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Description
Zero-to-One Product Design for AI-Native Ventures
Gitwit is hiring a Product Designer to lead the product experience for new AI companies, and to set the standard of design quality the whole studio builds to.
You will decide what the product should be and design it to a level of finish that holds up in front of real users. This is a seat for a designer with strong product judgment.
This is a senior seat, and the designers who thrive in it tend to share a few things:* 5+ years in product design, hands-on and deep in the craft.
- Experience taking products from zero to one.
- A portfolio of work you can share that showcases your experience.
Most of all, we're looking for someone whose craft and judgment make the people around them better.
Gitwit is an AI-first venture studio built to find problems worth solving, validate them rigorously, and build the companies that earn the right to exist.
We are on pace to launch 15+ ventures in the next four years. For a designer, that means repeated chances to work on the most interesting part of company building: the first version.
You are not maintaining one mature product for years. You are helping create new companies from the beginning, shaping the ambiguity, getting close to users, making the product calls, building the prototypes, launching, reading early signal, and handing off to a founding team.
The pace only works if design can make the first clear product calls before everything is figured out. We need someone who can jump into a half-formed venture, figure out what the product needs to become, and help the team learn before we overbuild.
You come in as a design lead from day one. We launch around five companies a year, and one or two of them will be yours to lead across the year. You own the product experience from early concept through prototype, first launch, user learning, and iteration.
You help set the quality standard here. The ventures you lead carry your fingerprints on how they look, how they behave, and how finished they feel the day they meet their first users. When a founding team inherits a product from you, they should inherit a clear design system, resolved states and edge cases, and specs an engineer can build from directly.
You will work shoulder to shoulder with discovery, product strategy, engineering, brand, and venture leadership to shape the first version of the company and product. The vision is shared. The product experience- how it works, how it feels, where it earns trust, and where it gets out of the user's way- is yours to lead.
Early on, the work is hands-on: wireframing in a room, shaping feature plans, prototyping with AI, sitting with users, and pulling apart what they say, what they do, where they hesitate, and what they actually need.
You are never tossing screens over a wall. You build next to an engineer in tight rotation, make product calls, test them quickly, and keep the distance between decision and working product short.
After the initial product, you stay close to what happens next: what users understand, where they hesitate, what breaks down, and what the early signal suggests, and turn that learning into sharper decisions and better next versions. When the product has enough shape and the right founding team is ready to carry it forward, you make the handoff clean, then move to the next early product problem.
What You’ll Do
- Set and hold the visual and interaction quality standard for the studio's products.
- Lead zero-to-one product design for new AI products, from blank canvas through first launch and iteration.
- Resolve the hard parts: the states, edge cases, and interaction details that make a product feel finished.
- Design AI workflows where the product may not behave the same way twice, changing outputs, wrong or uncertain answers, human review, approval moments, fallback paths, and clear next steps.
- Generate multiple design directions, weigh tradeoffs, make the call, and explain the reasoning clearly.
- Prototype quickly to help the team learn what is real before overbuilding.
- Translate user observation, research, and usage signals into product hypotheses, experiments, and design decisions.
- Work tightly with product strategists, researchers, engineers, designers, and venture leaders to turn a promising idea into something real enough to test.
- Bring discipline and meticulousness to how design gets built across the studio, so each product does not have to reinvent the basics from scratch.
- Help the studio get smarter with each AI product by capturing what we learn about review, correction, approval, uncertainty, failure states, and user trust, and sharing it so each new venture starts sharper than the last.
In your first few months, you will have:* Stepped into an active venture, understood the product direction quickly, and identified the first design moves that would reduce risk, test key assumptions, and help the team move faster.
- Brought a venture's product from rough to genuinely polished, with screens you'd be proud to put in your own portfolio.
- Taken ownership of a new venture concept from the beginning, shaping the workflow, product direction, prototype, and first testable version.
- Created the early design system, flows, states, and specs engineers need to build quickly and consistently without guessing.
- Helped run lightweight user tests or experiments with discovery and product strategy to validate core product assumptions, and converted early signal into prioritized design iterations.
You don't need to be a machine learning expert, but you should be genuinely curious about how AI is reshaping product design.
