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About This Role
*NOTE PLEASE read this entire job description before applying. There are specific instructions inside it, and applications that don’t follow them won’t be considered. This role is all about attention to detail, so this is your first test.
You’ll also need to complete a short 10-minute project (details under “How to apply”). It shows us right away that you have both the willingness and the ability to make this a great fit. **
A bit about us
I’m an entrepreneur running several businesses at once: real estate development and construction, a telehealth company, a medical spa clinic, a digital marketing agency, and a few other ventures. Things move fast, there are a lot of moving parts, and I need one person who can keep it all organized and make things happen.
I’m looking for a Chief of Staff who becomes my right hand. Not someone who waits to be told what to do, but someone who sees what needs to get done, gets it done, and keeps everyone on track.
We use AI and technology across everything we do, so this person needs to be on the cutting edge. You should be the one bringing new tools and smarter ways of working to the table, not the one catching up.
The role
Full time, in person, based out of our clinic office in Sherman Oaks. Monday through Friday during business hours, with some evenings and weekends when things come up. You’ll also be out around LA for meetings, job sites, and errands, so a reliable car is required.
What you’ll be doing
- Running my calendar, inbox, and priorities day to day
- Following up with teams, vendors, lawyers, contractors, and partners across all the businesses so nothing falls through the cracks
- Tracking projects and deadlines and keeping me updated on where things stand
- Using AI tools every day to work faster: drafting, research, summaries, organizing info, and automating repetitive tasks
- Finding and setting up new tools and automations that save time across the businesses
- Handling basic IT: setting up accounts, devices, apps, and integrations, and troubleshooting when something breaks
- Writing emails, summaries, and documents professionally and clearly
- Helping on projects across the businesses, from marketing to clinic operations to construction
- Personal and local errands when needed
What the right person looks like
First and foremost, a great attitude and a real desire to learn. You’ll be jumping between very different businesses every day, and that should excite you, not stress you out.
You’re very organized, sharp, and a strong communicator. You take ownership and follow through without being chased. To show us you read the full description, start your cover letter with the words “Ready to run it all” before anything else.
You’re an expert with AI and VERY tech savvy. You use tools like ChatGPT, Claude, and other AI apps daily and know how to get real results out of them. You stay up to date on what’s new in tech and AI, and you get excited about finding better ways to get things done. You’re comfortable with Google Workspace, Excel, Slack, project management apps, and automation tools like Zapier or n8n.
You must be reliable, mature, and trustworthy. You’ll be handling sensitive business and personal information, so discretion is a must. You show up on time, ready to work, and you care about doing things right.
Requirements
- 3+ years as an executive assistant, chief of staff, operations manager, or similar
- Expert level with AI tools and very strong with computers and technology
- Basic IT skills (account setup, devices, software, troubleshooting)
- Excellent writing skills in English
- Strong with Excel / Google Sheets
- Understanding of basic digital marketing is a big plus
- Reliable car and valid driver’s license
- Must live in or near Los Angeles
Preferred
- AI classes or certifications (please list them in your application)
- Experience with automation tools (Zapier, n8n, Make, or similar)
- Bachelor’s degree
How to apply
Submit your resume through Indeed with a cover letter that includes:
- Your answers to this short project:
- You walk in Monday morning and have three things waiting: a contractor needs an answer on a change order by noon, a patient complaint came in at the clinic, and I’m asking for a summary of a vendor proposal before a 2pm call. Walk us through how you’d handle the morning in 4 to 6 sentences.
- Find 3 private meeting rooms or coworking spaces within 15 minutes of Sherman Oaks that can be booked by the hour. List the name, hourly rate, and link for each.
- Tell us about one AI tool or automation you’ve used to save real time. What was the task and how did you set it up?
- Any AI classes or certifications you’ve completed
- Why you’d be a great fit for this role
- Your required pay
- Links to your social media so we can start getting to know you
Applications missing any of these won’t be considered. If you’ve sent everything, we’ll get back to you quickly.
Job Type: Full-time Schedule: Day shift, on call, weekends as needed Supplemental Pay: Bonus opportunities Experience: Executive assistant, chief of staff, or operations: 3 years (Required) Education: Bachelor’s (Preferred) Certifications: AI certification (Preferred) Location: In person, Sherman Oaks, CA 91403
Pay: $62,100.00 - $74,900.00 per year
Work Location: In person
Salary Context
This $62K-$74K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At ROI ADS LLC, 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 $212,040 based on 13,780 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000. This role's midpoint ($68K) sits 68% below the category median. Disclosed range: $62K to $74K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
ROI ADS LLC AI Hiring
ROI ADS LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Sherman Oaks, CA, US. Compensation range: $74K - $74K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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