Top Tech Jobs & Startup Jobs in Seattle, WA

Leads the strategy, architecture, and deployment of AI-enabled business intelligence infrastructure. Designs governed semantic layers, data models, and BI systems using dbt and enterprise data warehouses. Manages and coaches a technical engineering team, partners with executives and cross-functional stakeholders, and ensures reliable, high-performing data pipelines, warehouses, and reporting tools. The role also advances AI-driven analytics, data discovery, and self-service reporting across the organization.

Design, implement, test, and optimize scalable control-plane and data-plane services for cloud networking products. Build APIs, distributed systems, monitoring tools, and performance improvements while supporting existing products. Establish engineering best practices, improve reliability and delivery processes, collaborate across teams, and mentor engineers. The role requires strong Go development, networking expertise, cloud and containerization experience, and familiarity with technologies including eBPF, XDP, gRPC, Kafka, and Kubernetes.

Develop and optimize high-performance network datapath software for cloud infrastructure. Build packet processing, routing, forwarding, load balancing, observability, and hardware-accelerated networking across Linux hosts, kernels, user space, NICs, DPUs, and SmartNICs. Diagnose production performance issues, create testing and benchmarking frameworks, author technical designs, collaborate across infrastructure teams, and mentor engineers.

Design and build software for global network automation, orchestration, provisioning, device lifecycle management, telemetry, validation, and automated remediation. Integrate DPUs, SmartNICs, and programmable network infrastructure into scalable cloud systems. Establish architecture and technical direction, write RFCs and design proposals, lead complex initiatives from concept through operation, mentor engineers, and collaborate across network, infrastructure, hardware, compute, storage, and security teams.

Design and build scalable Go-based distributed control plane microservices for cloud compute infrastructure. Develop gRPC interfaces, optimize Linux kernel and virtualization systems, and improve performance, reliability, observability, failover, and recovery. Lead complex cross-functional technical projects, mentor engineers, conduct architectural reviews, support production systems through on-call rotations, and drive continuous improvements across the Compute ecosystem.

Lead secure design reviews, threat modeling, and security risk assessments for products and features. Partner with engineers, product managers, and designers to develop secure architectures, provide remediation guidance, promote security culture through training and mentoring, oversee vulnerability management, and explain security events. Build security tooling, automate secure development practices, and establish architectural patterns and processes that reduce security risk across DigitalOcean’s engineering organization.

Leads DigitalOcean’s Applied Research function for agentic AI, setting the research agenda across model routing, memory, observability, and reinforcement learning. Builds and manages a research team, connects research investments to product outcomes, and partners with engineering to ship production capabilities that improve reliability, cost, latency, and task success. Represents the company externally through publications, talks, open-source work, and research collaborations.

Lead DigitalOcean’s AI security strategy, governance framework, threat modeling, and multi-year roadmap. Partner with security, engineering, and data science teams to secure AI products, deploy guardrails, conduct adversarial testing and red teaming, and monitor AI/ML systems. Provide architectural reviews, executive risk briefings, and guidance on emerging threats including prompt injection, model supply-chain risks, agentic AI, and data exfiltration.

Design and optimize scalable, multi-tenant serverless inference infrastructure and APIs for large-scale AI workloads. Build highly available distributed services, improve GPU utilization, observability, capacity management, fault tolerance, and operational automation. Partner with platform and GPU infrastructure teams, contribute to architecture decisions, participate in on-call and incident response, and provide technical leadership and mentorship to engineers.

Own technical go-to-market for AI-native cloud products and market segments. Translate complex capabilities into narratives, build demos and code, create technical content, run launches, enable sales, monitor market/competitive signals, and engage developer communities to drive adoption, pipeline, and wins.