What is an AI Engineer at Berkeley Research Group?

As an AI Engineer at Berkeley Research Group (BRG), you are at the forefront of transforming complex data and expert insights into scalable, intelligent solutions. BRG is a premier global consulting firm known for its rigorous, data-driven approach to disputes, investigations, and strategic advisory. In this environment, AI is not just a buzzword; it is a critical lever for analyzing massive datasets, automating complex workflows, and delivering unprecedented value to clients.

This role uniquely bridges the gap between deep technical infrastructure and high-impact product solutions. Depending on your specific track—whether as an AI Lab Infrastructure Engineer or an AI Product and Solutions Engineer—your impact will span from designing the foundational MLOps pipelines that power our AI Lab, to building large language model (LLM) applications that directly solve client problems. You will work alongside top economists, data scientists, and industry experts, translating their domain knowledge into robust AI architectures.

Expect a highly dynamic, intellectually stimulating environment. You will be tackling ambiguous, high-stakes problems where scale, security, and accuracy are paramount. This role requires not only exceptional technical depth in machine learning and software engineering but also the strategic mindset to build tools that genuinely move the needle for the business and our clients.

Common Interview Questions

Getting Ready for Your Interviews

Preparing for an interview at Berkeley Research Group requires a balanced focus on technical rigor and business acumen. We want to see how you build, how you think, and how you collaborate.

Technical Excellence & System Design – This evaluates your hands-on ability to build scalable AI systems. Interviewers will look for deep proficiency in Python, cloud infrastructure, LLM integration, and MLOps. You can demonstrate strength here by clearly articulating architectural trade-offs and writing clean, production-ready code.

Problem-Solving & Ambiguity – In a research and consulting environment, problems are rarely perfectly scoped. This criterion assesses your ability to take a vague business problem, break it down into technical requirements, and design a pragmatic AI solution. Show your strength by asking clarifying questions before jumping into technical implementation.

Cross-Functional Communication – You will be working with non-technical stakeholders, including economists and legal experts. We evaluate your ability to explain complex AI concepts simply and effectively. Strong candidates can pivot their communication style depending on their audience.

Execution & Delivery – This measures your pragmatic approach to getting things done. We look for engineers who understand that a simple, reliable model deployed today is often better than a perfect model deployed next month. Demonstrate this by discussing how you prioritize features, manage technical debt, and ensure robust CI/CD practices.

Interview Process Overview

The interview process for an AI Engineer at Berkeley Research Group is designed to be thorough, collaborative, and reflective of the actual work you will do. You should expect a multi-stage process that progressively deepens in technical and strategic complexity. The pace is typically deliberate, allowing both you and the hiring team ample time to assess mutual fit.

Your journey will generally begin with an initial recruiter screen focused on your background, role alignment, and high-level technical experience. From there, you will move into technical deep dives. Unlike companies that rely solely on abstract algorithmic puzzles, BRG heavily favors practical, applied engineering assessments. You may encounter a take-home challenge or a live pair-programming session focused on real-world scenarios, such as designing an API for an LLM application or structuring an MLOps pipeline.

The final stages involve a virtual onsite loop consisting of several specialized interviews. These rounds will test your system design capabilities, your understanding of AI product integration, and your behavioral competencies. The culture at BRG is highly collaborative and data-centric, so expect interviewers to probe not just what you built, but why you built it and how you measured its success.

This visual timeline outlines the typical progression from your initial application through the final onsite rounds. Use this to pace your preparation—focusing first on core coding and ML concepts, and later shifting your energy toward system design and behavioral storytelling. Note that specific stages, such as the inclusion of a take-home case study, may vary slightly depending on whether you are interviewing for the Infrastructure or Product/Solutions track.

