Interested in this AI/ML Engineer role at Access Ingenuity?
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About This Role
About Access Ingenuity
Access Ingenuity is a digital accessibility services company, we make content accessible for people with disabilities. That means braille, audio, and large print documents; PDF remediation; web and mobile accessibility testing; and assistive technology consulting. Our clients are primarily in the SLED and health care market (state agencies, school districts, universities), and our work has real\-world impact for people with disabilities every single day.
We’re a small, focused team, which means interns here don’t shadow. They build.
Overview of the Role
We’re looking for a CS student with an AI\-first mindset to spend the summer building automation workflows and intelligent agents that help our team deliver accessible content faster and more accurately. You’ll work directly with operations and production leadership to identify real bottlenecks, ship real tools, and leave a real footprint.
If you’re the kind of person who reaches for Claude Code before Stack Overflow, thinks in workflows, and wants your internship project to still be running in 2027, this is your role.
What You’ll Be Doing
- Design and build Power Automate flows to streamline production and operational processes across our Microsoft 365 environment.
- Develop and extend SharePoint integrations, document tracking, automated task routing, status dashboards, and team collaboration tools.
- Use Claude Code to build agentic workflows: autonomous, multi\-step tools that reduce manual touchpoints in our accessibility pipelines.
- Prototype and deploy AI agents for intake, routing, quality review, and delivery stages of our alt format and PDF production workflows.
- Collaborate with production team leads to document, test, and hand off your work so it sticks around after the summer ends.
Required Qualifications
- Currently enrolled in a BS program in Computer Science or a closely related technical field.
- Hands\-on experience with Microsoft Power Automate and the Power Platform.
- Comfortable building with SharePoint, lists, libraries, flows, and integrations.
- Hands\-on experience or genuine curiosity with AI\-assisted development tools (Claude Code, GitHub Copilot, Cursor) and an eagerness to use them in real workflows.
- An AI\-first mindset: comfortable experimenting with prompting, tool use, and agentic frameworks to ramp fast and contribute from day one.
- Strong communicator, written and verbal. You can explain what you built and why.
Preferred Qualifications
- Experience building or critically evaluating AI agents using LLM frameworks (Claude, GPT, Gemini, or similar).
- Python scripting and/or REST API integration experience.
- Familiarity with PDFix, ClickUp, HubSpot, or Smartlead and their integration capabilities.
- Exposure to accessibility standards: WCAG 2\.x, Section 508, ADA.
- Interest in making a difference in the lives of people with disabilities.
Why Access Ingenuity
We know you have options. Here’s what makes this different:
- Ownership from day one, you’ll lead your projects, not support someone else’s.
- Your work matters. The tools you build will directly affect how people with disabilities receive accessible content.
- You’ll work at the intersection of two fast\-growing fields: AI/automation and digital accessibility.
- Direct access to leadership, no layers, no bureaucracy. You’ll present your work and get real feedback.
- Flexible schedule designed around your summer. We work with you, not against you.
How to Apply
Send your resume and a brief note (2–3 sentences max) on the most interesting automation or AI tool you’ve built, or wish you had built, to:
[email protected]
Subject line: “Summer 2026 Intern \[Your Name]”
Pay: $25\.00 \- $28\.00 per hour
Benefits:
- Flexible schedule
Education:
- Bachelor's (Required)
Work Location: Hybrid remote in Santa Rosa, CA 95403
Salary Context
This $52K-$58K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Access Ingenuity, 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 $181,170 based on 12,692 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,880. This role's midpoint ($55K) sits 70% below the category median. Disclosed range: $52K to $58K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Access Ingenuity AI Hiring
Access Ingenuity has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Santa Rosa, CA, US. Compensation range: $58K - $58K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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