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About This Role
About Yumwoof
At Yumwoof, we're redefining pet wellness with science\-backed, anti\-inflammatory dog food made from real ingredients. We operate as a lean, fast\-moving team where speed, craftsmanship, and pragmatic problem\-solving drive our growth.
As our AI\-Native Senior Fullstack Developer, you won't just write code—you will own products end\-to\-end. You will serve as the core technical engine for Yumwoof, translating high\-level business goals directly into robust, production\-ready software. By pairing elite fullstack engineering fundamentals with an AI\-first workflow and exceptional communication, you will eliminate administrative middle layers and ship feature\-rich experiences that directly impact thousands of pet parents.
- Lead with Impact: Directly drive revenue, conversion, and operational capability by shipping features across our entire digital ecosystem—from e\-commerce touchpoints to backend APIs.
- Ship with Autonomy: Own features from initial napkin sketch to production. You will interact directly with business stakeholders, scope requirements cleanly, and execute without handoff friction.
- Leverage AI at Scale: Operate as an AI\-native builder—using advanced AI workflows (Cursor, Copilot, custom agents, LLM code generation, and automated spec drafting) to multiply your personal output and maintain an elite execution velocity.
Role Overview
We are seeking a versatile, self\-directed developer who thrives on end\-to\-end ownership. You will bridge the gap between business goals and technical architecture. Rather than relying on project managers or analysts to translate requirements or clear friction, you bring the commercial acumen and communication skills required to scope work directly with non\-technical team members, set pragmatic expectations, and build elegant solutions fast.
You will operate at the intersection of product, engineering, and AI automation—maximizing feature throughput while keeping system architecture clean, scalable, and maintainable across our full technology stack.
What You’ll Do
- End\-to\-End Feature Ownership: Take complete accountability for features across the lifecycle—collaborating on initial business requirements, architecting database models, building APIs, developing UI components, and deploying to production.
- Direct Stakeholder Collaboration: Act as a proactive technical partner to marketing, operations, and leadership. Translate business objectives directly into technical solutions without relying on intermediaries or complex bureaucratic handoffs.
- AI\-First Engineering: Embed modern AI tools natively into your day\-to\-day work (spec generation, rapid prototyping, code drafting, refactoring, test coverage, and debugging) to operate with the output capacity of a multi\-person engineering pod.
- Fullstack Architecture \& Maintenance: Write clean, maintainable, and performant code across our core stack (Shopify Liquid, Python/FastAPI, React, TypeScript, and PostgreSQL).
- Pragmatic Requirement Scoping: Self\-manage feature specs, backlog priorities, and technical trade\-offs. Turn high\-level ideas into clear technical plans using AI tools to speed up pre\-development planning.
- Cross\-Functional Communication: Frame technical constraints, tradeoffs, and timelines in clear, business\-focused language, ensuring all stakeholders are aligned on project status and deliverables.
What We're Looking For
- Core Technical Experience: 4\+ years of hands\-on fullstack software engineering experience in fast\-paced DTC, E\-commerce, or high\-growth startup environments.
- Proven Fullstack Mastery: Deep proficiency with our core tech stack:
- Frontend: React, TypeScript, and custom Shopify Liquid theme development.
- Backend \& Data: Python (FastAPI framework), RESTful APIs, and PostgreSQL database management.
- AI\-Native Development Expertise: Demonstrated mastery of modern AI coding environments (e.g., Cursor, GitHub Copilot, Claude/GPT\-4 for development, advanced prompting for architecture, and automated workflow agents) to significantly compress delivery cycles.
- Exceptional Communication \& Commercial Acumen: Strong articulate communication skills. You can explain complex technical concepts simply, set clear cross\-functional boundaries, and align technical choices with business revenue goals.
- Product \& Business Mindset: You build for the user and the business outcome, not just for technical perfection. You understand e\-commerce fundamentals (conversion rate, subscription workflows, order fulfillment APIs).
- Autonomy \& Systems Thinking: Self\-directed worker who proactively identifies system bottlenecks, cleans up technical debt, and improves overall developer workflows without needing step\-by\-step management.
Time Commitment
- Part\-time: 20 hours per week
- Remote: US Central Time Work Hours
Why Join Us
At Yumwoof, we're redefining pet wellness through science\-backed, anti\-inflammatory recipes made with real ingredients.
We offer:
- Competitive pay and a role with real technical ownership from day one
- 100% remote working environment
Be Part of a Mission That Matters: Help dogs live longer, healthier lives through real ingredients, rigorous science, and category\-defining product experiences.
Be the Ultimate Builder: Your code, architectural decisions, and product intuition will directly shape our platform. You won't be checking boxes or waiting on ticket handoffs—you’ll be shipping high\-impact features end\-to\-end.
Build Systems That Scale: Architect and scale the digital infrastructure that allows a high\-output, AI\-native technical engine to deliver massive results. You are building the scalable technical foundation our platform will grow on.
Join an Elite, Autonomous Team: Work alongside a passionate, ambitious team focused on driving real business outcomes without bureaucratic bloat or unnecessary middle\-management drag.
Pay: $2,000\.00 \- $3,500\.00 per month
Application Question(s):
- Tell us about a complex, data\-driven feature you owned end\-to\-end in a previous DTC or e\-commerce environment. How did you approach designing the database schema (PostgreSQL), building out the API layer (Python/FastAPI), and executing the user experience (React/TypeScript or custom Shopify Liquid)?
- Describe a recent fullstack feature or project where you actively engineered your AI workflow (e.g., using Cursor, custom LLM agents, automated spec drafting, or multi\-prompt architecture pipelines) to drastically compress the delivery cycle. What did your specific AI\-human loop look like? Where did the AI excel, where did it hallucinate/fail, and how did your elite engineering fundamentals steer it back on track?
- How many years of experience do you have with React/TypeScript, and have you built or modified custom Shopify Liquid themes?
- How many years of experience do you have with Python (specifically FastAPI) and writing raw or ORM\-driven queries in PostgreSQL?
Work Location: Remote
Salary Context
This $24K-$42K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Yumwoof Natural Pet Food Inc., 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($33K) sits 85% below the category median. Disclosed range: $24K to $42K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Yumwoof Natural Pet Food Inc. AI Hiring
Yumwoof Natural Pet Food Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $42K - $42K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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