Interested in this AI/ML Engineer role at Partly?
Apply Now →Skills & Technologies
About This 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.
Our Story
Partly is building the infrastructure for anyone to repair anything. We believe everything built should be repairable, and we're building frontier AI to put repair knowledge to work, starting with the $2tn automotive market.
Our model, Interpreter, is the first AI purpose-built to understand repair. Trained on 1M+ car models and 1B+ parts, it reads a repair job the way an estimator does, resolves damage into the exact purchasable part, and shows its reasoning with a confidence score. On real production jobs, an estimator working with Interpreter reaches 98.8% parts-list accuracy, against 92.1% with today's tools. It powers PartlyRepair, used by collision shops, workshops and suppliers to take the admin off the job; PartlyLabs, the agentic infrastructure enterprises use to deploy Interpreter across their own workflows; and a developer API.
Founded by ex-Rocket Lab engineers, we've raised a $50m Series B led by DST Global (Anthropic, Airbnb, Meta, TikTok, Spotify), with Blackbird Ventures, WNDR, Activant Capital, Icehouse Ventures, Square Peg, Airtree and Ecliptic Venture Capital. Hear it from the team: Why Partly exists and AI at Partly.
The opportunity
Repairers train for years to be on the tools, then spend half their day on quotes, paperwork and finding parts. Partly's product aims to automate the admin so repairers can focus on their craft. The products we've launched this year start with a conversation, and behind it a system generates the interface the moment needs. So we no longer design a set of pages. We design components, and then we design the brain the AI uses to assemble them. Very few designers have done this yet. That's the opportunity: to work out what product design becomes at an AI company, in a domain where a wrong answer is a $1,000 part that can't be returned.
The problems are real and unsolved. How does someone who's never used a chat interface know what to ask when they land on an empty box between phone calls? How does a task you do forty times a day become one tap, not one more sentence to type, with the same result every time? How much of the agent's reasoning do you show before trust turns into a wall of text? Where should the interface be opinionated, and where should it be generated? We don't have the answers yet. You'll own finding Partly's.
The role
Our product teams are organised around a product, each led by a Product Lead (Principal Product Manager) with a Product Engineer and a mix of engineers. Design is not embedded in those teams. We're building a global design team that works across all of our products and is focused on three things: the design system everything is built from, coherence across the whole product family, and the quality of the experience wherever a repairer or supplier meets us. Our customers range from repairers and parts admins in a single independent workshop to multi-billion-dollar repair groups, and the suppliers who provide their parts. The team exists to drive adoption and stickiness through experiences that feel magical, in a way that works for our customers. We work collaboratively with the Product Leads who drive their products.
This is a senior design role for someone with incredible judgement and hands-on experience with AI surfaces and experiences. You lead with design: problem framing, systems thinking, interaction and craft. And you make things real, with working prototypes built inside our design system.
You won't sit in a product team's backlog. Product Leads own their products and what ships. You own the principles, patterns and system they build from, and you set your own agenda for where those need to go next. When something ships below the bar, you're the one who says so.
This role is based in one of our offices: Auckland or Christchurch in New Zealand, London in the UK, or Austin, Texas in the US. The design team works across all of them.
What you'll do
- Own the design system that engineers and coding agents build our AI experiences from. Components, patterns, rules and guidance that read correctly to an engineer and to a coding agent, so the right choice is the default even when you're not in the room.
- Shape and ship the patterns for agentic and generative experiences. Natural language where conversation fits, generated UI where the data is dense, and agents that take on the admin, verification and sourcing around a repair while the person keeps the decisions that matter.
- Define how the product behaves when it's uncertain. When the agent acts, proposes or asks, in line with the cost of being wrong. How it shows its work, how a person corrects it, and who's right when they disagree.
- Give the product its character. How our agents speak to a repairer, their tone, when they're brief and when they explain. A personality people recognise on every surface where they meet us.
- Hold the bar across every product. Notice when the same job looks different in two products and fix it at the source, in the system rather than page by page. Measure adoption and quality, and hold teams to them.
- Work with real prototypes. Build working versions with AI tools inside our design system, test them on real data, and put them in front of repairers and suppliers.
- Pair with engineers, no hand-offs. Work directly with Product Engineers and their teams, polish the details together, and review the UI that coding agents produce from the system.
- Spend real time with customers. In the workshop, on the phone and in the product, running research alongside the Product Leads who hold the deep customer context in each region.
- Write the principles and rules. How the experience behaves on behalf of a person, written clearly enough that an engineer and a model can both follow them.
What you bring
- Senior experience, evidenced. You've been trusted to run a project end to end and can point to the user behaviour that changed because of it. Typically 5+ years designing complex software; the evidence matters more than the years.
- You've designed agentic experiences, and can show the work. Shipped at work or built seriously on your own time. Tasks, tools, autonomy, human-in-the-loop, evaluation: you've made real calls on what an agent does alone, what it proposes and what it asks, and you can tell us which of those calls were wrong.
- Systems thinking. You've contributed to or built a design system that engineers reached for without being told to, and you have a view on what a system needs to be when its heaviest user is a coding agent.
- Craft that holds up in production. Real states, real edge cases, real data. Deep fluency in interaction design, copy that carries its weight, and the visual taste to make a dense, technical tool look considered.
- Product judgement. You can take a fuzzy brief and produce the problem, the constraints and the first testable version, and you can explain a trade-off you made and who disagreed. You build to learn, and you know when your prototype is lying to you.
- Comfort working with several product teams at once, respecting that each Product Lead drives their product while bringing a view that spans all of them, and comfortable telling them when it isn't good enough yet.
- Curiosity about our customers. Professionals with high expectations, low tolerance for friction, and workflows shaped by decades of industry convention.
- Clear written, verbal and visual communication.
Nice to have
- Experience designing workflow-heavy, data-dense or technically complex B2B products.
- Marketplace, catalogue, e-commerce or supply chain domains. Automotive is a bonus, not a requirement.
- High-growth startup and scale-up environments.
- Experience working in a global, distributed design team.
We're not looking for a designer whose strength is producing a hundred screens in a traditional SaaS model, or one who is apprehensive about AI. If half the work makes you nervous, this isn't the role.
How we build
We're an AI research company and we build like one. In discovery mode, anyone, designers included, can use Claude Code to build a working version inside our design system, run it against real data, and show a customer the feature with their own jobs in flight. In scale mode, engineering rebuilds what's proven. We don't ship prototype code to production; we use it to decide what production should be. Designers work in both.
Our Benefits
- Healthy, Catered Lunches - Enjoy fresh, healthy lunches every workday in our Auckland, Christchurch, London and San Francisco 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 gym memberships, rock climbing, physio, massage, GP 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 (four days on full pay for your first three months back).
- Getting Here Is On Us - If you commute to a Partly office or co-working space, choose from a paid 24/7 car park or commute 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 a few steps away.
- Office-First with Flexibility - In cities where we have an office (Christchurch, Auckland, London, San Francisco), we default there every day. This let's us move faster, make better decisions and build strong relationships. We also operate with a very high trust environment, so you can manage your time around your life, and flex your schedule to get your best work done.
- 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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Partly, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Partly AI Hiring
Partly has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $209,100 across 306 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.