Head of Platform

Company: Partly
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 Partly Repair, used by collision shops, workshops and suppliers to take the admin off the job; Partly Labs, 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 .
This role
Head of Platform is responsible for building and leading the team that provides Partly ’s foundation for shipping and operating software: the internal platform, infrastructure foundations, reliability practices, and security-by-default capabilities that let product teams move fast without breaking things. This now extends in two directions: the specialised infrastructure behind Partly 's foundational ML/AI capabilities (GPU compute, model training and serving, inference cost), and a platform that natively supports agentic development so regionally-embedded product engineers (including forward-deployed engineers) can move exceptionally fast. You’ll treat platform as an internal product - setting strategy, driving adoption, and partnering closely with engineering and the business to improve delivery speed, uptime, and cost efficiency as we scale.
What will you do
Platform Strategy & Team Leadership: Build and lead the Platform function (SRE, DevEx, Infrastructure, Security enablement as appropriate). Set a clear roadmap, establish ways of working, hire and grow the team, and manage prioritisation/trade-offs.
Developer Experience : Create the default path for engineers to build, test, deploy, and operate services (service templates, CI/CD, environment provisioning, secrets/config, deployment patterns, feature flags). Focus on adoption and measurable improvements in developer productivity.
Reliability & Operational Excellence: Own or drive (depending on org boundaries) our reliability foundations: observability (metrics/logs/traces), alerting standards, incident response, postmortems, SLO/error budget practices, rollout/rollback patterns, backups/DR, and reducing on-call toil.
Infrastructure Foundations: Ensure our cloud and Kubernetes foundations are scalable, secure, and maintainable. Use Infrastructure-as-Code and automation (Terraform for GCP, GitOps with ArgoCD, Python/Bash tooling, etc.) to run repeatable, auditable infrastructure.
ML & Foundation-Model Infrastructure: Build the platform beneath Partly 's foundational models — accelerator (GPU/TPU) provisioning, scheduling and utilisation, training/fine-tuning orchestration, and scalable, low-latency model serving. Own the ML deployment lifecycle (model registry, experiment tracking, evaluation/observability) and treat inference economics as a first-class cost driver, since inference — not training — typically dominates AI infrastructure spend at scale.
Agentic & Forward-Deployed Enablement: Make the platform natively support agentic development. Design golden paths that are self-service and machine-consumable (discoverable, executable, safe) so AI coding agents and engineers alike can go from generated code to production without the platform team as a bottleneck — with isolated execution environments, guardrails, and observability built in. Ensure regional and forward-deployed engineers can move at maximum speed on a robust but extensible foundation.
Security Enablement: Partner with security/compliance to make secure-by-default the easiest path (IAM patterns, secrets management, vulnerability management, policy-as-code where appropriate, audit evidence automation).
Cost & Performance Ownership: Establish FinOps-style visibility and guardrails, track cost drivers, and deliver optimisations that improve unit economics without sacrificing reliability or developer velocity.
Cross-Functional Collaboration: Work closely with Product/Engineering leadership and stream-aligned teams to understand bottlenecks, influence architectural direction, and ensure platform work translates into real outcomes for customers and the business.
Hands-on Delivery (especially early): You’ll be technical enough to dive in, review designs, unblock incidents, prototype solutions, and set technical standards, while building a team that doesn’t rely on you as the single point of execution.
Want to learn more about the problems we're solving and the culture we're building at Partly ? Hear directly from our team here:
Your skills
Platform Leadership: Proven experience leading a platform / infrastructure / SRE function, including roadmap ownership, stakeholder management, and building teams in a fast-moving environment. You know how to balance reliability, developer productivity, security, and cost.
SRE & Operations Expertise: Strong grounding in SRE practices (SLOs/error budgets, incident management, observability, capacity planning, resilience engineering) and a track record of improving uptime and reducing operational toil.
Cloud & Kubernetes Depth: Deep familiarity with running production workloads on a major cloud (GCP preferred) and Kubernetes. You can design scalable infrastructure, debug systems issues, and make pragmatic build vs buy decisions.
Infrastructure-as-Code & Automation: Hands-on expertise with IaC and GitOps workflows (Terraform, ArgoCD or equivalent) and the software engineering ability to build robust tooling (not just scripts).
Developer Experience Mindset: You treat platform as a product: you can define “golden paths,” simplify workflows, drive adoption through empathy and excellent docs, and measure impact (e.g., lead time, deploy frequency, MTTR, change failure rate).
Security-by-Default: Practical experience embedding security into platforms and SDLC (IAM, secrets, vulnerability management, supply chain hygiene). Bonus if you’ve helped achieve/maintain compliance (SOC2/ISO).
Strong Engineering Fundamentals: Solid CS and system engineering fundamentals (concurrency, networking, Linux internals, performance profiling, distributed systems, reliability patterns).
ML/AI Infrastructure Depth: Familiarity with the AI infrastructure stack — GPU/accelerator compute, training/fine-tuning orchestration, inference and model serving, MLOps (registries, experiment tracking, evals), and the cost/performance trade-offs of running models in production. You know where the real bottlenecks and spend live.
Agentic-Native Platform Thinking: You design platforms for a world where AI agents are first-class users, not just humans — machine-consumable golden paths, safe autonomous execution, and self-service that scales to far higher workload volume. Bonus if you’ve enabled forward-deployed / embedded engineers to customise and ship rapidly without sacrificing robustness.
Communication & Influence: Excellent written/verbal communication. You can align senior stakeholders, explain trade-offs t