Senior/Staff ML Engineer, ML Acceleration and Performance

As a Staff Software Engineer for ML Optimization and Hardware Acceleration, you will be a lead member of the Autonomy team at Rivian. You will develop and optimize advanced machine learning algorithms that directly impact the safety-critical self-driving features of our category-defining vehicles. This role focuses on the intersection of cutting-edge model architectures including Transformers, LLMs, VLMs, LDMs and high-performance hardware execution. You will bridge the gap between theoretical ML research and real- time embedded deployment, ensuring our autonomy stack remains both state-of-the-art and ultra-efficient.

• Model Optimization: Develop and deploy ultra-low latency Deep Learning and Machine Learning algorithms specifically tailored for Rivian ADAS and Autonomy use cases.

• Hardware-Aware Design: Research and implement hardware-aware optimization strategies, including Post-Training Quantization (PTQ), Quantization-Aware Training (QAT), kernel fusion, and model distillation to maximize throughput on embedded platforms.

• Performance Profiling: Utilize and automate deep-dive profiling tools (e.g., Torch Profile, NVIDIA Nsight) to identify bottlenecks and ensure performance consistency across weekly evaluation runs.

• Cross-Functional Collaboration: Partner with low-level software and hardware architecture teams to characterize in-house ML models on embedded platforms, optimizing them within strict compute and memory constraints.

• Architectural Reasoning: Apply a deep understanding of GPU architectures to optimize models across significantly different hardware targets, ensuring scalability across the Rivian fleet.

• Workflow and Infrastructure Engineering: Design and build automated pipelines for regular model profiling across diverse architectures to enhance organization-wide insight into execution bottlenecks.

• 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 experience with TensorRT, AIMET, ONNX runtimes.
  • Experience with low-level programming (CUDA kernels, C++, or BLAS subroutines) for inference logic.
  • Experience with profiling tools like torch profiler and nvidia nsight.

• Leadership: Strong team player with excellent communication skills to drive complex, cross-functional efforts in a fast-paced environment.

How to distinguish yourself:

• A strong track record of publications in top-tier venues such as MLSys, ICML, NeurIPS, or ISCA.
• Significant and direct industry experience in a related domain.
• Active participation and contributions to relevant open-source projects.
• Public demonstrations of expertise, including technical talks, presentations, or live demos.