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About This Role
Description: What we do
US Signal is a leading data center services provider, offering secure, reliable network, cloud hosting, colocation, data protection, and disaster recovery services — all powered by its expansive, robust fiber network. We also help customers optimize their IT resources through managed services and professional services that scale with their business.
The opportunity
US Signal is seeking a curious, motivated AI Orchestration Engineer Intern to join our core AI team. You will work directly alongside our AI Infrastructure team, gaining hands\-on exposure to how modern AI applications are actually built and shipped.
This is not a coffee\-fetching internship. You will write real code that makes it into our codebase, contribute to agentic workflows, and grow into a reliable contributor over the course of the program.
About the role
As an AI Orchestration Engineer Intern, you will take on scoped, meaningful engineering tasks within our agentic application development efforts. You'll learn how Small Language Models (SLMs) and stateful agents come together inside a structured SDLC, build features that real users rely on and develop your engineering skills under the direct guidance of senior engineers who are invested in your growth.
Success in this role means shipping clean, tested code, asking sharp questions, and leaving the codebase better than you found it.
What you'll do
- Application Development: Build and maintain features for our agentic applications, writing clean, tested, and well\-documented code under the guidance of senior engineers.
- Agentic Workflow Support: Assist in developing and debugging components of our agentic workflows, learning how stateful agents are structured, deployed, and monitored.
- Tooling \& Automation: Write scripts and small utilities that improve developer experience, automate repetitive tasks, and support the team's CI/CD pipelines.
- Testing \& Quality: Contribute to unit and integration tests, help triage bugs, and participate in code reviews to learn and uphold team standards.
- Documentation: Keep technical documentation current as features evolve, making it easier for the whole team to move quickly.
Requirements: Who you are
You thrive in a smaller, agile team and treat every code review and bug as a chance to level up. You're comfortable not knowing everything yet — you'd rather ship something imperfect and iterate than wait for perfect conditions. You ask good questions, accept feedback well, and can clearly explain what you're working on and where you're stuck.
You're a builder at heart, and you're genuinely excited about what's possible with AI.
What you bring to the team
- Solid foundation in at least one modern programming language (Python strongly preferred), including data structures, control flow, and basic algorithms
- Comfortable with Git and collaborative workflows (branches, pull requests, code review)
- Familiarity with LLMs/SLMs and genuine interest in agentic systems — production experience not required, but eagerness to learn is
- Some exposure to Docker, REST APIs, or cloud environments is a plus; willingness to learn quickly matters more
- Currently pursuing or recently completed a degree in Computer Science, Software Engineering, or a related field
Core competencies
- Simplify: Problem Solving, Analytical Thinking
- Put the Customer First: Collaboration, Service Orientation
- Act Like an Owner: Accountability, Initiative
What we offer
In return for your contributions, you'll enjoy a supportive team environment, along with:
- Paid internship with competitive hourly compensation
- Hands\-on mentorship from senior engineers and AI/ML practitioners
- Real project ownership — your code ships
- Exposure to a modern AI/ML tech stack in a production environment
- Business casual work environment
Compensation
The anticipated hourly rate for this position is $20/hour.
Working conditions and physical demands
This position may be performed in either a standard office or home office environment. It requires prolonged periods of sitting, frequent use of a computer and other office equipment, and effective time management in a self\-directed setting.
All US Signal employees are expected to comply with information security policies to ensure the confidentiality, integrity, and availability of company and customer data, as well as applicable state and federal regulations.
We are interested in every qualified candidate eligible to work in the United States. Visa sponsorship is not available at this time.
US Signal is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
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 US Signal, 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. Entry-level AI roles across all categories have a median of $97,880.
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.
US Signal AI Hiring
US Signal has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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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