Job description

Software Engineer, ML Data Infrastructure W2 Contract Pay Rate: $60 - $70 per hour Location: Cupertino, CA - Remote Role Job Summary: We are hiring a Software Engineer to build and run the data infrastructure that feeds our ML training and inference systems. You will design high-throughput, distributed data systems on top of columnar and lakehouse formats, so that GPUs and models are never starved for data. This is a hands-on role for someone who enjoys performance engineering and wants to work close to the boundary between data systems and machine learning. Duties and Responsibilities: • Design, build, and operate large-scale distributed data systems that serve ML training and inference workloads in production. • Strong Python Engineer • in Rust (or C++/Go) with Python bindings for ML practitioners to build high-performance data loading, storage, and retrieval layers (Systems Performance, which is critical work) • Evaluate and adopt columnar and lakehouse formats (Parquet, Iceberg, Delta, Lance), and make the trade-offs explicit for schema evolution, random access, scan performance, and versioning. • Optimize I/O-bound pipelines using Arrow, zero-copy techniques, memory mapping, async I/O, and efficient object storage access patterns (request coalescing, prefetching, caching, parallel range reads). • Profile and remove bottlenecks across the data path, from object storage to host memory to accelerator, so training and inference stay compute-bound rather than I/O-bound. • Partner with ML researchers and engineers to understand how training loops, evaluation, and inference services consume data, and turn that into system requirements. • Define reliability, observability, and cost standards for data infrastructure, including SLOs, monitoring, capacity planning, and incident response. • Write design documents, review code, and mentor engineers on performance engineering and distributed systems practices. Requirements and Qualifications: • Extensive experience building and running large-scale distributed data or ML infrastructure in production. • Strong programming skills in Python, plus a systems language for performance-critical work (Rust strongly preferred; C++ or Go acceptable). • Deep familiarity with columnar and lakehouse formats (Parquet, Iceberg, Delta, or Lance) and the trade-offs between them. • Hands-on performance engineering for I/O-bound workloads: Arrow, zero-copy, memory mapping, async I/O, and high-throughput object storage access patterns. • Working knowledge of the end-to-end ML workflow and how training and inference workloads consume data, enough to design data systems that serve them well. Preferred Qualifications: • Production Rust experience, including async runtimes (Tokio), FFI, and Python bindings (PyO3 or similar). • Contributions to open-source data or ML infrastructure projects such as Arrow, Lance, Iceberg, Ray, or PyTorch data loading. • Experience with distributed computing and orchestration frameworks (Spark, Ray, Dask, Kubernetes). • Familiarity with multimodal or large-scale unstructured data (images, video, audio, embeddings) and vector or random-access storage. • Experience with GPU-aware data pipelines, including pinned memory, GPUDirect Storage, and overlapping I/O with compute. • Track record of cost optimization for cloud object storage and egress at petabyte scale. • Experience leading technical design across teams and mentoring other engineers. • BS/MS/PhD in Computer Science or a related field, or equivalent practical experience. Bayside Solutions, Inc. is not able to sponsor any candidates at this time. Additionally, candidates for this position must qualify as a W2 candidate . Bayside Solutions, Inc. may collect your personal information during the position application process. Please reference Bayside Solutions, Inc.'s CCPA Privacy Policy at www.baysidesolutions.com.

Requirements

Must-haves

Software Engineer, ML Data Infrastructure
Pay Rate: $60 - $70 per hour
Location: Cupertino, CA - Remote Role
Design, build, and operate large-scale distributed data systems that serve ML training and inference workloads in production.
Strong Python Engineer
in Rust (or C++/Go) with Python bindings for ML practitioners to build high-performance data loading, storage, and retrieval layers (Systems Performance, which is critical work)
Evaluate and adopt columnar and lakehouse formats (Parquet, Iceberg, Delta, Lance), and make the trade-offs explicit for schema evolution, random access, scan performance, and versioning.
Optimize I/O-bound pipelines using Arrow, zero-copy techniques, memory mapping, async I/O, and efficient object storage access patterns (request coalescing, prefetching, caching, parallel range reads).
Profile and remove bottlenecks across the data path, from object storage to host memory to accelerator, so training and inference stay compute-bound rather than I/O-bound.
Partner with ML researchers and engineers to understand how training loops, evaluation, and inference services consume data, and turn that into system requirements.
Define reliability, observability, and cost standards for data infrastructure, including SLOs, monitoring, capacity planning, and incident response.
Write design documents, review code, and mentor engineers on performance engineering and distributed systems practices.
Extensive experience building and running large-scale distributed data or ML infrastructure in production.

Nice-to-haves

Strong programming skills in Python, plus a systems language for performance-critical work (Rust strongly preferred; C++ or Go acceptable).
Production Rust experience, including async runtimes (Tokio), FFI, and Python bindings (PyO3 or similar).
Contributions to open-source data or ML infrastructure projects such as Arrow, Lance, Iceberg, Ray, or PyTorch data loading.
Experience with distributed computing and orchestration frameworks (Spark, Ray, Dask, Kubernetes).
Familiarity with multimodal or large-scale unstructured data (images, video, audio, embeddings) and vector or random-access storage.
Experience with GPU-aware data pipelines, including pinned memory, GPUDirect Storage, and overlapping I/O with compute.
Track record of cost optimization for cloud object storage and egress at petabyte scale.
Experience leading technical design across teams and mentoring other engineers.
BS/MS/PhD in Computer Science or a related field, or equivalent practical experience.
Bayside Solutions, Inc. is not able to sponsor any candidates at this time. Additionally, candidates for this position must qualify as a W2 candidate .
Bayside Solutions, Inc. may collect your personal information during the position application process. Please reference Bayside Solutions, Inc.'s CCPA Privacy Policy at www.baysidesolutions.com.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlmachine learningpythonrustgokubernetes

Platform: careers-html
Effort: medium