
Posted today
Senior System Software Engineer
AI Summary
Designs, builds and operates large-scale distributed backend services that run on thousands of servers and power NVIDIA's engineering workflows. Leads architecture, CI/CD, observability and reliability work while mentoring engineers.
About this role
System Software Engineer
NVIDIA is advancing AI and accelerated computing to solve complex challenges across industries. Our Infrastructure organization builds the platforms that power NVIDIA’s engineering teams worldwide. We are looking for an experienced and passionate full-stack engineer to compose, build, and monitor highly scalable distributed services. These services run on thousands of servers and complete millions of automated jobs daily, directly improving the efficiency of thousands of NVIDIA software engineers.
What you’ll be doing:
- Define system architecture and guide development decisions for complex engineering workflows, balancing scalability, reliability, cost, and delivery timelines.
- Lead reviews of system approaches and promote safe, incremental, low-risk rollouts of major infrastructure features across multiple teams.
- Set the strategy for CI/CD pipelines and release practices, enhancing build, test, and deployment speed and quality.
- Support the adoption of containerised services on Docker and Kubernetes, and advocate automation to reduce operational toil.
- Define the observability approach (monitoring, logging, alerting, SLOs) to detect issues early, reduce alert noise, and accelerate incident resolution.
- Lead root-cause analysis of complex production incidents and champion long-term reliability improvements.
- Mentor and grow senior and junior engineers, raise engineering standards, and work closely with teams and collaborators across time zones.
What we need to see:
- BS/MS in Computer Science, Engineering, or equivalent experience, with 5+ years building and operating large-scale backend or distributed systems.
- Deep Java expertise, including concurrency, performance tuning, and debugging complex production issues.
- History of crafting, scaling, and managing large-scale, highly available full-stack applications in production, including leading major architectural changes or migrations.
- Experience in working with Version Control Systems like Gitlab and Gerrit
- Solid grounding in data structures, algorithms, distributed system development, and testing practices.
- Hands-on experience with relational databases (e.g., MySQL) and at least one NoSQL store (e.g., Elasticsearch, MongoDB, Cassandra).
- Experience with Docker and Kubernetes, along with infrastructure automation tools such as Ansible or Chef.
- Experience with one or more monitoring and observability tools (e.g., Prometheus, Grafana, Kibana, Splunk), with a focus on actionable alerts.
- Demonstrated technical leadership: mentoring engineers, influencing roadmaps, and driving cross-team initiatives.
- Strong communication skills and the ability to collaborate across teams and time zones.
Ways to stand out from the crowd:
- Experience taking major infrastructure features from build through incremental production rollout.
- Ability to break complex problems into simple parts and reuse existing solutions.
- SRE approach: using logs and metrics to troubleshoot failures, define SLOs, and improve reliability.
- Experience designing full-stack applications that run well with minimal operational support.
Skills
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