Senior DevOps Engineer (USA)

Work Model: Remote (work from home). 

Time Zone & Collaboration: Collaborate with a distributed DevOps team across US, UK, and New Zealand, with an expectation to overlap at least four hours with US Pacific Time (9 AM – 5 PM) most days and to participate in an on-call rotation for production emergencies.

Business Travel: Occasional domestic travel (typically no more than once per quarter) for company events or team gatherings, subject to business needs.

AI tooling: Engineering, including DevOps, uses AI tools extensively to boost productivity and quality. This role is expected to actively use AI-assisted coding and analysis tools and to experiment with new AI workflows in areas like CI/CD, observability, and ML Ops so we can measure and raise AI adoption across R&D.

Own key areas of DroneDeploy’s cloud infrastructure, improving reliability, performance, and usability of the platform and related services.

Design, build, and evolve infrastructure-as-code to provision and manage scalable, secure environments used across engineering teams.

Lead improvements to CI/CD pipelines and release workflows (e.g., GitHub Actions, Jenkins, CircleCI), creating templates and tooling that make deployments faster, safer, and more consistent for product teams.

Champion observability by implementing and refining monitoring, logging, alerting, and dashboards that give teams clear visibility into system health and customer impact.

Partner with engineers across multiple product areas to streamline code deployment, promote a culture of collaboration and shared responsibility, and define “golden paths” for how services are built, tested, and operated.

Identify areas of operational toil or fragility and implement durable automation and process improvements that reduce recurring incidents and support burden.

Participate in the production support rotation, including incident response, troubleshooting high-impact issues, and contributing to post-incident reviews and follow‑through actions.

Contribute to ML Ops efforts by collaborating with AI-focused teams on running machine learning processes in the cloud, supporting model training, evaluation, and related infrastructure needs when required.

Share expertise through code reviews, documentation, pairing, and mentoring, helping other engineers adopt best practices in infrastructure, deployment, and reliability while continuously learning new tools and techniques.

Use AI tooling in day‑to‑day engineering work (e.g., code generation, test scaffolding, troubleshooting) and provide feedback that helps improve how DroneDeploy uses AI across R&D.

Extensive experience working with at least one major cloud provider (AWS, Azure, or GCP) to design, deploy, and operate production systems.

Experience operating Kubernetes clusters in the cloud at scale.

Strong experience writing and maintaining software applications (not just scripts), with proficiency in Python and/or Golang and comfort contributing to shared repositories and services.

Solid hands-on experience with infrastructure-as-code such as Terraform (or similar tools like Pulumi or CloudFormation) to manage complex environments.

Proven track record building, maintaining, and improving CI/CD pipelines using platforms such as GitHub Actions, Jenkins, CircleCI, or comparable solutions.

Strong Linux system administration fundamentals, including networking, performance tuning, and security basics in a cloud-native environment.

Demonstrated ability to troubleshoot complex production issues spanning application, infrastructure, and third-party services, using observability tools and structured problem-solving.

Experience collaborating closely with software engineering teams, influencing patterns and practices through clear communication, documentation, and enablement rather than formal authority.

Comfort operating in distributed, remote teams, taking initiative to move projects forward independently, surface tradeoffs, and propose solutions in areas like CI/CD, reliability, or cost efficiency.

No specific drone certification is required for this role.

Impact at scale: Help ensure world-class reliability for a global reality-capture platform used on high‑visibility projects in construction, energy, agriculture, and more. Your work on infrastructure, pipelines, and observability will directly affect how often and how safely teams can ship product.

Senior‑level scope: As a P4 Senior DevOps Engineer, you’ll own substantial slices of our platform—such as core CI/CD frameworks, infrastructure domains, or reliability initiatives—and be trusted to lead end‑to‑end improvements across design, implementation, rollout, and continuous refinement.

Modern, AI‑driven environment: Work at the intersection of cloud infrastructure and AI, supporting ML Ops workflows and partnering with teams rolling out AI-enabled features and developer tooling like DX AI Code Insights and other AI-based coding tools.

Strong DevOps culture: Join a tight‑knit DevOps group that values simplicity, empathy for users (internal and external), thoughtful tradeoffs, and continuous learning. You’ll collaborate with experienced Senior and Lead DevOps Engineers and cross-functional leaders in TechOps, Platform, and Product Engineering.

Growth & learning: Build deep expertise in areas like Kubernetes, large‑scale observability, ML Ops, and compliance‑driven reliability work (e.g., GDPR data lifecycle, incident and outage practices), with opportunities to mentor others and grow toward Staff-level or leadership paths over time.

Flexibility: Enjoy a remote‑first role within the U.S., with reasonable expectations for overlapping collaboration hours and limited travel, so you can do focused engineering work while staying closely connected to a distributed, high-performing team.