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

The following questions are representative of what candidates typically face during the AI Engineer interview loop. While you should not memorize answers, use these to understand the patterns of inquiry and the depth of technical and strategic thinking expected by the hiring team.

Machine Learning & Applied AI

This category tests your practical knowledge of deploying and optimizing models, particularly in the generative AI space.

• How do you mitigate hallucinations in an enterprise RAG application?
• Explain the mathematical difference between dot product and cosine similarity in the context of vector search.
• Walk me through the steps you would take to fine-tune an open-source LLM on a proprietary dataset.
• How do you evaluate the quality of a generative AI model's output when there is no strict "ground truth"?
• Describe a time you had to optimize a model to reduce inference latency.

Software Engineering & Infrastructure

These questions assess your ability to build robust, scalable systems around your AI models.

• Design an API architecture that allows users to submit long-running ML jobs and retrieve the results later.
• What is your approach to structuring a Dockerfile for a heavy PyTorch application to keep the image size manageable?
• How would you design a CI/CD pipeline for a system where both the code and the underlying data are constantly changing?
• Explain how you would implement rate limiting on an LLM-powered endpoint to manage API costs.
• Write a script to efficiently process and chunk a 10GB text file for vector database ingestion.

Behavioral & Consulting Scenarios

This category evaluates your communication skills, business acumen, and ability to navigate complex stakeholder dynamics.

• Tell me about a time you built an AI solution that directly impacted a business outcome. How did you measure success?
• Describe a situation where a stakeholder asked for an AI feature that was technically impossible or highly impractical. How did you handle it?
• How do you balance the need for rapid prototyping with the necessity of writing clean, maintainable code?
• Tell me about a time you had to learn a completely new technology or framework on the fly to deliver a project.
• Give an example of how you have advocated for better engineering practices within a team.

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?"

Key Responsibilities

As an AI Engineer at Berkeley Research Group, your day-to-day work will be a blend of deep technical execution and strategic problem-solving. If you are leaning toward the Infrastructure track, your primary responsibility will be designing, building, and maintaining the scalable platforms that support our AI Lab. This involves provisioning cloud resources, setting up Kubernetes clusters, and ensuring that our data scientists have a frictionless path from model experimentation to production deployment.

If your focus is more on the Product and Solutions track, you will spend your days prototyping and building intelligent applications. You will collaborate closely with researchers and consultants to understand their workflow bottlenecks, and then design LLM-powered tools—such as automated document summarization pipelines or intelligent data extraction APIs—to solve those problems. You will be responsible for the entire lifecycle of these applications, from initial prompt engineering to backend API development.

Regardless of your specific track, cross-functional collaboration is a daily reality. You will frequently sync with data scientists to optimize model performance, work with IT and security teams to ensure compliance with strict data governance standards, and present your technical solutions to project managers. You will also be expected to champion AI best practices across the firm, writing technical documentation and occasionally mentoring peers on new frameworks and tools.

Role Requirements & Qualifications

To thrive as an AI Engineer at Berkeley Research Group, you need a solid foundation in software engineering coupled with specialized knowledge in modern AI and machine learning ecosystems. We look for builders who are as comfortable debugging a deployment pipeline as they are fine-tuning an LLM.

• Must-have technical skills – Deep expertise in Python and its core data science libraries (Pandas, NumPy). Hands-on experience with modern LLM frameworks (LangChain, LlamaIndex, Hugging Face). Strong proficiency in cloud platforms (AWS, Azure, or GCP) and containerization tools (Docker, Kubernetes). Experience building RESTful APIs using FastAPI or similar frameworks.
• Must-have soft skills – Exceptional communication skills with the ability to translate complex technical concepts for business stakeholders. A high degree of autonomy and the ability to thrive in an ambiguous, fast-paced consulting environment.
• Experience level – Typically, candidates possess 3 to 7+ years of software engineering or data engineering experience, with at least 1-2 years specifically focused on deploying AI/ML models or building generative AI applications in production.
• Nice-to-have skills – Prior experience in a consulting, legal, or financial services environment where data privacy is paramount. Familiarity with advanced MLOps tools (MLflow, Kubeflow, Weights & Biases). Experience with vector databases (Pinecone, Weaviate, Milvus).

