📍 Job Overview

Job Title: Senior Site Reliability Engineer, ML Platforms
Company: NVIDIA
Location: Bengaluru, Karnataka, India
Job Type: On-site, Full-time
Category: DevOps, Site Reliability Engineering
Date Posted: June 25, 2025

🚀 Role Summary

Design, build, and maintain large-scale production systems supporting advanced data science and machine learning applications.
Collaborate with cross-functional teams to plan and implement changes to existing systems while monitoring capacity, latency, and performance.
Apply SRE principles to improve production systems and optimize service SLOs.
Leverage strong background in SRE practices, systems, networking, coding, capacity management, cloud operations, and continuous delivery.

📝 Enhancement Note: This role requires a deep understanding of large-scale distributed systems and the ability to work in a dynamic and collaborative environment. Familiarity with machine learning platforms and data science workflows is a plus.

💻 Primary Responsibilities

System Design & Architecture: Design and implement scalable, reliable, and efficient systems to support machine learning workloads.
Incident Management: Troubleshoot and resolve complex issues, automate repetitive tasks, and proactively identify potential outages.
Capacity Planning: Monitor and manage system capacity, ensuring optimal resource utilization and minimal downtime.
Automation & Tooling: Develop tools and automation to reduce operational overhead and eliminate manual tasks.
Collaboration & Communication: Work closely with data science and engineering teams to understand their needs and provide reliable, performant services.

📝 Enhancement Note: This role involves a significant amount of problem-solving, root cause analysis, and optimization. Strong technical skills and a proactive approach to system management are essential.

🎓 Skills & Qualifications

Education: Master's or Bachelor's degree in Computer Science, Electrical Engineering, or a related field, or equivalent experience.

Experience: Minimum of 6+ years of experience in SRE, Cloud platforms, or DevOps with large-scale microservices in production environments.

Required Skills:

Proficiency in Python, Go, or other programming languages.
Strong understanding of SRE principles, including error budgets, SLOs, and SLAs.
Experience with incident, change, and problem management processes.
Hands-on experience with scaling distributed systems in public, private, or hybrid cloud environments.
Familiarity with streaming data infrastructure services, such as Kafka and Spark.
Expertise in building and operating large-scale observability platforms for monitoring and logging (e.g., ELK, Prometheus).

Preferred Skills:

Experience operating large-scale distributed systems with strong SLAs.
Excellent coding skills in Python and Go, with extensive experience operating data platforms.
Knowledge of CI/CD systems, such as Jenkins and GitHub Actions.
Familiarity with Infrastructure as Code (IaC) methodologies and tools.
Excellent interpersonal skills for identifying and communicating data-driven insights.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Demonstrate experience in designing, building, and maintaining large-scale production systems.
Showcase your ability to troubleshoot and resolve complex issues, automate tasks, and optimize system performance.
Highlight your experience with streaming data infrastructure services and observability platforms.

Technical Documentation:

Provide detailed documentation of your past projects, including system architecture, design decisions, and performance metrics.
Include code samples and explanations of your problem-solving approach and optimization techniques.

📝 Enhancement Note: As this role involves a significant amount of system design and architecture, it's crucial to showcase your ability to make informed decisions and optimize system performance.

💵 Compensation & Benefits

Salary Range: INR 1,800,000 - 2,500,000 per annum (Estimated based on industry standards for Senior SRE roles in Bengaluru)

Benefits:

Competitive health, dental, and vision insurance plans.
Retirement savings plans with company match.
Generous time-off policies, including vacation, sick leave, and holidays.
Employee stock purchase plan.
Tuition reimbursement and professional development opportunities.
On-site amenities, such as fitness centers, cafes, and shuttle services.

Working Hours: Full-time, typically 40 hours per week. Flexible scheduling and remote work options may be available for some roles.

📝 Enhancement Note: Salary and benefits information are estimated based on market research and may vary depending on the candidate's experience and qualifications.

🎯 Team & Company Context

🏢 Company Culture

Industry: Semiconductor and software, focusing on AI, machine learning, and high-performance computing.

Company Size: Large (over 20,000 employees), with a global presence and a strong focus on innovation and collaboration.

Founded: 1993, with a rich history of groundbreaking developments in AI, HPC, and visualization.

Team Structure:

The Data Science & ML Platforms team is part of the AI Enterprise group, working closely with data science, engineering, and product teams.
The team consists of SREs, software engineers, data engineers, and data scientists, collaborating to build and maintain reliable, scalable, and performant machine learning platforms.

Development Methodology:

Agile and iterative development processes, with a focus on continuous integration, delivery, and improvement.
Strong emphasis on collaboration, code reviews, and pair programming.
Regular team meetings, stand-ups, and retrospectives to ensure alignment and continuous improvement.

