Senior Machine learning Engineer

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 27,000+ 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

As a Senior Machine learning Engineer in the Perception team, you will play a pivotal role in building and maintaining the backbone of our L2+ ADAS stack. This senior role calls for an experienced engineer who can think critically, execute independently, and deliver results on scalable deep learning infrastructure, optimize massive data ingestion pipelines, and ensure maximum efficiency across our compute clusters.

You will be responsible for the entire DL infrastructure lifecycle—from managing Azure storage and hybrid Kubernetes clusters to designing efficient data loaders for multimodal training. You will work at the intersection of infrastructure, data engineering, and deep learning, enabling feature teams to train complex models (single frame, temporal, and multimodal) with speed and reliability. Your ability to solve abstract infrastructure challenges and apply "T-shaped" expertise—going deep in areas like infrastructure, multitask deep learning among others while maintaining breadth in software design—will be key to our success.

Deep Learning Infrastructure & Compute:

●Manage and optimize the entire DL infrastructure, including Azure Blob Storage integration, VNET setups, and hybrid compute resources (Cloud and On-premise/Frankfurt clusters).

●Lead performance investigations and benchmarking for next-gen hardware (e.g., comparing H200 vs. H100, Azure native vs. deployment nodes) to ensure cost and speed efficiency.

●Maintain and scale Kubernetes clusters for training and inference workloads.

Data Pipelines & Efficient Loading:

●Architect and develop high-performance data loaders for complex multimodal datasets (camera, radar, temporal/non-temporal data).

●Modernize data processing pipelines using Ray and Kubernetes to parallelize data caching, shuffling, and oversampling.