Job Title: Machine Learning Systems Engineer (Infrastructure & Tooling) – AR/VR Location: Redmond, WA (Onsite, with occasional WFH flexibility) Duration: 12+ Months and extendable Pay rate: $100-$110/hr on W2 About the Role Seeking an experienced Machine Learning Systems Engineer to join our AR/VR team, focusing on building robust infrastructure and tooling to empower research scientists. In this role, you’ll work closely with world-class researchers to develop scalable ML systems, optimize distributed compute solutions, and shape the next generation of AR/VR technologies. This is a high-impact opportunity for engineers passionate about Python, PyTorch/TensorFlow, and distributed systems, with a knack for solving complex problems in multimodal data (e.g., audio, sensor fusion). Responsibilities • Write clean, maintainable code and debug complex, cross-system challenges. • Design scalable ML tooling/infrastructure to leverage massive compute/storage resources. • Collaborate with cross-functional teams to integrate tools with internal platforms. • Work with large, complex datasets (capture, annotation, preprocessing). • Prototype and evaluate end-to-end ML systems for AR/VR applications. • Continuously learn and adapt to new technologies in a fast-paced environment. Minimum Qualifications • BS in Computer Science or equivalent experience. • 4+ years of industry experience with Python and deep learning frameworks (PyTorch/TensorFlow). • 2+ years working with large-scale ML datasets and distributed compute (e.g., GPUs, clusters). • Hands-on experience deploying ML systems and CI/CD pipelines. • Strong collaboration and documentation skills. Preferred Qualifications • Experience with audio, multimodal ML, or DSP. • Background in AR/VR applications. • Linux/Windows scripting and high-performance computing. • Ability to translate research requirements into production-grade tools. Top 3 Must-Have Skills 1. Python (ML/infrastructure focus). 1. PyTorch or similar ML frameworks. 1. Distributed systems (scaling training/inference). Interview Process 1. Initial Screen (30 min): Behavioral + high-level coding. 1. Technical Round (60 min): Live coding (CoderPad). Note: This is an onsite role with core collaboration hours (8–10 AM PT) to align with UK teammates. How to Apply Interested? Submit your resume and a brief note highlighting your experience with ML infrastructure, PyTorch, or AR/VR.