Interested in this AI/ML Engineer role at Cwill?
Apply Now →About This Role
About CWILL CWILL (pronounced 'quill') is a post-purchase and retention suite built for Shopify brands. Reduce support tickets, recover lost revenue from returns, and turn one-time buyers into loyal fans — with tools purpose-built for every touchpoint that follows the sale.
About the Role As our product portfolio grows, we need new product talent that can learn fast, stay close to users, and help turn real customer problems into great product experiences. Our Product Management Trainee program places you inside one of our product teams, where you'll learn the full PM workflow through real work — not slides and simulations. You'll start by supporting core product tasks, and over time grow into someone who can independently own a product module or direction.
You'll join the AI Chat team. CWILL AI Chat is our AI customer service chat for Shopify stores. It answers shoppers' questions around the clock, from "Where's my order?" to returns, product questions and store policies, and hands off to a human when needed.
Product Intro:
https://www.cwill.com/products/ai-chat/
Base location required:
Cary, NC
(We are unable to hire in other states; relocation will be required.)
Hybrid remote working setting
What You'll DoAI Chat Focus
- Review real conversations, find where the AI misunderstands shoppers or gives wrong answers, and work with engineering on fixes
- Design how the AI handles common intents such as order status, returns, refunds and product questions, including when to ask a clarifying question and when to hand off to a human agent
- Work on how AI Chat uses order, tracking, returns and policy data so answers are accurate and the AI never promises something that has not happened
- Track metrics such as resolution rate, handoff rate, answer accuracy and shopper satisfaction, and help build evaluation sets to check quality before and after each release
Product Requirement Analysis & Solution Design
- Collect and analyze user feedback, customer requests, product data, and market information to identify product improvement opportunities
- Participate in feature planning discussions — ideation, user flows, and solution design
- Under PM guidance, produce requirement documents, user flows, wireframes, functional specs, and acceptance criteria
- Track features from discovery through design, engineering, and launch — make sure problems are clearly defined and solutions land correctly
Product Iteration
- Monitor product data and user feedback after launch; surface issues and propose next improvements
User & Market Research
- Join user interviews and feedback sessions with Shopify merchants and ecommerce brands
- Track competitors in your product's category and produce structured competitive analysis
- Convert user insights and market signals into actionable product recommendations
Cross-Team Collaboration & Execution
- Work closely with engineering, design, QA, operations, and customer success to drive high-quality product delivery
- Proactively track project progress and flag issues early
- Own your tasks with a results mindset — follow through on commitments and complete retrospectives after launch
Continuous PM Development
- Learn product planning, user research, data analysis, and project management through real work
- Conduct retrospectives on the features you ship; build your product judgment through iteration
- As you grow, take on increasing responsibility — eventually owning a product module or workstream independently
Requirements Education & Experience
- Recent graduate from a US university (BS or MS), or within 1–2 years of graduation
- Any major welcome; Computer Science, Information Systems, Business Analytics, Engineering, Economics, or Design preferred
- Internship experience in product management, product operations, business analysis, user research, software development, or a SaaS company is a plus
- Mandarin Chinese communication ability required
Passion for Product Work
- Genuine interest in internet products, SaaS, or the tech industry — you want to build a career in product management
- Sensitive to user experience and product details; you ask "why does this problem exist?" and "how do we make this better?"
- Curious about new technology; willing to learn continuously and move fast
Learning Ability & Logical Thinking
- Fast learner who can absorb new business contexts, product logic, and technical concepts
- Structured thinker — you can break down complex problems, identify the core issue, and form clear conclusions
- Comfortable grounding product decisions in data and user facts
Owner Mindset & Execution
- Strong sense of ownership — you go beyond your assigned tasks and proactively drive resolution
- Results-oriented; when faced with uncertainty, you gather information, coordinate resources, and follow through
- Detail-oriented and thorough — you balance execution speed with delivery quality
Communication & Collaboration
- Strong written and verbal English communication — you can clearly articulate ideas and product thinking
- Collaborative across functions — you build effective working relationships with engineering, design, and business teams
- Open to feedback; constructive and practical in discussions
Nice to Have
- Hands-on experience building with LLMs (prompting, chatbots, agents) or evaluating AI output
- Experience in customer support, conversational design or UX research
- Familiarity with e-commerce or Shopify stores
Benefits
Starting Pay: 60k-85k depends on experiences, open to negotiation
- 401(k)
- PTO
- Paid Holidays
- Insurance
Salary Context
This $60K-$85K range is in the lower quartile 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 Cwill, 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 ($72K) sits 66% below the category median. Disclosed range: $60K to $85K.
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.
Cwill AI Hiring
Cwill has 4 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Based in Cary, NC, US. Compensation range: $85K - $110K.
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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