Staff ML Engineer, Autonomy Hardware & Model Optimization

Rivian; Palo Alto (CA); On-site; USD 228,000 - 285,000; Full time

Benefits offered by this job

Medical Insurance

Dental Insurance

Vision Insurance

Job summary

Rivian is seeking a Staff Software Engineer for ML Optimization and Hardware Acceleration to lead optimization of ML models for autonomous driving. You will bridge ML research and embedded deployment, focusing on Transformers, LLMs, VLMs and LDMs across diverse embedded hardware.

You will collaborate with low-level software and hardware teams, implement PTQ/QAT, kernel fusion, and model distillation to maximize throughput on Rivian's embedded platforms.

Qualifications

• Education/Experience: MS (+3 years of experience in deep learning, heterogeneous computing, and ML accelerators) or Ph.D. in Computer Science, Electrical Engineering, or a related field.
• Core ML Expertise: Deep understanding of modern model architectures, including Transformers, LLMs, VLMs and LDMs.
• Optimization Skills: Proven experience in model compression techniques: knowledge distillation, pruning, and quantization (PTQ/QAT).
• Hardware Knowledge: In-depth understanding of GPU architecture and the ability to optimize for diverse hardware specifications.
• Technical Toolset: Proficiency in Python and deep knowledge of PyTorch or TensorFlow; hands-on with TensorRT, AIMET, ONNX runtimes; experience with CUDA kernels, C++, or BLAS; profiling with Torch profiler and Nsight.

Responsibilities

• Model Optimization: Develop and deploy ultra-low latency DL/ML algorithms for Rivian ADAS and Autonomy use cases.

Skills

Transformers

LLMs

VLMs

LDMs

Python

PyTorch

TensorRT

CUDA

Profiling tools

Education

MS +3 years in DL/accelerators

PhD in CS/EE or related

Tools

TensorRT

AIMET

ONNX runtimes

CUDA

BLAS

Nsight

Torch profiler

PyTorch

TensorFlow

Job description

Rivian is seeking a Staff Software Engineer for ML Optimization and Hardware Acceleration to lead optimization of ML models for autonomous driving. You will bridge ML research and embedded deployment, focusing on Transformers, LLMs, VLMs and LDMs across diverse embedded hardware.

You will collaborate with low-level software and hardware teams, implement PTQ/QAT, kernel fusion, and model distillation to maximize throughput on Rivian's embedded platforms.