Preparing for an interview at Moveworks.Ai requires a balanced approach. You must demonstrate both the architectural rigor of a senior systems engineer and the rapid experimentation mindset of an applied AI practitioner.

Role-Related Knowledge – You must show a deep understanding of LLM integration, prompt engineering, cognitive architectures, and the end-to-end machine learning lifecycle. Be prepared to talk about concrete experiences optimizing latency, handling model failures, and designing clean interfaces for generative models.

System Design & Scalability – Moveworks operates at massive enterprise scale. You will be evaluated on your ability to design robust, extensible APIs, build high-throughput distributed systems, and implement comprehensive logging, tracing, and monitoring frameworks.

Coding Craftsmanship – Writing readable, performant, and highly extensible code is non-negotiable. Whether you prefer Python, Golang, or Java, you should focus on clean modular design, correct concurrency patterns, and writing robust unit and integration tests.

Execution & Ownership – As a late-stage startup, Moveworks values engineers who can hit the ground running, embrace rapid iteration, and maintain a high standard of operational excellence. You need to show that you are self-driven and comfortable navigating ambiguous technical landscapes.

The interview loop at Moveworks.Ai is structured to evaluate your technical depth, architectural vision, and cultural alignment. The process is rigorous but highly transparent, aiming to simulate the actual collaborative environment you will experience on the job.

The journey begins with an initial technical recruiter screen to discuss your background, your experience with AI systems, and your alignment with the role. This is followed by one or two technical phone screens focusing on core coding and practical LLM system design. Once you pass these initial stages, you will move to the onsite loop, which consists of deep-dive sessions covering system architecture, hands-on coding, cognitive agent design, and behavioral alignment.

Throughout the process, Moveworks interviewers look for candidates who don't just understand the theory of AI, but who have practical, battle-tested experience shipping reliable software to production.

The timeline above outlines the standard progression from your initial application to the final offer. Candidates should expect the entire process to take between 3 to 5 weeks, depending on scheduling availability. Use this timeline to pace your preparation, focusing heavily on core coding fundamentals early on, and shifting to complex system design and agentic architectures as you approach the onsite loop.

To succeed in the Moveworks.Ai interview loop, you must master several core domains. Below is a detailed breakdown of the primary evaluation areas you will encounter.

Cognitive Architecture & Agentic Design

This area evaluates your ability to build systems where LLMs act as central reasoning engines. The focus is on how you design frameworks that allow agents to plan, use tools, and recover from errors autonomously.

Be ready to go over:

• Reasoning Frameworks – Designing and implementing multi-step reasoning patterns like ReAct, plan-and-solve, and self-correction loops.
• Tool Integration – Architecting secure, scalable interfaces for function calling, API execution, and working with enterprise knowledge graphs.
• State & Memory Management – Managing conversation state, summarizing long-term history, and optimizing context window usage.
• Advanced concepts (less common) – Multi-agent collaboration protocols, reinforcement learning from user feedback (RLHF) integration, and environment simulation for agent testing.

Example scenarios:

• "Design an agentic system that can automatically resolve an employee's IT ticket by querying a knowledge base, calling an internal provisioning API, and asking the user for clarifying details when necessary."
• "How would you construct an offline evaluation dataset to measure the accuracy and reliability of an agent's tool-selection logic?"

Scalable Systems & API Infrastructure

This area assesses your ability to build the robust product infrastructure that powers the conversational platform. You must demonstrate a systematic approach to performance, reliability, and API design.

Be ready to go over:

• API Abstractions – Creating clean, modular APIs that decouple the core dialog engine from various chat clients (Slack, Teams, Web).
• Latency Optimization – Identifying and resolving performance bottlenecks, optimizing network calls, and managing concurrent execution of downstream services.
• Observability & Debugging – Implementing tracing, logging, and metrics frameworks to monitor non-deterministic AI behaviors in real-time.
• Advanced concepts (less common) – Real-time multilingual translation streaming pipelines, hybrid search retrieval mechanisms, and distributed caching topologies.

Example scenarios:

• "Design a logging and tracing system that allows an on-call engineer to trace a failed user transaction back through multiple agent steps, LLM calls, and database lookups."
• "Architect a high-performance dialog engine that can handle thousands of concurrent conversations while keeping the memory footprint per conversation minimal."

Production-Grade Coding & Algorithms

This area tests your fundamental programming skills. You are expected to write production-quality code that is clean, well-tested, and optimized for performance.

Be ready to go over:

• Data Structures & Algorithms – Graph traversal (for knowledge graphs), string parsing, and efficient queue management.
• Concurrency & Parallelism – Writing safe concurrent code, handling race conditions, and managing thread pools or goroutines.
• Code Craftsmanship – Applying design patterns, writing modular and extensible interfaces, and ensuring comprehensive test coverage.
• Advanced concepts (less common) – Building custom tokenizers, writing highly optimized serialization/deserialization logic, and implementing custom rate-limiting algorithms.

