Interested in this AI/ML Engineer role at TubeScience?
Apply Now →Skills & Technologies
About This Role
- Senior AI Workflow \& Systems Engineer
Build and run the AI infrastructure that powers every team at TubeScience.
️ Role: Senior AI Workflow \& Systems Engineer
Location: Remote (Los Angeles based preferred)
Compensation: Remote $70,000–$120,000 \| Los Angeles $110,000–$160,000
Reports to: VP of IS
Team: Information Systems
About TubeScience
TubeScience is a data\-driven creative studio producing performance advertising at massive scale — and we're growing fast. We're looking for a Senior AI Workflow \& Systems Engineer to be the most technically sophisticated AI builder in the company. You'll sit in IT but serve everyone — owning the infrastructure, deployments, and systems that make our AI initiatives real, and unblocking every team that's building on top of them.
The Role
This is a systems and deployment role for someone genuinely excited about where AI is taking enterprise engineering. You won't just design workflows — you'll own the infrastructure they run on, keep them running reliably, and be the expert other teams call when things break or they hit a wall.
You are the architect, the deployer, the maintainer, and the unlocker — all in one. When there's no PM driving an AI initiative, you'll step in and own it end\-to\-end.
What You'll Own
AI Workflow Engineering
- Build and deploy LLM\-powered applications and agent\-based workflows that eliminate manual effort across the company
- Design multi\-step agentic pipelines — tool use, RAG, structured outputs — built for production, not demos
- Integrate AI workflows with TubeScience's existing systems via REST APIs, webhooks, and custom integrations
- Develop automation pipelines
- Evaluate emerging AI tooling and own build\-vs\-buy decisions
️ Infrastructure \& Deployment
- Own deployment and management of AI workflows and applications on Vercel and cloud platforms
- Build and maintain the infrastructure that supports TubeScience's AI initiatives — including cloud\-based agents, serverless functions, and supporting services
- Design for resilience: logging, error handling, alerting, and monitoring across all deployed systems
- Manage secrets, environment configs, and deployment pipelines across environments
- Align with engineering on architecture, scalability, and infrastructure decisions
Cross\-Functional Enablement
- Serve as the go\-to technical resource for teams across TubeScience building AI\-powered workflows and apps
- Deploy, maintain, and improve departmental AI tools — owning the full lifecycle from build to production
- Debug and unstick builders across the company when they hit technical walls
- Translate team\-specific business needs into precise technical requirements and actionable solutions
- Serve as final escalation for complex AI and systems issues teams can't resolve on their own
Ownership \& Improvement
- Proactively audit AI systems and workflows for reliability issues, inefficiencies, and improvement opportunities
- When there's no dedicated PM on an AI initiative, step in: define the problem, scope the solution, and drive it to completion
- Prototype emerging AI tools and frameworks and bring the best ones into TubeScience's stack
- Document every system thoroughly so the company can run it confidently
What We're Looking For
Background \& Experience
- 4–6\+ years in software engineering, DevOps, or systems engineering — with hands\-on AI/ML experience
- Strong foundation as a software, systems, or DevOps engineer who has grown into AI — not the other way around
- Proven experience deploying and managing production applications on Vercel, AWS, GCP, or equivalent
- Hands\-on with LLMs, generative AI, and orchestration tools (n8n, Make, Zapier, LangChain, or equivalent)
- Proven REST API integration experience with solid edge\-case handling
- Experience building or maintaining cloud\-based agents and serverless infrastructure
Technical Skills
- Strong Python and/or JavaScript/Node.js — clean, production\-grade code
- Solid understanding of deployment pipelines, CI/CD, environment management, and secrets handling
- Experience with vector databases and embedding\-based retrieval
- Comfortable with cloud infrastructure (AWS and/or GCP) and cloud\-native application patterns
- Familiarity with monitoring, logging, and alerting for production systems
Soft Skills
- Highly autonomous — identifies problems and ships solutions without waiting to be asked
- Effective communicator across technical and non\-technical audiences
- Strong product instincts: can step into ownership of an initiative when there's no PM in the room
- Calm under pressure; reliable when other teams are blocked and need answers fast
- Comfortable working across many different teams and problem domains simultaneously
➕ Bonus Points
- Experience with AI agent frameworks
- Background in high\-volume performance advertising, media, or creative production
- Experience with AI in a production context
- Multi\-step agentic pipeline design or large\-scale workflow orchestration
- Experience with data pipelines or BI tooling
✨ Benefits
Health, Vision \& Dental coverage
Unlimited PTO
401(k) \+ Matching
Life Insurance
Paid Sick Days
Paid Parental Leav
Salary Context
This $110K-$160K 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($135K) sits 25% below the category median. Disclosed range: $110K to $160K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.