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About This Role
Argus HD is a video production and broadcast company, and we're hiring a driven assistant to help run our marketing, automations, and production work with AI. We're a small, tight\-knit, fiercely customer\-first team that produces live events for clients across tech and healthcare, while also running SF Green Screen, our production studio in South San Francisco. Because we're a lean operation, you'll have a real, tangible impact from day one.
This is an entry\-level role with plenty of room to grow. We're looking for someone who's passionate about this work, excited to build, and eager to grow.
Here's the deal: outside of some basic coding, we can teach you the rest. You'll work alongside our senior, experienced team. The one thing we can't teach is passion, so that's what you need to bring.
You'll get clear direction on what good looks like, and real input in shaping how we get there. You don't invent the strategy, you take a clear target and ship excellent work against it, leaning on AI to move fast.
The role is about 75% digital and 25% hands\-on. The hands\-on part is not a desk job. When we're prepping a shoot, we need real help loading gear and being onsite with the crew. Those days tend to be the fun part, and they keep us connected to what we actually do for our clients.
What You'll Do
- Lead the AI: use AI tools and agents to run daily workflows, produce and format content, and build automations. If a task is repetitive, your first instinct is to automate it.
- Build and maintain our websites, automation, etc. You'll do this hands\-on, directing AI agents and fixing what they get wrong.
- Run outreach and lead generation, which naturally flows into our social content. You'll draft, help develop ideas, schedule, and keep it all moving.
- Pitch in onsite and in the shop, including loading and prepping gear (cases can be 50\+ lbs).
What We're Looking For
Two things you need coming in:
- Some basic coding. You can read, edit, and fix JavaScript and Python well enough to update our website, build simple automations, and correct what an AI agent gets wrong. Basic is enough, this does not need to be advanced, but it is required.
- Real passion and drive. You're genuinely excited to build, learn fast, and grow. This is the one thing we can't teach, so it's the one thing you have to bring.
Everything else, we'll grow with you:
- Working with AI. Comfort with AI tools and agents is the job, and it's the core of the day\-to\-day, but if you're early with it and eager, we'll get you sharp fast.
- An eye for marketing. The role is heavy on outreach, which naturally turns into social content. You don't need experience, just enough instinct to tell when the output lands and when it falls flat.
- Interest in our industry. You care about video, broadcast, and live events. You'll be onsite with the crew and the work serves real clients, so caring about the field is what makes the hands\-on days fun rather than a chore. You do not need to be an AV tech, this is not an AV\-first role.
Also: you're a finisher who likes a clear target, you keep a customer\-first mindset, and you're Bay Area based and able to work onsite in South San Francisco. Occasional driving between local production sites may be required. Candidates must have a valid driver’s license and a clean driving record.
The Details
Pay is $28 to $32/hr to start based on experience, with room to grow. Part\-time, roughly 10 to 25 hours per week, with guaranteed hours and scheduling flexibility. Primarily onsite in South San Francisco with event onsite work too. Event hours can vary so we'll need flexibility in your schedule Hourly, Non\-Exempt
To Apply
Cover letter is great if you have it \- or tell us what AI tools you use, what you've automated or built with them, where your interest in video or live production comes from, and why a mix of digital and hands\-on work appeals to you.
Pay: $28\.00 \- $32\.00 per hour
Benefits:
- Flexible schedule
Work Location: In person
Salary Context
This $58K-$66K 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 Argus HD, 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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($62K) sits 66% below the category median. Disclosed range: $58K to $66K.
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.
Argus HD AI Hiring
Argus HD has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in South San Francisco, CA, US. Compensation range: $66K - $66K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% 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 3,823 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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