Senior Software Development Engineer

Gibson Dunn•New York City, NY

2d•Onsite

About The Position

Gibson Dunn is a leading global law firm, advising clients on significant transactions and disputes. Our exceptional teams craft and deploy creative legal strategies that are meticulously tailored to every matter, however complex or high-stakes. The firm’s work is distinguished by a unique combination of precision and vision. Based in New York City or Washington, D.C., the Senior Software Development Engineer designs, architects, builds, and leads the delivery of enterprise applications and shared platform capabilities, serving as a technical leader responsible for driving engineering excellence, scalable architecture, and successful delivery of complex software initiatives. This role is ideal for an engineer who enjoys building user-centric software solutions and integrating cutting-edge capabilities into scalable enterprise applications. The role works in close partnership with the firm’s AI Platform Engineering team to design and build agentic workflows – systems that plan, invoke tools, and carry out multi-step work on behalf of attorneys and business professionals.

Requirements

Experience building web applications, APIs, microservices, and distributed systems, with a strong grounding in software architecture and object-oriented design.
Demonstrated experience designing scalable systems and optimizing high-volume applications for performance and reliability.
Experience leading architecture and technical delivery for enterprise-scale, cross-functional initiatives, including breaking down ambiguous business problems into workable technical solutions.
Proven ability to mentor senior engineers, influence technical direction, and establish engineering standards.
Experience with source control, Git workflows, automated testing, CI/CD pipelines, and DevOps practices.
Experience building production software backed by large language models, including tool or function calling, retrieval, and orchestration of multi-step workflows.
10+ years of progressive experience designing, developing, and leading delivery of enterprise software applications.
Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related technical discipline (or equivalent experience).

Nice To Haves

Hands-on experience with agent frameworks and orchestration tooling (e.g., LangGraph or Semantic Kernel), Model Context Protocol (MCP), and evaluation harnesses for non-deterministic systems preferred.
Experience developing applications in Microsoft Azure, and relevant certifications in Azure, software development, or cloud technologies, preferred.

Responsibilities

Build user-facing applications, APIs, and services that enable attorneys and business professionals to leverage capabilities securely and effectively.
Develop solutions that integrate generated insights into existing legal, business, and knowledge management workflows.
Design, develop, and maintain enterprise-grade software applications that consume and interact with Gibson Dunn’s proprietary LLMs, services, and intelligent agent frameworks.
Define the long-term technical strategy and lead the architecture for enterprise applications and shared platforms.
Drive technical direction across multiple engineering teams and strategic initiatives.
Make architectural decisions balancing scalability, maintainability, security, and performance.
Evaluate emerging technologies and recommend adoption where appropriate.
Translate legal and business workflows into agent-assisted experiences, exercising judgment about where an agent adds real value and where deterministic software remains the better answer.
Design and build agentic workflows that plan, call tools, and execute multi-step legal and business tasks, including the tool and Model Context Protocol (MCP) interfaces that expose firm systems to agents under correctly scoped permissions.
Build the orchestration around agent runs, including state management, retries, checkpointing, and safe recovery for long-running and asynchronous work.
Design human-in-the-loop review, approval, and override points so attorneys retain control over consequential actions and agent output remains verifiable.
Define evaluations and regression tests for agent behavior, instrument runs end to end (traces, tool calls, tokens, latency, cost), and tune for quality and unit economics alongside AI Platform Engineering.
Develop modern web applications and services using contemporary front-end and back-end technologies.
Create responsive and intuitive user experiences that simplify interaction with complex capabilities.
Design and implement reusable application components, services, and integration patterns that support scalability and maintainability.
Establish enterprise application architecture standards, reusable engineering frameworks, and design patterns across teams.
Review technical designs and provide guidance on implementation approaches.
Integrate enterprise applications with internal platforms, knowledge repositories, document management systems, search services, and business applications.
Develop secure APIs, microservices, and middleware components that facilitate communication between services and enterprise systems.
Collaborate with platform and engineering teams to operationalize new capabilities and services.
Build on the AI platform’s model gateway, retrieval, and evaluation services rather than standing up parallel capability, and work with AI Platform Engineering to close genuine gaps in the platform.
Lead multiple strategic engineering initiatives while establishing engineering standards and delivery best practices across teams.
Drive sprint planning, technical estimation, and delivery planning.
Champion engineering best practices including automated testing, CI/CD, observability, and release management.
Identify technical debt and develop long-term modernization strategies.
Extend testing and release practices to non-deterministic systems, including evaluation-based quality gates, staged rollout, and rollback plans for agent capabilities.
Build solutions that adhere to firm security standards, data governance policies, confidentiality requirements, and client obligations.
Implement authentication, authorization, auditing, and monitoring capabilities in accordance with enterprise security practices.
Ensure applications appropriately handle sensitive and privileged information.
Lead architectural security reviews and partner with Information Security to establish secure development standards, championing secure coding practices and participating in threat modeling and design reviews.
Enforce least-privilege tool access for agents and design against prompt injection, data exfiltration, and unintended actions on firm or client systems.
Ensure agent activity is fully auditable, with a traceable record of what an agent retrieved, invoked, and produced, consistent with confidentiality and privilege obligations.
Monitor and optimize application performance, scalability, reliability, and user experience.
Implement telemetry, logging, and observability solutions to support production operations.
Troubleshoot application issues and collaborate across teams to resolve complex technical challenges.
Define platform reliability objectives, resiliency standards, and operational excellence metrics, and improve system resiliency, monitoring, and operational maturity.
Lead production incident investigations and root cause analyses.
Monitor agent quality in production alongside conventional performance metrics, including silent regressions that follow model, prompt, or tool changes.
Partner with attorneys, business stakeholders, product owners, architects, and engineers to understand business needs and translate them into technical solutions.
Mentor junior engineers through code reviews, technical coaching, and design guidance.
Lead architecture discussions and technical design reviews, and influence engineering standards and software development best practices across teams.
Partner with product owners and engineering leadership to define long-term technology roadmaps.
Build consensus across engineering, infrastructure, security, and business teams while influencing technology investment decisions.
Partner closely with AI Platform Engineering, adopting its paved roads by default and feeding real delivery experience back into the platform roadmap.