Deep Dive into Evaluation Areas

Applied AI and LLM Integration

As an AI Engineer, your ability to leverage modern AI paradigms is critical. This area evaluates your practical experience with Large Language Models, prompt engineering, and Retrieval-Augmented Generation (RAG). Strong performance means demonstrating a nuanced understanding of how to constrain model hallucinations, optimize latency, and handle context windows effectively.

Be ready to go over:

• RAG Architectures – Understanding vector databases, embedding models, and retrieval strategies.
• Prompt Engineering & Fine-Tuning – Knowing when to rely on zero-shot prompting versus when to fine-tune a model using LoRA or QLoRA.
• Model Evaluation – Techniques for evaluating generative AI outputs systematically.
• Advanced concepts (less common) – Agentic workflows, multi-modal model integration, and custom decoding strategies.

Example questions or scenarios:

• "Walk me through how you would design a RAG system to query thousands of dense legal documents for a consulting engagement."
• "How do you handle situation where an LLM confidently hallucinates an answer in a client-facing application?"
• "Explain the trade-offs between using a managed LLM API (like OpenAI) versus hosting an open-source model (like Llama 3) internally."

AI Infrastructure and MLOps

For the AI Lab Infrastructure side of the role, this is the most critical evaluation area. We need to know that you can build the pipes that keep our models running securely and efficiently. Interviewers are looking for candidates who treat ML models as software that needs rigorous testing, deployment, and monitoring.

Be ready to go over:

• Model Deployment – Containerizing models with Docker and orchestrating them via Kubernetes or cloud-native services.
• CI/CD for Machine Learning – Automating model training, testing, and deployment pipelines.
• Monitoring & Observability – Tracking data drift, concept drift, and model performance degradation in production.
• Advanced concepts (less common) – Distributed training architectures, GPU memory optimization, and custom CUDA kernels.

Example questions or scenarios:

• "How would you design an infrastructure to serve a highly requested ML model with strict latency requirements?"
• "Describe your approach to setting up a CI/CD pipeline for a machine learning project."
• "What metrics would you monitor for an NLP model deployed in production, and how would you detect drift?"

Software Engineering & System Design

AI engineers are, fundamentally, software engineers. This area tests your ability to write clean, maintainable, and scalable code. Strong candidates will show proficiency in Python, an understanding of software design patterns, and the ability to design distributed systems that integrate AI seamlessly into broader product ecosystems.

Be ready to go over:

• API Design – Building robust RESTful or GraphQL APIs using frameworks like FastAPI or Flask.
• Database Design – Structuring relational (PostgreSQL) and non-relational (MongoDB, Redis) databases.
• Scalability & Reliability – Designing systems that can handle concurrent users, large data volumes, and failovers gracefully.
• Advanced concepts (less common) – Event-driven architectures, stream processing (Kafka), and microservices orchestration.

Example questions or scenarios:

• "Design a system architecture for an internal tool that allows consultants to upload massive datasets and run predictive models asynchronously."
• "Write a Python function to process and clean a streaming dataset before it hits our inference endpoint."
• "How do you ensure data security and compliance when designing systems that handle sensitive client information?"

Behavioral and Stakeholder Management

At Berkeley Research Group, technical brilliance must be paired with consulting skills. This area evaluates how you handle conflict, influence decisions without authority, and manage the expectations of non-technical stakeholders. A strong performance involves using the STAR method to tell compelling stories about your past experiences.

Be ready to go over:

• Navigating Ambiguity – How you proceed when requirements are vague or constantly shifting.
• Cross-Functional Collaboration – Working with domain experts, product managers, and external clients.
• Failing Forward – Discussing a time a project failed and what you learned from it.
• Advanced concepts (less common) – Leading technical strategy shifts or mentoring junior engineers.

Example questions or scenarios:

• "Tell me about a time you had to explain a complex machine learning limitation to a non-technical stakeholder."
• "Describe a situation where you had to push back on a feature request because it wasn't technically feasible or scalable."
• "How do you prioritize your engineering tasks when multiple teams are depending on your AI infrastructure?"