Frequently Asked Questions

Q: Is this role fully remote? Yes, the job postings for both the AI Lab Infrastructure Engineer and the AI Product and Solutions Engineer indicate that these positions are remote. However, you should be prepared to collaborate closely via video conferencing and align with the core working hours of your primary team.

Q: How much of the interview process focuses on LeetCode-style algorithms versus practical engineering? Berkeley Research Group strongly favors practical engineering over abstract algorithms. While you should be comfortable with standard data structures and algorithms, your time is better spent preparing for system design, API development, and applied ML scenarios (like RAG architecture and MLOps pipelines).

Q: What is the difference between the Infrastructure and the Product/Solutions tracks? The Infrastructure track is heavily focused on the backend: Kubernetes, cloud architecture, CI/CD, and scaling ML workloads. The Product/Solutions track is more application-focused: prompt engineering, building APIs with FastAPI, integrating LLMs into user-facing workflows, and directly solving client use cases.

Q: How should I prepare for the system design round? Focus on the intersection of standard backend system design and ML systems. Be prepared to discuss data ingestion, processing pipelines, model serving (batch vs. real-time), and how to handle the unique bottlenecks of AI applications (like GPU memory limits or API rate limits).

Q: What is the culture like for engineers at a consulting firm like BRG? Engineers at BRG operate as high-impact enablers. The culture is highly intellectual, data-driven, and client-focused. You will not be coding in a silo; you will be an active partner to domain experts, meaning your ability to understand the business context is just as valued as your technical code.

Other General Tips

• Emphasize Business Value: In a consulting environment, technology is a means to an end. Whenever you answer a technical question, tie your solution back to how it improves efficiency, reduces costs, or enhances the client experience.
• Master the "Why" in System Design: Interviewers care less about the specific tool you choose and more about your justification. Be ready to defend why you chose PostgreSQL over MongoDB, or why you opted for a managed vector database rather than hosting your own.

• Structure Your Behavioral Answers: Use the STAR method (Situation, Task, Action, Result) strictly. Ensure that your "Action" focuses on what you specifically did, not just what the team accomplished, and quantify your "Result" whenever possible.
• Clarify Before Coding: Whether in a live coding round or a system design interview, never start building immediately. Spend the first few minutes asking clarifying questions about data volume, expected latency, and edge cases.

• Prepare Questions for Them: Interviews are a two-way street. Ask insightful questions about their current AI stack, the biggest bottlenecks their data scientists face, or how they measure the ROI of internal AI tools.

Summary & Next Steps

Joining Berkeley Research Group as an AI Engineer is a unique opportunity to operate at the cutting edge of applied artificial intelligence within a prestigious, global consulting framework. You will be building the critical infrastructure and intelligent products that empower world-class experts to solve some of the most complex economic and strategic challenges today.

This salary module reflects the published compensation range for the AI Product and Solutions Engineer role ($130,000 -$190,000 USD). When interpreting this data, remember that specific offers will depend heavily on your seniority, your specific technical track (Infrastructure vs. Solutions), and your performance during the interview process. Use this information to anchor your expectations and negotiate confidently when the time comes.

To succeed in this interview loop, focus your preparation on the intersection of solid software engineering and applied AI. Review your system design fundamentals, practice articulating the trade-offs of different MLOps and LLM architectures, and refine your behavioral stories to highlight your ability to deliver business value. For more detailed interview insights, peer experiences, and targeted practice resources, continue exploring Dataford. You have the foundational skills required for this role—now it is time to structure your knowledge, practice your delivery, and show the team at BRG exactly what you can build.