Field Application Engineer

About Speedata

Speedata is modernizing analytics infrastructure with the first purpose-built ASIC processor, the Analytics Processing Unit (APU), for analytics and AI data workloads. Delivering up to 100x faster Apache Spark performance while cutting infrastructure TCO by 90%, the APU executes analytics operations directly in silicon with seamless integration and no code changes. For more information, visit www.speedata.io.

We are looking for a Field Application Engineer (Performance) to join this team. This is a hands-on systems engineering role focused on POC execution, performance optimization, deep troubleshooting, and helping customers experience the full value of Speedata’s acceleration technology.

What You Bring

• 3–7 years of hands-on experience in DevOps, SRE, HPC, or distributed systems roles.

• Strong Linux fundamentals (networking, filesystems, process debugging, kernel behaviors).

• Proven ability to troubleshoot complex issues across OS, storage, networking, and distributed compute layers.

• Experience deploying or operating systems like Spark, Kubernetes, Hadoop, or other large-scale clusters.

• Automation/scripting skills in Python, Bash, or equivalent.

• Familiarity with performance engineering concepts, data formats (Parquet), and observability tooling.

• Customer-facing communication skills and the ability to represent the company in technical discussions.

• High ownership mindset, strong analytical thinking, and comfort working in a fast-paced deep-tech environment.

What You’ll Do

• Deploy and configure distributed systems across Linux, networking, storage, and Spark environments.

• Run customer POCs and performance benchmarks, including setup, tuning, validation, and analysis.

• Troubleshoot complex system issues across compute, memory, disk, network, containers, and distributed runtimes.

• Build automation, tooling, and repeatable workflows for deployment, monitoring, and performance testing.

• Work directly with customer engineering teams to understand workloads and diagnose performance bottlenecks.

• Partner closely with R&D to surface bugs, performance gaps, and usability issues observed in the field.

• Contribute to internal documentation, playbooks, and best practices for global Field Engineering teams.