Senior Software Development Engineer

Gibson Dunn•Washington, DC

3h•$230,000 - $300,000•Hybrid

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 Engineering Manager, Platform Engineering leads and grows the team responsible for the firm’s internal developer platform: the reusable platforms, tools, and services that improve developer productivity, accelerate delivery, and standardize engineering practices across the firm. This role is accountable for the team’s people, delivery, reliability, and security outcomes, balancing hands-on technical leadership with the management of engineers. The Engineering Manager owns the team’s roadmap and execution, develops engineers, sets the operating rhythm for delivery, release management, and on-call, and partners with engineering and business leaders to ensure the platform meets the firm’s needs.

Requirements

Proven people leadership: hiring, coaching, performance management, and team development.
Strong delivery-management track record across planning, execution, and risk.
Solid technical grounding across software engineering (Go/Python), CI/CD, IaC/GitOps, cloud (AWS/Azure), Kubernetes, observability/SRE, platform security, and test automation, sufficient to review code, guide architecture, and make sound trade-offs.
Excellent stakeholder communication and cross-functional collaboration.
12+ years of experience in platform, DevOps, SRE, or software engineering, including 4+ years managing and developing engineering teams.
A strong hands-on engineering background (coding, infrastructure as code, and cloud) prior to management.
Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field (or equivalent experience); advanced degree a plus.
Experience delivering internal developer platforms or cloud/DevOps capabilities in a global professional services, legal, or regulated environment.

Nice To Haves

Familiarity with ITIL best practices (incident, change, and problem management) preferred.
Experience leading within Lean or Agile organizations (e.g., Scrum, SAFe) preferred.
Cloud certifications and a prior individual-contributor platform or SRE background preferred.
Working knowledge of emerging AI governance frameworks (e.g., EU AI Act, NIST AI RMF, ISO/IEC 42001) preferred.

Responsibilities

Lead, coach, and develop a team of platform engineers, fostering a high-performing, inclusive, and collaborative engineering culture.
Own performance management, career development, goal-setting, and retention; provide regular, actionable feedback.
Balance individual growth with team delivery, removing blockers and supporting engineers’ success.
Own the Platform Engineering roadmap and delivery, translating firm and engineering strategy into prioritized, well-scoped engineering work.
Ensure predictable, high-quality delivery through effective planning, execution, and risk management.
Establish and run the team’s operating rhythm (planning, standups, code review culture, retrospectives, and on-call).
Review designs and code, guide architecture, and make sound technical trade-offs.
Shape platform architecture, coding standards, and long-term direction.
Champion a code-first, everything-as-code culture and measurable developer-experience outcomes.
Ensure the team extends platform standards to AI systems, including version-controlled prompts, retrieval configurations, evaluations, and release processes alongside application code.
Hold accountability for platform reliability, SLOs, incident management, and on-call health, and continuous improvement of operational maturity.
Ensure robust pipelines-as-code, release-management, and test-automation practices that enable safe, frequent delivery.
Ensure platform security and compliance obligations are met, partnering with security and governance teams.
Ensure the platform accounts for AI-specific operational and security realities, including provider outages, graceful degradation across models, non-deterministic failure modes, silent quality regressions after model updates, inference and GPU cost management, prompt-injection and data-exfiltration defenses, output filtering, and confidentiality constraints across prompts, retrieval, and agent tool access.
Partner with development teams, business stakeholders, and IT leadership to align platform investments with firm needs.
Manage relationships with strategic vendors, along with budgets, licensing, and SLAs.
Communicate roadmap, progress, risks, and outcomes clearly to leadership.
Own hiring, onboarding, and workforce planning to build and scale a strong engineering team.
Invest in engineers’ technical and professional growth and support mentorship and communities of practice.
Promote a culture of ownership, learning, reliability, and continuous improvement.