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Staff Software Engineer, Perception Frameworks, Autonomy

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Rivian

2mo ago

  • Job
    Full-time
    Senior Level
  • Software Engineering
  • Palo Alto

AI generated summary

  • You need a MS or Ph.D. in engineering or computer science, 5+ years DL model experience, Python skills, Kubernetes knowledge, MLOps pipeline expertise, communication skills, and familiarity with PySpark, Petastorm, and Parquet.
  • You will design and deploy large-scale systems for model training, develop scalable frameworks, address issues in MLOps processes, collaborate on tooling for optimization, and integrate DL models into larger systems.

Requirements

  • MS or Ph.D. in Electrical, Mechanical, Aerospace Engineering, Computer Science, or a related field
  • 5+ years of experience in training and deploying deep learning models for Computer Vision problems like object detection and segmentation
  • Strong Python programming background and working knowledge of at least one modern DL framework (PyTorch, TensorFlow, Caffe2, or MXNet)
  • Experience with containerization and job management using Kubernetes
  • Research and development experience in large-scale distributed training pipelines, automating complex MLOPs pipelines, or related areas
  • Excellent communication skills and ability to work in a fast-paced development environment
  • Hands-on experience with PySpark, Petastorm, and Parquet is highly desirable
  • Preferred Qualifications:
  • Experience developing and deploying ML pipelines using GCP, Azure ML, or AWS SageMaker
  • Prior experience following industry-standard MLOps and DevOps principles in an automotive/ADAS setting
  • Experience using DL frameworks like PyTorch Lightning or dl-catalyst

Responsibilities

  • Design and deploy large-scale systems for supervised and unsupervised model training paradigms
  • Develop highly scalable and efficient frameworks for different aspects of DL model lifecycle
  • Understand and address shortcomings in existing MLOPs processes and contribute to multiple production data and ML pipelines
  • Collaborate with infrastructure teams to develop tooling and interfaces for rapid benchmarking, analysis, and optimization of model training and serving workflows
  • Work closely with cross-functional teams to integrate DL models into larger systems

FAQs

What are the main responsibilities of a Staff Software Engineer, Perception Frameworks, Autonomy at Rivian?

The main responsibilities of a Staff Software Engineer in this role include designing and deploying large-scale systems for model training, developing scalable frameworks for different aspects of deep learning model lifecycle, addressing shortcomings in MLOPs processes, collaborating with infrastructure teams for benchmarking and optimization, and integrating DL models into larger systems.

What qualifications are required for the Staff Software Engineer position at Rivian?

Qualified candidates should have an MS or Ph.D. in a relevant field, 5+ years of experience in training and deploying deep learning models for Computer Vision problems, proficiency in Python programming and a modern DL framework, experience with containerization and job management using Kubernetes, research and development experience in distributed training pipelines, strong communication skills, and proficiency in tools like PySpark, Petastorm, and Parquet.

What are some preferred qualifications for the Staff Software Engineer position at Rivian?

Preferred qualifications include experience with ML pipelines on cloud platforms like GCP, Azure ML, or AWS SageMaker, following industry-standard MLOps and DevOps principles in an automotive/ADAS setting, and using DL frameworks like PyTorch Lightning or dl-catalyst.

Rivian

Keep the world adventurous forever.

Manufacturing & Electronics
Industry
10,001+
Employees
2009
Founded Year

Mission & Purpose

Doing something different is never easy. It requires courage, optimism and grit. Core to our mission is building a team of adventurous individuals determined to make a positive impact on the world. This means challenging ourselves constantly. Stretching beyond the bounds of conventional thinking. Reframing old problems. Seeking new solutions. And operating comfortably in a space of uncertainty. While our backgrounds are diverse, our team shares a love of the outdoors and a desire to protect it for future generations. Do you like doing the impossible? We’d love to hear from you.