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About This Role
About Atmosphere TV
Atmosphere is the leading streaming TV platform built specifically for businesses. Unlike ad networks or signage companies, Atmosphere is the only true TV company whose first priority is to entertain television audiences outside of the home. Our content is designed to be fun, engaging, and worth watching, transforming waiting rooms, gyms, bars, and restaurants into better experiences for customers and better businesses for owners.
We are the first and only company to think about both the business owner and their customers when creating TV content. With 55,000+ venues and a global audience of over 150 million monthly viewers, Atmosphere is redefining what TV means outside the living room.
About the role
We're looking for an AI Enablement Lead to turn Atmosphere's existing AI tooling into workflows that give our internal teams back time for creative problem-solving, client relationships, and strategy. Our product and engineering team already builds with AI every day; this role brings that foundation to the rest of the company.
You'll sit side-by-side with internal teams, map how their work actually flows, find where it slows down, and build tools that fix it, whether that's a custom AI skill, an automated assistant, or an integration. You'll build, test, and refine alongside the people who use the tools. You'll start with our ad sales team, then bring what you learn to other departments.
This role is equal parts team advocate, systems builder, and practical coach. You'll need the empathy to understand user pain points, the judgment to tell whether a problem needs better process design or a new tool, and the hands-on technical skills to build solutions people actually want to use.
This position reports to the Chief Product & Technology Officer and is on-site in Austin, TX. In-office availability 4–5 days a week is required.
What you'll do
- Embed with internal teams to map their workflows end to end, find the bottlenecks, and tackle the highest-impact problems first.
- Tie every build to a measurable business outcome, such as lower cost-per-revenue or faster sales outreach, and work backward from that metric.
- Design and build agentic workflows on our Claude plugins, connectors, and Salesforce stack, pairing live with users to solve problems in the moment rather than through a ticket queue.
- Build tools that fit into the way people already work, and provide one-on-one coaching where a tool alone isn't enough.
- Capture team inputs alongside final approved work and use that data to refine prompts, rules, and tool behavior over time.
- Partner with engineering to decide what belongs in shared core architecture versus team-specific tooling, keeping our tech stack clean and unified.
- Advise teams across departments on complex operational problems, including whether the right fix is better process, new software, or AI.
- Document experiments, results, and reusable patterns so each new team rollout moves faster than the last.
Qualifications* A track record of shipping AI workflows that non-technical users adopted, and the ability to explain what didn't get used and why
- Working understanding of agent loops, including how to design skills and tools and connect them to external APIs
- Hands-on experience with current AI tooling (Claude skills, plugins, MCP, or similar)
- Enough Python or TypeScript to build integrations yourself
- Product instincts: you frame the problem, pick the metric, prioritize hard, and stay on one bottleneck as long as the leverage is there
- Experience improving operational processes, or a demonstrated knack for untangling messy workflows
- Strong people skills, including the ability to bring busy, skeptical users along rather than running past them
- Clear writing across specs, prompts, and write-ups
- A bias toward simplicity and toward tools that disappear into the work
Nice to Haves:
- Operational exposure to sales teams
- Salesforce or Gong automation experience
- Experience building evaluation or feedback tooling for LLM outputs
- Background as a founder, early employee, forward-deployed engineer, or GTM engineer
- Experience with programmatic document generation (python-pptx or similar)
Compensation & Benefits:
- Competitive salary
- Company Equity
- Company 401(k) with employer matching
- Competitive insurance plans
- Flexible Time Off Policy
Applicants must be legally authorized to work in the United States without the need for current or future employer sponsorship.
Our Commitment to Diversity:
Don't meet every single requirement? Research shows that women and underrepresented groups often hesitate to apply unless they meet all the criteria. At Atmosphere, we're committed to building a diverse, inclusive team where creativity, innovation, and teamwork thrive. If you're excited about this role but your experience doesn't perfectly align with every qualification, we still encourage you to apply—you might be the right fit for this or another role.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Atmosphere TV, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Atmosphere TV AI Hiring
Atmosphere TV has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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