Example scenarios:

• "Write a thread-safe connection pool manager for handling connections to multiple downstream LLM APIs."
• "Implement an algorithm to detect and resolve circular dependencies in a complex workflow graph before execution."

As an Agentic AI Engineer at Moveworks.Ai, your day-to-day responsibilities will bridge the gap between advanced AI research and robust systems engineering.

• Construct Product Infrastructure – You will build the core platform features and developer tools that allow other engineers and configurators to customize, optimize, and evaluate generative AI models for diverse enterprise use cases.
• Design Scalable APIs – You will design and maintain clean, modular API abstractions for the Moveworks conversation platform, ensuring seamless integrations with popular chat clients like Slack and Microsoft Teams.
• Optimize the Dialog Engine – You will continuously refine the core dialog engine to support complex, multi-turn conversations, leveraging private enterprise knowledge graphs while maintaining low latency and high reliability.
• Implement Frontier AI Algorithms – You will productionize cutting-edge AI architectures, agentic frameworks, and retrieval techniques, translating the latest machine learning advances into delightful user experiences.
• Enhance Observability – You will build and maintain tracing, logging, and automated metrics frameworks to provide deep visibility into model performance, latency, and system health.
• Collaborate and Mentor – You will work closely with ML engineers, product managers, and security teams. Depending on your seniority, you will also mentor other engineers, drive technical roadmaps, and champion coding best practices.

Moveworks looks for engineers who combine strong computer science fundamentals with a passion for applied AI. The requirements scale based on level (Software Engineer, Senior, or Staff), but the core expectations remain consistent.

• Must-Have Technical Skills – Strong proficiency in Python, Golang, or Java in a Mac/Linux development environment. Expert-level knowledge of computer science fundamentals, data structures, and distributed systems architecture.
• Systems Experience – Proven track record of building, scaling, and optimizing high-throughput production systems, complete with robust tracing, logging, and metrics.
• AI & LLM Expertise – Practical experience building with LLMs, prompt engineering, cognitive architecture design, and managing the latency/correctness trade-offs of generative models in a data-driven way.
• Soft Skills – Exceptional communication skills, a high level of curiosity, attention to detail, and a strong desire to ship high-quality code at a fast startup pace.
• Nice-to-Have Qualifications – Experience in AI safety, permission controls, and data privacy. Background in hands-on ML lifecycle stages (dataset curation, offline evaluation, model training).

Q: What programming languages are most commonly used for this role? A: The primary development languages at Moveworks are Python and Golang, with some services built in Java. You should be highly proficient in at least one of these and ready to work in a Mac development environment.

Q: How much machine learning theory do I need to know? A: While you do not need a PhD in machine learning, you must have a solid, practical understanding of how LLMs work, how to evaluate them, and how cognitive architectures like ReAct operate. The role focuses heavily on the systems engineering required to make these models reliable in production.

Q: What is the hybrid/remote work policy at Moveworks? A: Moveworks has a strong collaborative culture and generally prefers a hybrid working model for its offices in Mountain View, CA, and San Francisco, CA. You should expect to spend designated days in the office collaborating with your team.

Q: How does Moveworks evaluate system design for senior and staff roles? A: Senior and Staff candidates will face highly rigorous system design interviews. These sessions focus on your ability to design complex, distributed conversational architectures, handle enterprise-grade security and permission boundaries, and optimize end-to-end system latency.

To truly stand out in the interview process, keep these practical, insider tips in mind.

• Think in Terms of Latency and Cost – Whenever you suggest an agentic design (like multiple LLM calls or self-reflection loops), immediately discuss the latency and API cost trade-offs. Show that you understand how to balance agent capability with real-time user expectations.

• Emphasize Data-Driven Evaluation – Non-deterministic AI systems are notoriously hard to test. Always explain how you would evaluate your designs using offline simulation, error analysis, and structured evaluation datasets.

• Showcase Startup Ownership – Be ready to talk about projects where you took complete ownership of a feature, from initial design to writing code, setting up CI/CD, and configuring production monitoring.

• Highlight Security and Privacy – Enterprise customers are highly sensitive about their data. Showing that you think about data privacy, AI safety, and role-based access control (RBAC) during your system design sessions will set you apart from other candidates.

The Agentic AI Engineer role at Moveworks.Ai is an exceptional opportunity to shape the future of enterprise automation. By working at the intersection of robust distributed systems and cutting-edge cognitive AI architectures, you will build products that redefine how the modern workforce gets things done.

To maximize your chances of success, focus your preparation on core algorithmic coding, scalable system design, and practical LLM orchestration patterns. Practice explaining your architectural decisions clearly, and always ground your technical choices in data-driven trade-offs. For more deep-dive insights and interview preparation resources, continue exploring the tools available on Dataford.

The compensation range shown above reflects the base salary for engineering roles across different seniority levels at Moveworks. When evaluating an offer, remember that total compensation also includes equity components, which can grow significantly as the company continues its late-stage expansion. Senior and Staff positions typically command salaries toward the upper end of this spectrum, accompanied by substantial equity packages reflecting their strategic impact.