Entry Level Ran Optimization Engineer Jobs in Austin, TX

Job Summary:
IBM is a leader in AI-powered, cloud-native products, and they are looking for a Lakehouse Performance Engineer to enhance the performance of their watsonx.data platform. The role involves benchmarking, workload engineering, and performance observability to ensure optimal performance and cost metrics across the platform.
Responsibilities:
• Run, maintain, and continuously improve reproducible benchmarks across watsonx.data configurations and against competitive offerings.
• Build and curate workload suites that reflect real customer query mixes, data volumes, concurrency profiles, and freshness requirements: not just synthetic benchmarks.
• Ensure every published result is reproducible end-to-end: controlled environments, pinned versions, locked datasets, documented methodology, variance analysis, and statistically defensible reporting.
• Operationalize the canonical price‑performance KPIs ($/query, $/TB scanned, $/training‑token, queries/sec/$, TCO at workload mix); instrument workloads, collect data, and produce repeatable scorecards.
• Build and maintain the metrics, traces, profiles, GPU/CPU utilization, query plan, and IO telemetry that flow from benchmark runs into the performance data store.
• Develop dashboards that surface trends, regressions, and competitive position to engineering, leadership, and external audiences.
• Operate performance regression gates in CI/CD; triage failures, file and drive issues with engine, storage, and GPU teams, and verify fixes.
• Drill into slow queries and GPU/CPU bottlenecks using profilers (Nsight, perf, async-profiler, pprof, flamegraphs) and query plan inspection to pinpoint regressions and improvement opportunities.
• Own the lifecycle of the dedicated performance environment(s) supporting watsonx.data: GPU and CPU clusters, networking, storage, and the orchestration that schedules workloads onto them.
• Build and maintain infrastructure-as-code (Terraform/Ansible/Helm) for provisioning, configuring, and resetting test environments deterministically across on-prem hardware and cloud (IBM Cloud and partner clouds).
• Develop and operate the benchmark harness itself: job scheduler, run orchestration, dataset provisioning, result capture, artifact storage, and the API/CLI other teams use to launch runs.
• Own the curated datasets used for benchmarking and the warehouse of historical results that powers trend analysis, regression detection, and competitive comparisons.
• Manage capacity and utilization of the performance lab so concurrent campaigns from different teams (query engine, storage, GPU acceleration, AI) run cleanly and without interference.
• Provide engineers across watsonx.data with self-service paths to run standardized perf experiments against well-known baselines, lowering the cost of evidence-based engineering decisions.
• Pair with engineers on the query engine, storage, GPU acceleration, catalog, and AI/RAG paths to land performance improvements and verify their impact.
• Produce data, charts, and write-ups that feed internal quarterly scorecards and external performance whitepapers, blog posts, and analyst briefings.
• Participate in design reviews and code reviews where performance is at stake; flag risks early and propose measurable acceptance criteria.
• Document workloads, harnesses, lab usage, and results so the next engineer internal or external: can reproduce what you ran.
Qualifications:
Required:
• 8+ years of professional software engineering experience with at least 2 years focused on performance engineering, benchmarking, or SRE for a data platform, database, distributed system.
• Strong programming skills in at least one of Python, Go, Java, plus comfort with shell scripting and modern automation tooling.
• Working knowledge of at least one modern analytics engine (Presto/Trino, Spark, DuckDB, ClickHouse, or comparable) and at least one open table format (Iceberg, Delta, or Hudi).
• Hands-on experience with at least some of: Linux performance tooling (perf, ftrace, eBPF), profilers (Nsight, async-profiler, pprof), and query plan analysis.
• Infrastructure-as-code fluency in at least one of Terraform, Ansible, Pulumi, or Helm; comfort writing and maintaining the automation, not just consuming it.
Preferred:
• Hands-on experience with GPU-accelerated data processing (RAPIDS/cuDF, Velox/Theseus‑class engines, CUDA) and the GPU memory hierarchy (HBM, NVLink, PCIe trade‑offs).
• Experience publishing or co-authoring peer-reviewed or industry-recognized performance results (TPC, MLPerf, ClickBench, LST‑Bench, or similar).
• Experience operating a multi-tenant performance lab or shared test fleet where multiple teams ran experiments concurrently.
• Experience building bespoke benchmark harnesses or workload generators, including dataset generation at TB+ scale.
• Familiarity with vector search, retrieval-augmented generation (RAG), and AI inference/training performance characterization.
• Familiarity with FinOps and cloud unit economics—translating raw performance numbers into $/performance and TCO conclusions.
• Contributions to relevant open-source projects (Iceberg, Trino, Spark, Arrow, Velox, RAPIDS, OpenTelemetry, perf-tooling, etc.).
• Hands-on experience designing and running performance experiments: controlling for variance, isolating variables, and producing clear, defensible results.
• Experience operating real infrastructure: Linux servers, Kubernetes, container runtimes, networking basics, and object storage.
• Comfort with observability tooling: metrics (Prometheus), tracing/telemetry (OpenTelemetry), and dashboards (Grafana or equivalent).
Company:
IBM provides technology and consulting, including software, infrastructure systems, and cloud-based solutions. Founded in 1911, the company is headquartered in Armonk, USA, with a team of 10001+ employees. The company is currently Late Stage.