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About This Role
AI Tools Associate — Creative Strategy
Remote \| Full\-time \| $65,000–$85,000 \+ performance bonus
We're building AI that makes great advertisers even better. You'll build the tools that make it happen.
TubeScience is one of the fastest\-growing performance video companies in the world. We don't just make ads — we engineer them. Data, AI, and creative systems working together to produce direct\-response video at a scale and speed nobody else has cracked. We're profitable, we're growing, and the problems we're solving are genuinely hard.
Our Creative Strategy team is in the middle of building an AI\-powered infrastructure that changes how strategists work — from how they plan campaigns to how they write briefs to how they evaluate creative. We need someone to build it.
What you'll actually be doing
You're not a strategist who uses AI on the side.
You're the person who builds the systems strategists depend on.
That means developing LLM\-based tools, automating data pipelines, and creating workflows that make a team of 50 people measurably faster and more consistent. You'll work directly with TJ, our VP of Creative Strategy, who sets the direction — and with Audrey, our Creative Systems Manager, who owns rollout.
You own the build.
Day to day, you'll be:
- Building and shipping AI tools — roadmap generators, brief scorers, hook writers, sprint planners, competitive intelligence tools. Real tools, used daily, with real usage metrics.
- Owning the data pipeline — turning raw performance data, consumer reviews, and competitive intel into structured inputs that feed every downstream tool. Python scripts, API integrations, SQL queries — you'll write and maintain all of it.
- Managing the AI architecture — client\-specific Claude Projects, skill files, knowledge bases. Every account should eventually have custom AI context that makes strategy work faster.
- Driving adoption — a tool nobody uses is a tool that doesn't exist. You'll train the team, gather structured feedback, track usage, and iterate until it sticks.
You're a fit if
- You're a strong Python developer comfortable writing production scripts, not just prototypes.
- You've worked with APIs (REST, JSON) and can integrate LLM APIs without hand\-holding — experience with OpenAI or Anthropic is a plus.
- You're comfortable with SQL at a working level — pulling data, writing joins, debugging pipeline issues.
- You can get something deployed (Vercel, Railway, or equivalent) independently.
- You learn fast in new domains — you don't need to know advertising coming in, but you need to pick it up quickly.
- You ship things. You document things. You own things.
You're not a fit if
- You want to experiment and prototype indefinitely without shipping.
- You expect a ticket queue and explicit instructions for everything.
- You're looking for a pure engineering role with no business context.
What's in it for you
Beyond the base salary, your bonus is tied directly to impact — not time served.
For every major tool that reaches sustained weekly active users on the team, you get paid. Ship tools people actually use, and you'll earn meaningfully more.
- Base: $65,000–$85,000 depending on experience
- Tool impact bonus for each qualifying tool with 5\+ sustained weekly active users
- Additional bonus at 10\+ weekly active users
- Targets: 2 qualifying tools in your first 6 months, 5\+ by end of year one
The honest pitch
This role has a steep learning curve.
You'll be picking up prompt engineering, advertising strategy vocabulary, and a proprietary methodology while simultaneously building and shipping tools the team uses every day. It's not for everyone.
But if you want to build AI infrastructure that has measurable impact on a real business — and you want to learn direct\-response advertising from the inside while doing it — this is a rare opportunity to do both at once.
Sound like you? Apply here
Salary Context
This $65K-$85K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At TubeScience, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($75K) sits 58% below the category median. Disclosed range: $65K to $85K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
TubeScience AI Hiring
TubeScience has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $85K - $160K.
Location Context
AI roles in Los Angeles pay a median of $189,000 across 1,686 tracked positions. That's 6% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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