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About This Role
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Berkeley Research Group's Ai Department is seeking an AI Infrastructure Engineer to lead the development of our Virtual Ai Lab initiative. Following the successful completion of Phase 01 (physical Ai Lab build\-out), this role will focus on creating a virtual access layer that makes our high\-performance Ai Lab remotely accessible to teams across BRG. The ideal candidate will design and implement scalable infrastructure to support processing 100,000\+ documents daily using state\-of\-the\-art LLMs from OpenAI and Anthropic.
About the Role
As an AI Infrastructure Engineer, you will architect and build the virtual access interface for our physical Ai Lab, ensuring secure, scalable, and efficient remote processing capabilities. You will lead the design and implementation of infrastructure that allows BRG teams to leverage our Ai Lab's computational power remotely, while maintaining performance standards for large\-scale document processing. Key responsibilities include developing customizable interfaces for different BRG groups, implementing secure access controls, and ensuring optimal resource allocation for concurrent users processing massive datasets through LLMs.
Key Responsibilities
- Design and implement a virtual access layer for the physical Ai Lab infrastructure
- Build scalable remote processing capabilities supporting 100,000\+ documents per day
- Create customizable, expandable interfaces for different BRG business units
- Optimize infrastructure for maximum LLM token throughput (OpenAI/Anthropic)
- Implement secure authentication and access management systems
- Ensure high availability and fault tolerance for mission\-critical AI workloads
- Lead infrastructure projects from conception to production deployment
Required:
- Bachelor's degree in Computer Science, Information Technology, or a related field
- Minimum six to eight (6\-8\) years of hands\-on experience designing, deploying, and managing scalable cloud infrastructure
- Strong experience with Infrastructure as Code (IaC) tools and methodologies
- Experience designing, implementing, and maintaining scalable, secure, and cost\-efficient cloud/on\-prem solutions
- Proven ability to manage and lead projects to deliver high\-quality, replicable solutions
- Proficiency in VCS (Git/GitHub), modern coding languages (Python, .NET, Java, etc.), Software Development Life Cycle, and CI/CD practices
- Experience with API design and implementation for distributed systems
- Knowledge of GPU infrastructure and optimization for AI workloads
- Hands\-on experience with AWS Services including:
+ EC2/Lambda (apps/functions)
+ SageMaker (ML)
+ S3 (file management)
+ Fargate/ECS/EKS (containerization)
+ CDK/Terraform (IaC)
+ Cost Explorer/Budgets
Preferred:
- Experience with LLM deployment and optimization (OpenAI, Anthropic, etc.)
- Background in building AI/ML infrastructure and platforms
- Experience with virtual desktop infrastructure (VDI) or remote access solutions
- Knowledge of distributed computing and job scheduling systems
- AWS certifications (Solutions Architect, Machine Learning, or similar)
- Experience with cost management and optimization strategies in the cloud
- Familiarity with security best practices for AI systems and data handling
About BRG
BRG combines world\-leading academic credentials with world\-tested business expertise purpose\-built for agility and connectivity, which sets us apart—and gets you ahead.
At BRG, our top\-tier professionals include specialist consultants, industry experts, renowned academics, and leading\-edge data scientists. Together, they bring a diversity of proven real\-world experience to economics, disputes, and investigations; corporate finance; and performance improvement services that address the most complex challenges for organizations across the globe.
Our unique structure nurtures the interdisciplinary relationships that give us the edge, laying the groundwork for more informed insights and more original, incisive thinking from diverse perspectives that, when paired with our global reach and resources, make us uniquely capable to address our clients’ challenges. We get results because we know how to apply our thinking to your world.
At BRG, we don’t just show you what’s possible. We’re built to help you make it happen.
BRG is proud to be an Equal Opportunity Employer. Our hiring practices provide equal opportunity for employment without regard to race, religion, color, sex, gender, national origin, age, United States military veteran status, ancestry, sexual orientation, marital status, family structure, medical condition including genetic characteristics or information, veteran status, or mental or physical disability so long as the essential functions of the job can be performed with or without reasonable accommodation, or any other protected category under federal, state, or local law.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Berkeley Research Group, LLC, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Berkeley Research Group, LLC AI Hiring
Berkeley Research Group, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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