Applied Computer Vision Engineer- Automated Driving - 2024

12 Hours ago • 4 Years + • Research & Development

About the job

Summary

Develop video perception algorithms for autonomous driving vehicles, focusing on unsolved problems. Analyze data to explain algorithm performance, identify failure patterns, and formulate solutions. Design and execute experiments to test hypotheses, leveraging deep learning techniques. Iterate quickly, working with various data sources and engineering disciplines to improve overall system performance. This role requires strong skills in deep learning, C++, and production machine learning pipelines.
Must have:
  • Strong DL knowledge (3+ yrs)
  • Full-stack vision project experience (3+ yrs)
  • Experience with TensorFlow 2+, PyTorch
  • Python expertise
  • Git experience
Good to have:
  • C++
  • Multi-processing/threading
  • Custom accelerator deployment (OpenVINO, Jetson)
  • Visual Studio Code
  • CUDA, RAPIDS, OpenMP
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Company Description

Bosch Global Software Technologies Private Limited is a 100% owned subsidiary of Robert Bosch GmbH, one of the world's leading global supplier of technology and services, offering end-to-end Engineering, IT and Business Solutions. With over 28,200+ associates, it’s the largest software development center of Bosch, outside Germany, indicating that it is the Technology Powerhouse of Bosch in India with a global footprint and presence in the US, Europe and the Asia Pacific region.

Job Description

A typical day in office:1.You are developing video perception algorithms for unsolved problems in the technology area perception for autonomous drive vehicles.2.You have to explain why something works or not using data. And what next for the things not working with the reduced cost of labelling.3.You have to find that elusive data through innovative algorithms,Your job would involve1.Looking at the nature of the data2.Current AI/DL algorithm performance3.Derive insights or patterns in failures4.Define a hypothesis for the solution making.5.Define Experiments for hypothesis testing6.Define/Review the techniques/methods in deep learning for experiments7.Analyze the results of experiments8.Define the next set of data or experiments forward9.Define the targets for the current sprint10.Review, Agree on the experiments11.Ensure delivery with the right process for qualityQualifications:Strong technical background. At least a Bachelors/Master of Engineering in Computer Science or Electrical Sciences or related areas with 4 years of industry experience in designing and implementing perception algorithms.

You are a skilled software engineer with experience in C++ and strong problem-solving skills.

You are passionate about solving real-world robotics problems, and you have ideally worked on autonomous robots before. Prior knowledge of working with deep neural networks is a strong plus.

A team player. You take ownership and work with the team to deliver exceptional results.

You are interested in the performance of the entire system across engineering disciplines.

Ability to build and iterate quickly (AGILE mindset). You enjoy working fast and smart, and you are comfortable in the earlier stages of developing an algorithm from scratch.

Hands on. You dig deep into important details such as the sensor driver if it improves the overall system. You like working with production machine learning pipelines, from dataset collection and labeling to training and validation.

Great communicator. You have experience writing clear, concise, and detailed documentation.

Should be able to explain technical solutions clearly to non-technical/different audience

Problem solver: You are expected to solve systemic problems.

Skills:Must have:1. Strong knowledge in DL (3yrs min)(few-shot learning, transfer learning, unsupervised and semi-supervised methods, active learning and semi-automated data annotation, large scale image and video detection and recognition)2. Hands on experience in Full stack Vision/Image related projects in production (3yrs min)

Data: Spark, Hadoop (user level), visualization, data preparation, data handling, understanding data

Model: tensor flow 2+ and PyTorch

Deployment: docker, flask, kubernets

Cloud deployment: Azure/AWS3. Experience in Python4. Tool:

GiT

Working with GPU

Linux

Good to have:Coding:

Multi-processing and multi-threading (application)

Understanding of and C++ (for development and deployment)

Sound knowledge of custom accelerator deployment (OpenVINO, Jetson)

Tooling:

Good understanding of Visual studio code

CUDA, RAPIDS, OpenMP, Distributed Systems and HPC application design

Qualifications

B.E/B.Tech

Additional Information

6+

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