About AtlasBase

AtlasBase is a fast-growing pioneer in Molecular Computing, bridging the gap between traditional IT and biotechnology. We are redefining global IT infrastructure by driving breakthroughs in both Molecular Compute and Molecular Data Storage. By commercializing synthetic DNA-based technologies at IT scale, AtlasBase delivers ultra-high-density, low-maintenance, and carbon-conscious solutions built for the explosive growth and value of the world's data. Leveraging nature’s most durable medium, we provide a virtually permanent foundation that transforms how the world processes and secures its most critical information.

Originally spun out of Twist Bioscience, our team is an elite fusion of scientists, engineers, and operators dedicated to solving some of the most complex infrastructure challenges on Earth. While our work is grounded in deep science, our success depends on world-class talent across every discipline. We are seeking a Staff High-Performance Software Engineer who is ready to bring their expertise to a high-impact environment and help us build a brand-new category of technology.

About the role

Role Overview As a Staff High-Performance Software Engineer, you will be a foundational member of our engineering team. You will play a critical role in accelerating our complex encoding and decoding pipelines, leveraging the full computational power of GPUs and CPUs within a distributed system environment.

Key Responsibilities

• System Architecture: Architect and implement highly efficient, distributed data processing pipelines across hybrid hardware platforms.
• Performance Optimization: Develop highly optimized pipelines capable of processing massive data payloads with predictable throughput and resources.
• Cross-Functional Collaboration: Partner closely with our software, and bioinformatics team on integration within our larger ecosystem.

Required Qualifications (Skills & Experience)

• Experience: 8+ years of professional software development experience in high-performance or systems-level environments.
• Languages: Deep expertise in systems-level programming languages such as C, C++, or Rust.
• Hardware Architecture: Strong understanding of CPU/GPU architectures, memory management, and hardware bottlenecks.
• Domain Knowledge: Advanced knowledge of parallel computing paradigms, concurrency, and distributed system architectures.
• Compute Frameworks: Proven ability to write highly optimized code using both high-level machine learning libraries (e.g., PyTorch) and low-level kernel development interfaces (e.g., CUDA, OpenCL, or Metal).

Preferred Qualifications

• Education: Bachelor’s or master’s degree in computer science, Computer Engineering, or a related highly technical field.
• Profiling Tools: Familiarity with performance profiling and debugging tools (e.g., NVIDIA Nsight, perf, Valgrind).