Founding AI Systems Engineer, Yask

Department: Gitwit

Employment Type: Full Time

Location: Bentonville, AR

Compensation: $170,000 - $195,000 / year

Description

Build the Adaptive Intelligence Behind Physical Operations

Physical operations are where inventory moves, shifts change, timing matters, and small misses quietly become expensive problems.

Stores. Warehouses. Routes. Facilities. The places where execution actually happens.

What makes these environments hard to run well is not a lack of data or effort. It's that what matters changes constantly, responsibility passes across people and shifts, and follow-through gets lost between what someone noticed and what someone else was supposed to do.

Yask exists to close that gap.

We're building an AI system that turns what people observe in the field and what gets buried in business data into the right next action, in the right place, in the right hands, at the right time, and then verifies it got done.

That requires more than prompts and dashboards.

It requires an adaptive system that can learn from messy real-world signals, remember context, orchestrate decisions, and improve from feedback over time.

That is the system you would build.

We are starting in retail and distribution, where this problem is easy to see.

Out-of-stocks alone are an $80B+ annual issue in the US. But the bigger problem is that displays get missed, product doesn't get rotated, follow-through dies in text threads, and store-level issues stay buried in fragmented communication until they become costly.

We already have a live design partner operating across nine locations in Oklahoma, Arkansas, and Texas. We are not guessing where work breaks. We can see it.

You won't be building in a lab.

You'll be building against real workflows, real users, and real operational pressure from day one.

Why This Is a Rare Challenge for a Great Engineer

Most AI engineering roles start with abstract use cases and synthetic data.

Yask starts with operating access.

The retailers, brands, distributors, buyers, and merchandisers whose decisions impact more physical locations than anywhere else in the world, within 30 miles of Bentonville. You will be right in the thick of it.

This is not a role where you optimize prompts in isolation.

This is a role where you:

• Watch how work actually happens
• See where execution breaks
• Design agentic systems, memory layers, and feedback loops that make that work easier
• Ship quickly, measure impact, and make the system sharper from real usage

You will be the engineer who turns a promising AI prototype into a production-ready, adaptive, agent-native platform.

This is a founding, hands-on role. In practice, that means:

• Audit the existing prototype and decide what to keep, what to rewrite, and how to turn it into an MVP architecture
• Design and implement agent orchestration patterns, memory/context layers, and retrieval pipelines for production use
• Build the feedback loops that let the system learn from user corrections and field signals
• Ship production-grade services: APIs, data pipelines, CI/CD, observability, and security fundamentals
• Instrument telemetry so we can see usage quality, drift, task success, and system performance
• Run fast experiments tied to real user behavior and measurable improvement
• Document architecture decisions and create onboarding artifacts for future engineers
• Work in-person with product, research, leadership, and customers to ground technical choices in field reality

If this role is going well, the signs will be visible:

The product will be live inside the design partner's operation.

The system will be getting smarter from usage.

The next customers will be easier to win because the proof is concrete.

This role fits engineers who like being close to the work while it is still unsettled.

People who recognize themselves in several of these:

• You have built and shipped agentic or LLM-driven systems in production
• You understand memory, context engineering, and RAG from experience, not theory
• You've taken a system from prototype to production before
• You are comfortable making pragmatic trade-offs to ship and learn quickly
• You like working directly with product, researchers, and users in person
• You enjoy ambiguity when it's paired with real ownership
• You want to be a founding engineer, not a ticket-taker
• You see Bentonville as a strategic advantage, not a compromise

This role is LESS likely to fit someone who prefers remote-first work, highly structured environments, or narrowly scoped engineering tasks.

What We Offer

This role comes with what strong founding engineers should expect: meaningful ownership, competitive compensation, and the chance to build something real from the beginning.

More importantly, it offers the seat itself:

You get to design the adaptive core of a system that learns from the real world and becomes smarter over time.

Compensation

• Base salary: $170,000-$195,000 (flexible for exceptional candidates)
• Founding-team-level equity
• Relocation support

Benefits

• Health, dental, and vision
• 401(k) with match
• Generous PTO
• Cell reimbursement, parking stipend
• Weekly team lunches
• Dog-friendly office
• Flexible, high-autonomy culture

Location and Work Style

This is a full-time, in-person role based in Bentonville.

We are open to exceptional candidates who would relocate, but this is not a remote or hybrid role. The work requires proximity to customers, product decisions, and the founding team.

If you've been looking for a role where you can apply cutting-edge AI work to messy, real-world execution problems and see the impact of what you build almost immediately, this is that role.

No cover letter. No generic note.

This is the Builder's Application. We think it does a much better job of highlighting your unique capabilities and qualities than a resume.

Don't spend time on polish. Raw, conversational, deeper narrative will give us a better signal of how great you are than a fancy deck. Notes, word doc, markdown file, notion, video - whatever works for you.

Click "Apply Now" to dive in.