Senior SW Engineer [Codegen]
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. Job Description Join a multi-disciplinary team of experts operating at the core of Speedata’s software stack Lead the design and implementation of highly efficient algorithms that maximize our performance Develop time-critical Python components that translate database operations to performant, hardware-optimized C++ Co-design the next generation of our architecture alongside Hardware & Software engineering leads Research state-of-the-art algorithms and techniques for data analytics and hardware acceleration Fly high with the SW, and dive in for a byte of the HW Qualifications BSc or higher degree in Computer Science or a related field. 8+ years of experience in software development in a deep-tech context Ability for independent learning of topics, tools and languages Strong proficiency in Python Knowledge of algorithms and data structures Excellent communication skills Advantages Meaningful experience with C++ Experience with hardware systems and compilers Experience with databases and SQL High tolerance for dad-jokes Familiarity with software optimization techniques for performance-critical code Familiarity with computer architecture and low-level programming We are looking for a highly skilled professional eager to work on cutting-edge technology and play a key role in something truly impactful. We encourage you to apply if you meet the qualifications and are excited about the opportunity to work with our team. Speedata is the creator of the Analytics Processing Unit (APU), the first processor purpose-built for Apache Spark SQL, batch ETL, and AI data preparation workloads. By executing Spark operations natively in silicon rather than memory, the APU delivers up to 100x faster performance and 90% lower total cost of ownership compared to general-purpose compute, with zero code changes required. In one enterprise AI data preprocessing deployment, customers replaced 37 servers with just 3. Speedata integrates directly into existing data pipelines, accelerating the most demanding analytics and AI data layer workloads at scale. Our hardware, the C200 APU card with the Callisto ASIC on PCIe Gen 5, delivers transparent acceleration for Spark SQL, DataFrame, and batch ETL workloads.
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