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About This Role
Company Overview
Chipply is the industry's preferred web store platform that gives the power to team dealers, custom apparel decorators, and corporate suppliers to launch online stores with confidence and grow their GMV efficiently.
Primary Purpose and Function
Chipply is hiring an Internal Forward Deployed AI Engineer to embed with our internal teams and ship AI\-powered solutions that reduce manual work, accelerate decision making, and improve productivity across the company. This role reports to the CEO and is responsible for building real, production\-grade engineering work — agents, integrations, and deterministic tooling — directly on top of the systems Chipply already runs on.
This is a hands\-on engineering role, not an orchestration or admin role. You will not be configuring SaaS platforms from the ground up. Instead, you will inspect the systems we already use (HubSpot, Microsoft 365, Confluence, Slack, and others), understand what APIs, webhooks, and MCP surfaces they expose, and partner with each department to design and build the AI agents, integrations, and automations that plug into them. Where AI is the right tool, you build with AI. Where a deterministic solution is better, you build that instead. The judgment to know the difference is part of the job.
This is also an intentionally evolving role. The scope of AI in the business will change as tools, models, and capabilities mature, and you must be comfortable defining the work as you go, reshaping priorities frequently, and building capabilities from the ground up. You will report directly to the CEO and partner closely with leadership, Engineering, Finance, Sales, Marketing, Customer Support, and Product to identify high\-impact opportunities and ship the engineering work that captures them.
Responsibilities
- Design, build, and maintain AI agents, integrations, and automations that reduce manual work, eliminate bottlenecks, and improve productivity across every department at Chipply.
- Inspect Chipply's internal SaaS platforms (HubSpot, Microsoft 365, Confluence, Slack, and others) to understand what APIs, webhooks, and MCP connectors they expose, and use those surfaces to build the integrations each team needs.
- Partner with leadership and department heads to identify and prioritize high\-impact opportunities by mapping existing workflows and pinpointing where engineering effort — AI\-powered or deterministic — will deliver measurable improvement.
- Write production code. Build multi\-tool and multi\-agent systems that connect Chipply's internal platforms into seamless end\-to\-end processes. Make sound judgment calls on when AI is the right tool and when a deterministic solution is a better fit.
- Build evals and monitoring for AI systems you ship, so output quality is measurable rather than assumed.
- Serve as Chipply's internal AI champion by leading education, training, and adoption efforts, and by acting as the go\-to engineering resource for AI questions, best practices, and emerging use cases.
- Establish and maintain responsible AI governance, including guidelines for data handling, privacy, model and tool selection, output quality monitoring, and ethical use.
- Continuously evaluate emerging AI tools, agent frameworks, model providers, and orchestration libraries; pilot promising technologies and recommend adoption decisions based on impact, cost, and fit.
- Measure and report on the ROI of the systems you ship, tracking time saved, error reduction, cost impact, and quality improvements.
- Develop and maintain documentation for the systems, integrations, and AI workflows you build so institutional knowledge scales with the company.
- Serve as a liaison across Engineering, Finance, Sales, Marketing, Customer Support, and Product to align on technology needs and ship workflow improvements end to end.
- Monitor security and compliance across the integrations and AI systems you build, collaborating with engineering and leadership to ensure data protection best practices are followed.
- Continuously redefine the scope, priorities, and deliverables of the role as Chipply's needs and the broader AI ecosystem evolve.
Requirements Qualifications
- Five to eight years of professional software engineering experience, with a meaningful portion of that time spent shipping production code against third\-party APIs and SaaS platforms.
- Strong programming fundamentals in at least one modern language commonly used for AI and integration work (Python and TypeScript preferred). Comfortable writing, testing, deploying, and operating code in production.
- Hands\-on experience building against the APIs of business SaaS platforms — HubSpot, Microsoft Graph / Microsoft 365, Slack, Confluence, or comparable systems. You should be able to read API docs, evaluate auth and rate\-limit constraints, and design a robust integration on top of them.
- Demonstrated experience building with large language models in production: prompt and context design, tool use / function calling, retrieval, evaluation, and cost and latency tradeoffs.
- Hands\-on experience with AI agent frameworks and MCP (Model Context Protocol) connectors, or comparable multi\-step automation that spans multiple systems. You should be able to explain when an agent is the right architecture and when a deterministic pipeline is better.
- Practical experience building with a major LLM provider (Claude, OpenAI, Gemini, Copilot, or similar). Chipply primarily uses Claude, but experience with any production\-grade LLM platform is welcome.
- Familiarity with business process automation tools (HubSpot workflows, Zapier, Make.com, n8n) — not as your primary craft, but enough to know when an off\-the\-shelf automation is the right answer instead of new code.
- Strong problem\-solving and debugging skills, with the ability to diagnose issues across distributed systems and design iterative, scalable solutions.
- Excellent project management skills, with the ability to balance multiple priorities, meet deadlines, and ship continuous improvements without losing the thread on quality.
- Self\-starter who is comfortable with ambiguity, thrives in fast\-moving environments, and is energized by a role whose scope will evolve significantly over time.
- Strong cross\-functional communication skills, with the ability to translate technical
- capabilities into business outcomes for non\-technical stakeholders, and to translate business problems into technical specs.
- Curiosity and discipline to stay current on a rapidly changing AI landscape and translate emerging trends into practical applications for Chipply.
- Excellent interpersonal and collaboration skills, with strong organizational habits and attention to detail.
- Ability to maintain confidentiality and exercise discretion when handling sensitive company and customer data.
- Knowledge of security best practices for API integrations, secrets management, and AI system data handling is preferred.
- Bachelor's degree in computer science, information systems, or a related field is preferred but not required; equivalent professional engineering experience is welcome.
Benefits Company Benefits
- Medical Insurance
- Dental Insurance
- Vision Insurance
- Paid Parental Leave
- 401(k) with Employer Match
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 Chipply, 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.
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.
Chipply AI Hiring
Chipply has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $218,800 across 493 tracked positions. That's 9% above the national 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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