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About This Role
### 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.
Yask is being built out of Gitwit, a venture studio that creates AI\-native companies through deep field research, customer access, and early proof.
We don’t start with ideas.
We start with access to real workflows and urgent problems, then build companies that earn traction from day one.
Yask is one of those companies.
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.### About Gitwit
Gitwit is a venture studio that finds problems worth solving, validates them rigorously, and builds AI\-native companies from the ground up. We invest early, build cross\-functionally, and work to create ventures that earn real traction, not just good stories.
A big part of that comes from working closely with design partners who give us deep access to real workflows, urgent pain points, and early market truth. That helps us find better opportunities and build ventures with stronger validation and traction, faster.
We are on pace to launch 15\+ ventures in the next four years.
That is how we turn insight into products, and products into companies.
Salary Context
This $170K-$195K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Gitwit, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. Disclosed range: $170K to $195K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Gitwit AI Hiring
Gitwit has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bentonville, AR, US. Compensation range: $195K - $195K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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
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