Staff Performance Engineer in Mountain View, California at Aerospike, Inc.

Aerospike is the real-time database for mission-critical use cases and workloads, including machine learning, generative, and agentic AI. Aerospike powers millions of transactions per second with millisecond latency, at a fraction of the total cost of ownership compared to other databases.

Global leaders, including Adobe, Airtel, Barclays, Criteo, DBS Bank, Experian, Grab, HDFC Bank, PayPal, Sony Interactive Entertainment, The Trade Desk, and Wayfair, rely on Aerospike for customer 360, fraud detection, real-time bidding, profile stores, recommendation engines, and other use cases.

At Aerospike, we dream big and deliver even bigger. Our mission is to unleash the power of the world’s real-time data with a database built for infinite scale, speed, and sustainability.

If you're ready to shape the future of data, join us.

Job Summary
We are seeking a highly skilled Staff Performance Engineer to join our Quality Engineering team. In this role, you will characterize the performance and reliability of Aerospike's database platform, with primary focus on the server and client libraries, and contribute across other product areas as needed.

You will work within Quality Engineering's regression practice across many builds, helping ensure regressions do not reach customers despite the challenge of continuously validating an expanding set of releases and products while scaling coverage into current gaps. You will design workloads that reflect real customer use, establish repeatable baselines, detect regressions before release, and help scale performance coverage into current gaps—integrating shared benchmark and cluster tooling into everyday regression and creating a foundation that developers and QE can extend. This is a hands-on role for someone equally comfortable tuning a benchmark, debugging a production-like cluster, and improving the systems that make performance testing reliable at scale.

Job Responsibilities

• Design workloads that stress and characterize system performance and reliability as experienced by customers.
  Build and maintain performance baselines across builds, hardware profiles, and configuration variants; use them to detect regressions and improvements with statistical rigor.
• Run and evolve performance regression as a pre-release quality gate across dedicated bare-metal clusters and remote containerized test environments, selecting the right environment for each workload.
  Investigate performance issues reported by customers or found in internal testing; reproduce problems, isolate root causes, and drive effective fixes.
• Analyze and optimize server and client performance, including indexing, query execution, data access paths, and client/server interaction.
• Integrate benchmark and cluster tooling into regular regression pipelines, expand automated nightly and pre-release gates, and prioritize high-value coverage additions.
• Collaborate with Product R&D to ensure performance and reliability requirements are reflected in development and release processes.
• Create and maintain documentation on benchmark methodology, baseline management, environment setup, and performance best practices.
• Engage with customers to understand deployment patterns, validate performance claims, and translate field requirements into actionable improvements.
• Stay current with industry trends in database performance, observability, and test automation

Technical Skills

• Strong programming experience in at least one systems-oriented language such as Java, C/C++, Go, or Python.
• Experience designing, running, and interpreting benchmarks or load tests for distributed systems, databases, or similarly latency-sensitive platforms.
• Solid understanding of database architecture, indexing, query processing, and performance tuning.
• Experience with observability tools and techniques such as metrics dashboards, tracing/profiling, and system-level monitoring (for example, Prometheus/Grafana, eBPF, or equivalent).
• Experience with Linux/Unix environments and practical system administration for test and production-like systems.
• Experience automating test or deployment environments using infrastructure-as-code, configuration management, or container/orchestration tools (for example, Ansible, Terraform, Docker, Kubernetes, or similar).
• Demonstrated ability to troubleshoot complex performance problems across application, database, network, storage, and hardware layers.
• Strong analytical skills and excellent written and verbal communication, including the ability to explain technical tradeoffs to both engineering and non-engineering audiences.

Highly Desirable

• Experience with NoSQL or real-time data platforms; experience with Aerospike or similar systems is a plus.
• Experience with database client libraries, SDKs, or application-side performance tuning.
• Experience operating or testing large-scale distributed systems in production or production-like environments.
• Experience building or maintaining automated regression systems, including baseline management and outlier/regression detection.
• Experience working across bare-metal and cloud test environments.
• Experience integrating benchmark or cluster-provisioning tools into CI/CD or scheduled test pipelines.
• Experience with expanding test coverage across multiple products and release lines.

To foster strong team alignment and productivity, Bay Area–based team members are expected to work from the office 3 times per week—typically Tuesday through Thursday.

Salary Range: $180,000 – $230,000
(Actual compensation will be based on experience, location, and other relevant factors.)

Aerospike is an Equal Opportunity Employer. We are committed to an environment free from discrimination based on race, religion, gender identity, sexual orientation, age, disability, or veteran status.

To keep our recruitment pipeline as high-performance as our database, Aerospike uses AI-driven tools to streamline resume reviews, scheduling, and communication. While we love smart tech, we value human connection even more. AI helps us optimize the process, but real people make 100% of the actual hiring decisions.