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About This Role
Description:About Angel
Angel is the home of stories that amplify light. Millions of people can help choose which films and series we make next, and that changes how the work feels: creators and audiences build each project together, and the story behind the story matters as much as what ends up on screen.
We're a team that cares about work and about each other. If you want to help build something that Amplifies Light, we'd love to meet you. Join us and be part of stories that amplify light!
Summary
This role is the product manager for those tools, and the person who builds and maintains tools and apps for operations teams.
You'll work with leaders to decide what gets built and in what order, write down what each tool needs to do, and then build it yourself using AI coding tools such as Claude Code. After launch you own the product: keep it running, keep its data safe, and improve it as needs change. Your first product will be the internal app that runs our Performance Stock Unit program.
This is an entry-level role for someone with a technical background. You don't need years of experience. You do need to understand what the AI wrote for you, to think like an owner of a product and not only its builder, and to care about the details when the data is pay, equity or personal information.
Done well, this work means non-technical teams get tools that work and keep working, leaders always know what's being built and when, and nothing we rely on depends on one person's memory of how it was made.
Requirements: Expectations of Team Members at Angel
- Amplify light in every action.
- As the owner of your outcomes, you are empowered to break down silos and coordinate with any and all people you need in order to reach your goals.
- You are ultimately responsible for achieving the outcomes while also using resources wisely, whether that be money, people, or time.
- Know your customer. It might be our viewers, investors, filmmakers, or internal Angel teams. Ensure you understand your customer and are providing what they need and not just what you want to build.
Essential functions
- Own the list of internal tools for Operations and People & Culture: what exists, who uses it, and what state it's in.
- Gather requests from leaders, ask questions until the real need is clear, and write short, plain requirements for each one.
- Keep a prioritized roadmap, agreed with the Chief Operating Officer and the Vice President of People and Culture.
- Plan each release, test it with the people who will use it, and train them before it goes live.
- Track how each tool is used and what it saves, and recommend what to improve, replace or retire.
- Build internal apps, automations and dashboards for Operations and People & Culture, using AI coding tools alongside your own judgment.
- Sit with the person who has the problem, ask questions until you understand it, and show a working first version quickly.
- Connect the tools Angel already uses, such as Google Workspace, Slack and our HRIS, so information moves without retyping.
- Recommend when to build, when to buy, and when a spreadsheet is enough.
- Take over and maintain existing internal apps, starting with the Performance Stock Unit program app.
- Test your work before others use it, and fix problems quickly when they appear.
- Keep all code in Angel's shared code repository, with a clear history of what changed and why.
- Write plain instructions for each tool, so someone else could run it or pick it up.
- Handle pay, equity and personal information with complete discretion, and only inside approved systems.
- Set up each tool so people see only what they are meant to see.
- Follow Angel's information security and AI-use policies, and work with Engineering and IT on access, hosting and review.
- Keep a simple, current list of requests, priorities and status that both teams can see.
- Tell leaders early when something will take longer or can't be done the way they pictured it.
- Teach Team Members to use what you build.
If you need an accommodation to do any of this, tell us and we'll work with you.
Competencies
- You understand what you ship. If the AI wrote it, you can still explain what it does and why.
- You listen first, and you ask questions until the real problem is clear.
- You test your own work and assume it can break.
- You can be trusted with anything. Pay, equity and personal information never leave approved systems.
- You explain technical things in plain words.
- You know your limits and ask Engineering for help before a problem gets big.
- You finish: documented, handed over, and still working a month later.
- You take ownership, share context, and are candid with positive intent.
- You care about supporting people and about our mission to amplify light.
- Required education and experience
- A technical background: a degree, coursework, bootcamp or work experience in computer science, information systems, software development or a related field.
- At least one working tool, app or automation you built with AI coding tools, such as Claude Code or Cursor, which you can show us and walk us through.
- Working ability to read and explain code in at least one common language, such as JavaScript, TypeScript or Python.
- Experience using Git and GitHub to save, track and review changes.
- Basic understanding of how data is stored, who can see it, and how to keep passwords and keys out of code.
- Experience gathering needs from non-technical people and turning them into a written plan, in a job, internship or project.
- Preferred education and experience
- Up to 2 years of experience in product management, business systems analysis, software development or IT, including internships.
- Experience maintaining a tool after launch: fixing bugs, handling requests and releasing updates.
- Experience building for non-technical teams, such as People & Culture, Finance or Operations.
- Experience connecting business tools through their APIs, such as Slack, Google Workspace or an HRIS.
- Experience working with confidential information.
- Familiarity with a project tracking tool and with writing user stories or requirements.
- Familiarity with Angel, the Angel Guild, and our body of work (films, series, and other projects).
Work environment
This is an on-site role, and sitting with the teams you build for is essential to it. When in the main office, expect a comfortable, air-conditioned work environment. Team Members are issued their own desks, but the office is an open, shared space and can be fast-paced and occasionally noisy.
Physical demands: This is a desk role. You'll spend most of the day at a computer or in meetings. If you need a reasonable accommodation to do any of that, tell us and we'll work with you.
Travel required: We don't anticipate significant travel for this position.
Work authorization
You must be authorized to work in the United States.
Confidentiality and disclosure
You'll work with confidential Team Member information every day: pay, equity and personal details. Share it only with the people who need it to do their jobs, and follow Angel's confidentiality, insider trading, and information security policies. Angel Studios is publicly traded, so some of what you learn about the business can count as material nonpublic information, which means this role falls under our insider trading policy and any applicable trading window restrictions.
Nothing in this job description, or in any Angel Studios policy or agreement, prohibits or restricts a team member from reporting possible violations of law to, filing a charge or complaint with, or communicating or cooperating with any federal, state, or local government agency or regulator, including the Equal Employment Opportunity Commission, the Securities and Exchange Commission, the National Labor Relations Board, or the Department of Labor, without notice to or approval from Angel Studios. Nothing in this description limits a team member's rights under Section 7 of the National Labor Relations Act, including the right to discuss wages, hours, and other terms and conditions of employment.
Required notices
Angel participates in E-Verify and will confirm your authorization to work in the United States after you accept an offer.
- E-Verify Participation Poster
- Right to Work Poster
- Know Your Rights: Workplace Discrimination is Illegal
EEO Statement
At Angel Studios, we are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants and team members.
Other Duties
Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the team member for this job. Duties, responsibilities and activities may change at any time with or without notice.
Applicants have Federal employment law rights. For more information about these protections, please review the following official posters:
- https://www.dol.gov/agencies/whd/posters/fmla
- https://www.eeoc.gov/poster
- https://www.dol.gov/agencies/whd/posters/employee-polygraph-protection-act
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 ＡＮＧＥＬ, 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 $211,950 based on 13,048 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $111,000.
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.
ＡＮＧＥＬ AI Hiring
ＡＮＧＥＬ has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Provo, UT, US.
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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