Staff Software Development Engineer (Backend - Python/Go/Rust)

Role

We are looking for a Staff Software Development Engineer (Backend) to join our team. This is a Hybrid (Bangalore) role, reporting to the Director, Threat Researcher in the Engineering department.

In this role, you will architect, scale, and operate complex distributed systems and cloud-native platforms running on Kubernetes and cloud infrastructure. You will own end-to-end technical initiatives, set high standards for reliability and performance, and multiply the overall impact of the engineering team.

What you’ll do (Role Expectations)

• Lead the design, development, testing, and evolution of backend services and APIs, making sound architectural trade-offs with a focus on scalability, reliability, performance, and maintainability
• Drive services end to end — development, deployment, scaling, monitoring, and ongoing production support — and set the standard for operational ownership across the team
• Design and manage services in containerized environments, leading deployment strategy, scaling, and troubleshooting on Kubernetes
• Lead incident response and root-cause analysis across distributed systems, monitor service health, debug complex failures, and drive systemic fixes that improve long-term production reliability
• Work across cloud infrastructure and compute environments to support workload deployment, service configuration, and operational troubleshooting, including multi-environment and capacity considerations

Who You Are (Success Profile)

• You act like an owner with a strong bias for action, operating with integrity, caring genuinely about outcomes, and navigating seamlessly between high-level strategy and hands-on execution
• You are a high-trust collaborator who is ambitious for the team, embracing a challenge culture through ongoing feedback delivered with candor, clarity, and respect
• You are customer-obsessed, building deep empathy for internal and external customers to anchor decisions in solving real-world problems and championing their success
• You are driven by innovation and energized by solving complex technical challenges, bringing deep curiosity and a constant search for better, more secure, and scalable solutions
• You lead with integrity, holding yourself and others to high standards of accountability while matching your words with consistent, transparent action

What We’re Looking for (Minimum Qualifications)

• Foundational understanding of AI/ML technologies and experience leveraging, securing, or positioning AI-driven solutions to optimize outcomes within your functional domain
• 5+ years owning backend or distributed production services, with strong expertise in Python, Go, or Rust and working proficiency in a second language
• Proven experience building, scaling, and troubleshooting high-volume production APIs and backend services using containers and Kubernetes
• Experience with cloud platforms, with Amazon Web Services (AWS) or Google Cloud Platform (GCP) experience preferred
• Strong system design, performance, and reliability fundamentals with proven ability to lead distributed root-cause analysis and make independent architectural decisions
• Experience working with AI-assisted development and engineering workflows, applying AI tools for code generation, code review, testing, debugging, and operational automation to improve engineering velocity and quality

What Will Make You Stand Out (Preferred Qualifications)

• Proven ability to leverage AI technologies and workflows to drive measurable operational efficiency
• Hands-on expertise deploying, operating, and troubleshooting production Kubernetes workloads across large-scale multi-cloud environments
• Strong, production-level proficiency in two or more of Python, Go, and Rust, or familiarity with C/C++ for systems-level or performance-sensitive components
• Deep understanding of authentication (SSO, OAuth, OIDC) and observability (metrics, logs, traces) to resolve production issues across app and infra layers, with proven ability to leverage AI workflows for operational efficiency