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About This Role
Role
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ACQ Media is the in\-house media engine behind Acquisition.com, producing long\-form series, podcasts, and daily social content at studio scale for millions of viewers. We are building an AI\-native production system that makes our team measurably faster, more consistent, and more capable than any team operating without it.
The Senior AI Engineer, Media is the technical builder who ships that system. You design, construct, and maintain the agentic workflows, internal AI tools, and generative media pipelines that power every department in our studio operation. This is a production engineering role: prototype rapidly, but ship to a bar production teams can rely on every day.
This is a senior individual\-contributor engineering role. You are not managing a team, and you do not own the strategic roadmap — the Head of AI does. You make architectural and shipping decisions inside that roadmap, flag what will not work, and ship on a timeline production can plan against.
You report to the Head of AI, Media with a dotted line to the CTO. A second hire — a generative\-media specialist focused on the creative tooling layer — will follow this one and partner with you on the deepest creative work.
Responsibilities
====================
- Design, build, and maintain agentic AI workflows, owning the full lifecycle from prototype through production monitoring. Framework choice is yours — LangChain/LangGraph, CrewAI, Google ADK, Anthropic’s managed agents, OpenAI Agents SDK, or no framework at all if that is the right call
- Build and operate generative media pipelines (video, audio, captioning, thumbnails, long\-form to short\-form repurposing) using ComfyUI, Fal, and direct model APIs. You will partner with the generative\-media specialist on the deepest creative\-tooling work once that role is filled
- Ship internal AI tools and dashboards (including search, retrieval, and MCP\-exposed surfaces) that give Post, Art, Content, and Marketing teammates direct access to AI workflows without engineering support
- Build evaluation, monitoring, and failure\-recovery systems for every workflow you deploy, including managed compute, model hosting, and API cost optimization
- Partner with the CTO and Tech team on infrastructure, deployment, security, and IP and data handling standards across all AI systems
- Translate department pain points into engineered solutions with measurable outcomes, train the receiving teams, and document every system you ship
Requirements
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- 5\+ years building production software, with strong Python, API design, databases, and cloud infrastructure fundamentals. Enough frontend competence to ship a usable internal tool without a dedicated frontend engineer
- 2\+ years building production agentic AI systems — framework\-based (LangChain, LangGraph, CrewAI, Google ADK, Anthropic managed agents, OpenAI Agents SDK, AutoGen, or equivalent) or framework\-free with custom orchestration. Hands\-on experience with RAG (including multi\-modal), evaluation, and prompt engineering
- Strong instincts for agent\-tool interface design. Direct MCP experience is a plus; the bar is the ability to reason clearly about tool surfaces, schemas, granularity, and statefulness
- Proven track record building internal tools, dashboards, and API integrations that non\-technical users actually adopt. Comfort partnering directly with creative roles — editors, writers, producers, designers — and translating their workflows into systems
- Demonstrated ability to operate in ambiguity, prototype rapidly, and ship production\-grade systems without a fully scoped specification
Preferred (not required)
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- Direct, professional experience running media production AI workflows with tools like ComfyUI, Fal, and similar generative platforms (including video diffusion, LLMs, and audio generation models)
- Experience working inside or directly supporting media, video production, or content operations teams
Results
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- Two or more production\-grade AI workflows deployed and actively used by department teams within 90 days, with monitoring and evaluation in place. The first will be a multi\-modal search and retrieval tool for our long\-form content library, surfaced both as a web portal and an MCP server
- Measurable reduction in turnaround time for at least one major production pipeline (editing, thumbnails, repurposing, or captioning) within 90 days
- Within 12 months, the AI systems you ship are demonstrably enabling content output to scale faster than headcount on the production teams using them, measured against pre\-launch baselines
- Within 12 months, external AI agency and contractor spend is reduced to near\-zero for work that falls within the systems you have shipped (excludes foundation model API costs)
- Every deployed system has documentation, a named owner on the receiving team, a success metric, and a maintenance plan
Schedule
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- Standard business hours, Pacific or Mountain time alignment preferred for studio overlap, with occasional flexibility for production deadlines and launch windows
Location
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- Full\-time, remote (US\-based)
- Light travel may be required for studio events
Compensation:
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- $175,000 \- $200,000 base salary
- The salary range may be inclusive of several levels that would be applicable to the position. Final salary will be based on a number of factors including, level, relevant prior experience, skills, and expertise. This range is only inclusive of base salary, not benefits (more details below).
Benefits:
=============
We offer a comprehensive, evolving benefits package designed to support your health, family, and wellbeing. Some key offerings:
- Flexible Unlimited Paid Time Off and Company\-wide Holidays
- Employer sponsored Medical, Dental, \& Vision plans
+ $1,950 annual Employer HSA contribution
+ FSA options including dependent care
+ Employee assistance program and mental health resources
- Employer match program for 401(k), eligible for both Traditional and Roth accounts
- $1,200 annual wellness reimbursement through JOON that supports health, family care, pet care, fitness, and more!
- For local or visiting team members, enjoy access to a state of the art gym at our HQ in Las Vegas
- *Benefits eligibility applies only to full\-time roles.*
ACQ Core Values:
====================
Our core values are the heart and soul of this incredible company. The right person for this role will appreciate each of these values, personally subscribe to them, and understand why each is critical to having a great business.
Competitive Greatness
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Be at your best when your best is needed. Enjoyment of a hard challenge. Those who have the drive to constantly improve, the superior intellect and long term commitment to see incremental improvements become compounding returns.
Sincere Candor
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Have the self awareness to accurately perceive and communicate hard truths that improve others and self, the courage to do so, and the humility to accept them, even when it hurts. Nothing great can be built without feedback: internally or externally.
Unimpeachable Character
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Be the type of person with whom people are always proud to associate, personally and professionally. We look for true alignment of thoughts, words, and actions towards a goal worth pursuing.
Compensation Range: $170K \- $200K
Salary Context
This $170K-$200K range is above the median 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 Acquisition.com, 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. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $170K to $200K.
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.
Acquisition.com AI Hiring
Acquisition.com has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $200K - $200K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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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