bangalore, India
Live, first seen by us 24 days agoWork setup in the employer’s posting: On-siteFrom the employer’s posting
Employment type in the employer’s posting: Full-timeFrom the employer’s posting
Manage and optimize DL infrastructure across Azure storage and hybrid cloud and on-premise clusters
Architect high-performance data loaders for multimodal camera, radar and temporal data
Modernize data pipelines with Ray and Kubernetes for caching, shuffling and oversampling
Design and maintain GitHub Workflows and CI pipelines for feature teams
Build Grafana KPI dashboards for compute usage, GPU efficiency and system health
Manage and optimize the entire DL infrastructure, including Azure Blob Storage integration, VNET setups, and hybrid compute resources
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
Design and maintain robust GitHub Workflows and CI pipelines for new and existing feature teams
Develop KPI dashboards using Grafana to monitor compute usage, GPU efficiency, unit test durations, and overall system health
●Big Data & Optimization: Proficiency with high-performance data processing tools such as Ray, PyArrow, and SQL. Experience optimizing data loading bottlenecks for
Bachelor’s degree in Computer Science, Electrical Engineering, or a related field. An advanced degree is an advantage.
●Soft Skills: Strong problem-solving abilities, proactiveness, and ownership of complex topics. Ability to adapt quickly to new technologies and work collaboratively in a supportive, high-performance team.
Required Qualifications ●Education: Bachelor’s degree in Computer Science, Electrical Engineering, or a related field. An advanced degree is an advantage. ●Experience: 5+ years of industry experience in MLOps, Data Engineering, or Software Infrastructure, with a focus on Deep Learning systems.
Qualifications Bachelor’s degree in Computer Science, Electrical Engineering, or a related field. An advanced degree is an advantage. Additional Information
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Day 24
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●Experience: 5+ years of industry experience in MLOps, Data Engineering, or Software Infrastructure, with a focus on Deep Learning systems. ●Programming & Software Design: Expert-level proficiency in Python with a strong emphasis on clean software design, object-oriented programming, and architectural patterns. ●Infrastructure & Orchestration: Deep hands-on experience with Kubernetes, Docker, and Cloud platforms (specifically Azure ML, Azure Storage/Networking).
●Modernize data processing pipelines using Ray and Kubernetes to parallelize data caching, shuffling, and oversampling. ●Leverage PyArrow and SQL to optimize data consumption and integration with data loops. ●Implement efficient dataset update strategies (handling deltas) and ensure seamless integration of new tasks into the multimodal multi-task network.
●Infrastructure & Orchestration: Deep hands-on experience with Kubernetes, Docker, and Cloud platforms (specifically Azure ML, Azure Storage/Networking). ●Big Data & Optimization: Proficiency with high-performance data processing tools such as Ray, PyArrow, and SQL. Experience optimizing data loading bottlenecks for GPU training.
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. Responsibilities
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.
●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.
●Programming & Software Design: Expert-level proficiency in Python with a strong emphasis on clean software design, object-oriented programming, and architectural patterns. ●Infrastructure & Orchestration: Deep hands-on experience with Kubernetes, Docker, and Cloud platforms (specifically Azure ML, Azure Storage/Networking). ●Big Data & Optimization: Proficiency with high-performance data processing tools such as Ray, PyArrow, and SQL. Experience optimizing data loading bottlenecks for
●Design and maintain robust GitHub Workflows and CI pipelines for new and existing feature teams. ●Develop KPI dashboards using Grafana to monitor compute usage, GPU efficiency, unit test durations, and overall system health. ●Manage dependency updates (Torch upgrades, Ubuntu updates, Hydra maintenance, Dependabots) to ensure a secure and modern stack.
GPU training. ●DevOps & Monitoring: Experience setting up complex CI/CD pipelines (GitHub Actions) and observability stacks (Grafana, Prometheus). ●Soft Skills: Strong problem-solving abilities, proactiveness, and ownership of complex topics. Ability to adapt quickly to new technologies and work collaboratively in a supportive, high-performance team.
●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. ●Leverage PyArrow and SQL to optimize data consumption and integration with data loops.
Additional Information - Deep Learning Knowledge: While deep algorithm knowledge is not mandatory, a strong understanding of Deep Learning workflows (PyTorch, training loops, multi-task learning) is highly beneficial. - Embedded Systems: Experience with deploying or evaluating models on embedded hardware (Qualcomm QNN, etc.) or setting up hardware-in-the-loop pipelines.
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Yes. We’ve followed one other Senior Machine learning Engineer job there since May 2026. It came down after 62 days.
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
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.
Responsibilities
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.
●Leverage PyArrow and SQL to optimize data consumption and integration with data loops.
●Implement efficient dataset update strategies (handling deltas) and ensure seamless integration of new tasks into the multimodal multi-task network.
CI/CD, Monitoring & Quality:
●Design and maintain robust GitHub Workflows and CI pipelines for new and existing feature teams.
●Develop KPI dashboards using Grafana to monitor compute usage, GPU efficiency, unit test durations, and overall system health.
●Manage dependency updates (Torch upgrades, Ubuntu updates, Hydra maintenance, Dependabots) to ensure a secure and modern stack.
●Drive software design excellence by performing thorough code reviews (PRs) and enforcing high standards in software architecture.
Embedded & Evaluation:
●Establish scalable evaluation pipelines for embedded targets, specifically for QNN boards and other edge devices.
●Collaborate with feature teams to support model compression experiments and on-target performance verification.
Required Qualifications
●Education: Bachelor’s degree in Computer Science, Electrical Engineering, or a related field. An advanced degree is an advantage.
●Experience: 5+ years of industry experience in MLOps, Data Engineering, or Software Infrastructure, with a focus on Deep Learning systems.
●Programming & Software Design: Expert-level proficiency in Python with a strong emphasis on clean software design, object-oriented programming, and architectural patterns.
●Infrastructure & Orchestration: Deep hands-on experience with Kubernetes, Docker, and Cloud platforms (specifically Azure ML, Azure Storage/Networking).
●Big Data & Optimization: Proficiency with high-performance data processing tools such as Ray, PyArrow, and SQL. Experience optimizing data loading bottlenecks for
GPU training.
●DevOps & Monitoring: Experience setting up complex CI/CD pipelines (GitHub Actions) and observability stacks (Grafana, Prometheus).
●Soft Skills: Strong problem-solving abilities, proactiveness, and ownership of complex topics. Ability to adapt quickly to new technologies and work collaboratively in a supportive, high-performance team.
Bachelor’s degree in Computer Science, Electrical Engineering, or a related field. An advanced degree is an advantage.
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.
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bangalore, India
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. Responsibilities
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.
No clear work-authorization passage found. Eligibility is unconfirmed.