An AI Engineer at Ironclad sits at the intersection of cutting-edge artificial intelligence, robust software engineering, and the highly complex world of legal contracts. Ironclad is the leading digital contracting platform, trusted by modern enterprises to manage, analyze, and automate their contract lifecycles. In this role, you will build and scale the intelligent systems that power features like automated contract redlining, instant metadata extraction, legal playbooks, and generative contract drafting.

The impact of this position is immense. Contracts are the foundational data layer of any business, but they are notoriously unstructured, dense, and written in complex legal language. As an AI Engineer, you will directly solve these challenges by building production-grade Retrieval-Augged Generation (RAG) systems, fine-tuning specialized Large Language Models (LLMs), and designing resilient API pipelines that can parse and analyze massive multi-page documents in seconds. Your work will directly reduce contract negotiation cycles from weeks to minutes for thousands of global customers.

What makes this role uniquely exciting is the balance of research and production engineering. You will not just be training models in isolated environments; you will be designing the APIs, microservices, and system architectures that deliver AI capabilities to enterprise users at scale. Whether you are optimizing a specialized Go-To-Market (GTM) AI tool or building core platform features, you will have a direct hand in defining how AI transforms the legal industry.

The questions you will encounter during the Ironclad interview process are designed to test your practical engineering skills, system design capabilities, and your ability to reason through complex machine learning problems. The following questions are representative of what candidates have faced in real interviews, categorized by the core competencies evaluated.

Machine Learning Case Studies & AI Design

• Explain how you would design a system to extract key metadata (such as termination dates, liability caps, and governing law) from unstructured legal PDFs.
• How do you evaluate the performance of an LLM-based system when the ground truth is highly subjective, such as legal contract summaries?
• Walk me through a complex machine learning model you built in the past. What were the data challenges, and how did you measure its success in production?

Preparing for the Ironclad AI Engineer interview requires a balanced approach. Because this role demands both strong software engineering fundamentals and deep machine learning expertise, you cannot afford to focus solely on model architecture or basic coding.

Role-Related Knowledge – You must demonstrate a deep understanding of modern NLP, LLMs, prompt engineering, and vector databases. Interviewers will evaluate your ability to select the right model for a task, optimize prompt pipelines, and implement robust evaluation frameworks.

System & API Design – Ironclad values engineers who build clean, scalable, and maintainable software. You will be evaluated on your ability to design robust APIs, handle asynchronous background jobs, manage state, and architect scalable data pipelines.

Problem-Solving & Pragmatism – AI is highly unpredictable. Interviewers want to see how you approach ambiguity, handle edge cases (like poorly scanned PDFs), and make pragmatic trade-offs between model accuracy, latency, and compute costs.

Collaboration & Communication – As an AI Engineer, you will work closely with product teams, frontend engineers, and legal specialists. You must be able to explain complex AI concepts simply and demonstrate alignment with Ironclad's collaborative and customer-centric culture.

The interview process at Ironclad is rigorous, comprehensive, and highly structured to evaluate both your engineering depth and your practical AI application skills. Candidates can expect a multi-stage loop that moves from initial screening to deep-dive technical evaluations and behavioral assessments.

The journey begins with a recruiter screen to discuss your background, alignment with the role, and salary expectations. This is quickly followed by a technical screen or a direct conversation with the hiring manager. In this initial technical stage, you will walk through your resume, discuss the architecture of AI models you have built in the past, and answer situational questions regarding machine learning and data science.

If you pass the initial screen, you will move to the core evaluation phase, starting with an ML case study interview. This leads into the final round, which typically consists of four distinct sessions: two behavioral interviews and two technical interviews, specifically focusing on API design and systems design.

The timeline above outlines the standard progression of the Ironclad interview loop. Candidates should use this visual roadmap to pace their preparation, ensuring they allocate sufficient time to practice both system design and coding before the intensive final rounds. While the exact timing can vary depending on candidate availability, the overall structure remains consistent across most engineering teams.

To succeed at Ironclad, you need to understand exactly what is expected in each core technical interview. The hiring team is not just looking for theoretical knowledge; they want to see how you apply engineering discipline to AI-driven products.

Machine Learning Case Study

The ML Case Study is a cornerstone of the Ironclad evaluation process. This interview tests your ability to take an ambiguous, real-world product problem and design an end-to-end machine learning solution. You will be evaluated on how you structure data pipelines, select model architectures, design evaluation strategies, and plan for production deployment.

Be ready to go over:

• Data Preprocessing & OCR – How to handle messy, unstructured document layouts, extract clean text from PDFs, and prepare data for model training or inference.
• Model Selection & Tuning – When to use off-the-shelf LLMs, when to leverage prompt engineering, and when it is necessary to fine-tune a specialized model.
• Evaluation Frameworks – How to build reliable validation sets and define metrics (such as precision, recall, and semantic similarity) to measure AI output quality.
• Advanced concepts – Multi-step agentic workflows, retrieval-augmented generation (RAG) chunking strategies, and handling long-context documents.

Example scenarios:

• "Design an automated contract compliance checker that flags clauses that deviate from a company's standard legal playbook."
• "How would you build a system to automatically categorize incoming legal agreements and route them to the correct department?"

Systems & API Design

At Ironclad, AI features must seamlessly integrate with the rest of the enterprise platform. The Systems and API Design round evaluates your ability to build scalable, resilient, and performant backends that power AI capabilities.

Be ready to go over:

• API Contract Design – Writing clean RESTful or GraphQL API endpoints, defining request/response payloads, and handling asynchronous task statuses.
• Asynchronous Processing – Using message brokers and task queues (such as Celery or RabbitMQ) to handle heavy, long-running AI extraction and OCR jobs.
• Scalability & Latency – Techniques to reduce user-facing latency, such as caching, streaming LLM responses, and optimizing database queries.
• Advanced concepts – Vector database indexing (HNSW vs. IVF), distributed tracing for multi-step AI pipelines, and handling rate limits from upstream model providers.

Example scenarios:

• "Design the API and backend architecture for an AI-powered contract drafting assistant that suggests text completions as a user types."
• "Architect a system that can ingest and process bulk uploads of 10,000 legal contracts simultaneously without degrading platform performance."

Behavioral & Situational Alignment

This round focuses on your past experiences, your engineering philosophy, and how you handle the unique challenges of working with probabilistic AI systems. The hiring manager will dive deep into your resume to understand your technical contributions and team collaboration style.

Be ready to go over:

• Resume Walkthrough – Be prepared to explain the technical architecture of previous AI systems you built, including your specific contributions and the business outcomes.
• Handling AI Failures – How you deal with hallucinations, bias, or sudden model degradation in a production environment.
• Cross-functional Collaboration – How you work with non-technical stakeholders, such as product managers, legal experts, and marketing teams, to align on technical capabilities.
• Advanced concepts – Managing compute budgets, conducting post-mortems on failed ML projects, and prioritizing technical debt versus rapid AI prototyping.

Example scenarios:

• "Tell me about a time you had to explain a complex AI limitation to a product manager who wanted to launch a high-risk feature."
• "Describe a situation where a model you deployed did not perform as well in production as it did in your offline validation tests. How did you resolve it?"