A lot of this work is not about adding AI to old software patterns. It is about rethinking the workflow itself: what the user gives the system, what the system does on its own, when the human needs control, where trust breaks, and what the product should do when the AI is wrong.
In one venture, that might mean designing how a user reviews and corrects an AI-generated recommendation. In another, it might mean replacing a dashboard with a workflow that quietly watches, decides, asks for approval, and acts.
These are hard things to make feel simple, obvious, and trustworthy. That is a craft problem as much as a strategy problem, and it is the part of the work we care most about getting right. We want a teammate who finds that shift exciting and has strong opinions about how the design job itself is changing.
The Kind of Designer We’re Looking For
We are looking for someone who came up as a strong designer and grew into product judgment. If you see yourself in most of these, we should talk.* Craft and finish. Your shipped work shows real command of typography, layout, hierarchy, color, and interaction, taken all the way to resolved rather than roughly right. You know what good looks like and can back it up.
- Zero-to-one ownership. You have taken ambiguous product ideas from early concept to launch for real businesses, and you can explain the tradeoffs that shaped the product.
- Product judgment under uncertainty. You can generate multiple directions, pick one, explain why, and define what would prove it wrong.
- AI-native interaction thinking. You are already thinking beyond forms, dashboards, and deterministic flows toward workflows where AI handles work, exposes uncertainty, earns trust, and knows when to ask for human judgment.
- Research-to-product synthesis. You can point to times when interviews, observations, or usage signals changed the product direction, and explain why.
- Clear cross-functional communication. You bring engineers, strategists, researchers, and venture leaders into the thinking so decisions do not feel mysterious or disconnected.
You should be the kind of designer who already has these instincts and finally wants a seat that lets you use them.
This is not a clean-lane design job. If you want polished requirements, a tidy backlog, long cycles, layers of review, and a roadmap handed down from somewhere else, this will feel uncomfortable. We move quickly, with incomplete information, high trust, and a lot of direct collaboration.
And this is not a brand design role. You do not need to invent a full identity system from scratch. But because our ventures are new, you do need to care about how the whole company shows up: a deck, an early website, a prototype, or how the product gets explained when it helps the venture become real.
The product experience is where our standards are highest.
What We Offer
This role comes with the things strong designers should expect: competitive salary, strong benefits, and meaningful equity in every venture the studio builds, but the real offer is bigger than a comp package alone.
You get a rare zero-to-one seat in an AI-first venture studio built to launch multiple companies, not just maintain one. You get the intensity and authorship of early-stage building, with the structural advantages of a studio: stronger validation, shared resources, repeated shots on goal, and the chance to help build multiple high-impact ventures over time.
Practically, that includes:* Competitive salary
- Meaningful equity in every venture the studio builds
- 8 weeks off per year: 5 weeks vacation + 3 weeks paid holidays
- Excellent health, vision, and dental insurance, with 99% of employee premiums paid
- 401(k) with 4% match
- Life insurance
- Paid parental leave
- Cell phone reimbursement
- Monthly parking stipend
- Weekly team lunches
- Dog-friendly office
- High-trust, high-autonomy culture
- The chance to work on multiple high-impact ventures each year
- Your own private office, and collaborative workspaces in one of the coolest office buildings in Tulsa
The package matters. But for the right person, the bigger offer is where you get to place your next years.
This is a full-time role built around real collaboration (with room for real life).
This role is based in Tulsa or Bentonville. We're open to exceptional candidates who would relocate for the right opportunity, but this is not a remote role. We want designers who want to build closely with the team in the room, in the work, and in the real decisions that shape what gets built.
That being said, we are very flexible with real life. Some days you might need to work from home, for when the plumber shows up, to step out for a kids' appointment, or just for a day of heads-down building. What we can't flex on is the overall proximity and regular in-person presence that keeps the studio engine running fast.
Send us a link to your portfolio or website, along with your resume.
Then answer one question:
Pick one project from your portfolio and tell us about a decision you'd make differently if you did it over, and why.
Keep it short and honest. We care more about how you think than how polished the answer sounds.
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 $140K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($155K) sits 27% below the category median. Disclosed range: $140K to $170K.
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.
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: $170K - $170K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 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
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