Head of Engineering

About PartsPulse

PartsPulse is the AI-powered margin and revenue platform for aftermarket parts. We help equipment manufacturers (OEMs) and dealers optimize the three levers that decide how much money a parts business makes: pricing, sales, and stocking.

OEMs and dealers depend on each other but operate in the dark. Dealers run multi-vendor parts businesses on tribal knowledge, and OEMs can't see their wallet share or where they are losing. The result is mispriced parts, missed sales, and equipment sitting idle waiting on a part.

PartsPulse connects both sides for the first time. Underneath is an AI parts engine that understands every part (equivalents, quality tiers, supersessions, market price) and every customer (what they buy, from whom, and who they resemble). We launched in March 2026, have a charter customer in production, and are ready to accelerate.

The Role

We're hiring a Head of Engineering to be the most senior technologist at PartsPulse. This is a hands-on leadership role. You'll write production code every week and own the architecture and infrastructure of our multi-tenant SaaS platform.

You'll be a player/coach to a team of full-stack engineers and data scientists, and the day-to-day technical partner to our AI-fluent product leader. You'll work AI-native, using agentic coding tools to ship production code at a pace that wasn't possible six months ago.

We need someone who leads from the front. You set the standard for how we build by shipping great software yourself, and you coach the team to that standard.

What You'll Do

Build & Ship

• Write production code across the stack (backend services, APIs, data pipelines, front end) as a core contributor, not a reviewer on the sidelines.
• Make agentic coding tools your primary way of building, and know where they break down and when to take over. Lead how the team uses them: set the shared practices, standardize the workflow, and keep raising the bar as the tools change.
• Work side by side with Product to shape the roadmap, sequence technical work, make smart tradeoffs between speed and durability, and turn ideas into working software.
• Partner with Product on our development process. Own the engineering components and establish and maintain the team's quality standards. Understand how AI multi-agent orchestration is fundamentally transforming processes originally designed around people and take advantage of these opportunities.
• Join customer conversations when technical depth helps land a deal or an integration.

Own The Platform

• Own the architecture of our multi-tenant SaaS platform as it scales from one customer to many.
• Manage the production environment end to end: cloud infrastructure, databases, CI/CD, deploys, and cloud cost.
• Design ingestion and integrations for OEM and dealer systems (ERP, DMS, pricing, inventory) that are messy, high-volume, and different for every customer.
• Put in place what enterprise customers expect: SSO, high availability, observability, data security, and SOC 2 readiness. Make pragmatic build-vs-buy decisions and document them.

Put AI & Data Science Into Production

• Work with our data scientists to get models into production and keep them healthy, with monitoring in place - customer clustering, pricing, and part cross-reference to start.
• Lead the build-out of LLM features in our platform - opportunity grading, AI suggestions on pricing flags, LLM-assisted call scripts - from architecture and model selection through guardrails and production rollout.
• Shape how AI changes the way we build: work with Product to define the path from PM and data science prototypes to production code, decide where humans stay in the loop, and grow the team’s AI-assisted practices while keeping a customer-facing app reliable.
• Build tenant data security into our AI strategy: keep each customer’s data isolated in model context, defend against prompt injection, and lead pen testing and the AI sections of enterprise security reviews.
• Stand up an eval framework and run automated evals on LLM and agent features, tracking quality, latency and cost per tenant and per product.

Lead The Team

• Manage the engineers directly: one-on-ones, pairing, design reviews, feedback on their work, and performance conversations. You are the skip-level for the data science team, which has its own lead.
• Own code review and the QA standard: what has to be true before work is ready for review, what gets tested and how, and where automated and AI-generated tests are required.
• Own the release process: set the standard for how we ship, decide who runs each release, and take responsibility for the outcome of every deploy.
• Help hire as the team grows, shape new roles around what you bring and what the team still needs, and build the culture you'd want to lead.

What We're Looking For

• 10+ years building software, with recent work as a senior, staff, or principal engineer or engineering lead who still ships production code.
• Experience architecting and scaling multi-tenant B2B SaaS, with strong full-stack skills: deep in one modern backend stack, comfortable in a modern front-end framework, fluent in a major cloud.
• Hands-on infrastructure and database ownership in production: deploys, a cloud environment, and relational databases at scale.
• Daily use of agentic coding tools. You can show us how AI has changed the way you build and how you keep quality high while moving fast.
• Experience taking ML or data science work into production.
• Experience managing or leading a small engineering team, including setting a QA and release standard and holding it.
• A builder's bias. You'd rather ship a prototype than write a strategy deck, and you thrive in the ambiguity and pace of an early-stage startup.
• A habit of bringing problems and proposals without being asked. At a company this size, every leader drives their own area.
• Clear communication of technical tradeoffs to product, sales, and customers.

Nice To Have

• ERP, DMS, or CRM integration experience (SAP a plus).
• Background in supply chain, aftermarket parts, manufacturing, or pricing and inventory software.
• Experience shipping LLM or agent-based features to production.
• Founding or early engineer at a venture-backed startup.

What Success Looks Like

First 90 Days:

• Ship at least one meaningful feature end to end in production.
• Own app infrastructure and architectural decisions that enable us to scale efficiently.
• Define the quality standards and process for QA and deployment.
• Lead the engineering team by example in your own work and through strong one-on-one relationships.
• Lead the team's AI-assisted development practice.

Within 6 Months:

• Run a platform that reliably and securely serves a growing base of OEM and dealer tenants.
• Have data science models in production with evaluation and monitoring.
• Have new LLM-powered capabilities in production.
• Lead a team that ships faster, with higher quality, because of how you've coached it.

Why Join PartsPulse

• A live product with paying customers, real data, and a backlog of high-value problems.
• A category-defining platform at the intersection of AI and the industrial aftermarket.
• An experienced founding team, including a 2x exited founder and a product partner fluent in AI.
• Competitive compensation with meaningful equity.

If you love building, want to own the architecture of a real AI product, and want to lead the team that builds it, we'd love to hear from you.