commute allowance.
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• Workspaces That Inspire – Architecturally designed offices with great coffee and social spaces.
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• Office‑First with Flexibility – Default to in‑office in cities with a location, but high trust environment allows flex.
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• We Celebrate Together – Weekly happy hours, monthly lunches, quarterly season openers and an annual global off‑site.
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Please note: If you don't have all the skills/experience listed above but believe you could be outstanding in this role, please still consider applying. Many folks, especially those from underrepresented or marginalised groups, often count themselves out. Please allow us to learn more about you and why you're exceptional!

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