AI/ML Workloads Engineer

Position Overview

The Role: Senior/Staff/Principal Research/SW Engineer (ML Workloads). The role requires you to be part of the team that helps productize the SW stack for our AI compute engine. As part of the Software team, you will be responsible for the development, enhancement, and maintenance of the development and testing infrastructure for next-generation AI hardware. This includes the software development repo and the testing infrastructure for the Compiler, Kernels, and will utilize simulators, FPGAs, Emulation, and the chip as test platforms. You are expected to build and scale software deliverables in a tight development window while collaborating with compiler and ML experts and working closely with other software and hardware specialists across the company.

Key Responsibilities

• Develop, enhance, and maintain the development and testing infrastructure for next-generation AI hardware.
• Collaborate with compiler and ML experts to productize the SW stack and optimize ML workloads.
• Utilize simulators, FPGAs, emulation platforms, and the chip itself for testing and validation.
• Build and scale software deliverables within tight development windows.
• Work closely with both software and hardware experts to ensure optimal performance and integration.

Required Qualifications

• • Computer Science, Engineering, Math, Physics or related degree
• • MS or PhD in Computer Science, Electrical Engineering, or related fields
• • Strong grasp of computer architecture, data structures, system software, and machine learning fundamentals
• • Strong theoretical understanding of machine learning
• • Experience implementing and optimizing ML workloads and low-level software algorithms for specialized hardware such as FPGAs, DSPs, and DL accelerators
• • Experience with mapping NLP models (BERT and GPT) to accelerators and awareness of trade-offs across memory, bandwidth, and compute
• • Experience with ML models from definition to deployment including training, quantization, sparsity, model preprocessing, and deployment
• • Proficient in Python development in a Linux environment using standard development tools
• • Experience with deep learning frameworks such as PyTorch or Tensorflow
• • Experience training, tuning, and deploying ML models for CV (e.g., ResNet), NLP (e.g., BERT, GPT), and/or recommendation systems (e.g., DLRM)
• • Experience deploying ML workloads on distributed systems
• • Self-motivated team player with a strong sense of ownership and leadership

Preferred Qualifications

• • Research background with a publication record in ML conferences such as ICML, NeurIPS, ICLR, etc.
• • Prior experience in a startup, small team, or incubation environment
• • Experience working at a cloud provider or AI compute/sub-system company
• • Experience implementing SIMD algorithms on vector processors
• • Familiarity with open-source ML compiler frameworks such as MLIR