Company Website: NVIDIA

📝 Enhancement Note: NVIDIA's culture is driven by innovation, collaboration, and a passion for solving complex problems. The company values diversity, intellectual curiosity, problem-solving, and openness.

📈 Career & Growth Analysis

Web Technology Career Level: Senior Site Reliability Engineer, with a focus on large-scale distributed systems, machine learning platforms, and data science workflows.

Reporting Structure: This role reports directly to the Engineering Manager of the Data Science & ML Platforms team, with a matrixed reporting structure to other teams within the AI Enterprise group.

Technical Impact: Senior SREs in this role have a significant impact on the reliability, performance, and scalability of NVIDIA's machine learning platforms, directly contributing to the success of data science and AI initiatives across the company.

Growth Opportunities:

Technical Growth: Deepen your expertise in machine learning platforms, data science workflows, and large-scale distributed systems.
Leadership Growth: Develop your leadership skills by mentoring junior team members, driving team projects, and contributing to cross-functional initiatives.
Architecture & Design: Gain experience in designing and implementing complex, scalable, and performant systems, with the opportunity to influence the architecture of NVIDIA's machine learning platforms.

📝 Enhancement Note: This role offers significant growth opportunities, both technically and in terms of leadership development. NVIDIA's culture of innovation and collaboration provides ample opportunities for learning, growth, and impact.

🌐 Work Environment

Office Type: On-site, with a modern, collaborative workspace designed to facilitate teamwork and innovation.

Office Location(s): Bengaluru, with remote work options available for some roles.

Workspace Context:

Collaboration: Open floor plans, meeting rooms, and collaboration spaces to encourage teamwork and communication.
Equipment: High-end workstations, multiple monitors, and specialized software tools to support machine learning workloads.
Accessibility: On-site amenities, such as fitness centers, cafes, and shuttle services, to ensure a comfortable and convenient work environment.

Work Schedule: Full-time, typically 40 hours per week. Flexible scheduling and remote work options may be available for some roles.

📝 Enhancement Note: NVIDIA's work environment is designed to foster collaboration, innovation, and work-life balance. The company offers a range of benefits and perks to support employee well-being and productivity.

📄 Application & Technical Interview Process

Interview Process:

Phone Screen: A brief phone call to discuss your background, experience, and motivation for the role.
Technical Deep Dive: A detailed technical conversation focused on your experience with SRE principles, system design, and problem-solving.
System Design Challenge: A hands-on exercise to evaluate your ability to design and optimize large-scale distributed systems.
Behavioral & Cultural Fit: A discussion to assess your communication skills, problem-solving approach, and cultural fit within the team.

Portfolio Review Tips:

Highlight your experience with large-scale distributed systems, machine learning platforms, and data science workflows.
Showcase your ability to troubleshoot and resolve complex issues, automate tasks, and optimize system performance.
Include detailed documentation of your past projects, including system architecture, design decisions, and performance metrics.

Technical Challenge Preparation:

Brush up on your knowledge of SRE principles, system design, and problem-solving techniques.
Familiarize yourself with NVIDIA's products, services, and machine learning platforms.
Prepare for hands-on exercises and system design challenges, focusing on large-scale distributed systems and machine learning workloads.

ATS Keywords: [See the comprehensive list of ATS keywords at the end of this document]

📝 Enhancement Note: NVIDIA's interview process is designed to assess your technical skills, problem-solving approach, and cultural fit within the team. Preparation and practice are essential for success in the technical deep dive, system design challenge, and behavioral & cultural fit discussions.

🛠 Technology Stack & Web Infrastructure

Programming Languages:

Python, Go, and other relevant languages for scripting, automation, and tool development.

Cloud Platforms:

AWS, GCP, and Azure, with a strong focus on hybrid and multi-cloud environments.

Distributed Systems & Frameworks:

Kubernetes, OpenStack, Kafka, Spark, and other relevant tools for building and operating large-scale distributed systems.

Monitoring & Logging:

Prometheus, ELK Stack, Grafana, and other relevant tools for monitoring, alerting, and log management.

CI/CD & Automation:

Jenkins, GitHub Actions, and other relevant tools for continuous integration, delivery, and automation.

📝 Enhancement Note: NVIDIA's technology stack is designed to support large-scale distributed systems, machine learning platforms, and data science workflows. Familiarity with these tools and technologies is essential for success in this role.

👥 Team Culture & Values

NVIDIA's Core Values:

Innovation: NVIDIA values innovation and encourages employees to think creatively and push the boundaries of what's possible.
Collaboration: NVIDIA fosters a culture of collaboration, with a strong emphasis on teamwork, communication, and knowledge sharing.
Integrity: NVIDIA expects its employees to act with integrity, honesty, and a commitment to ethical business practices.
Performance: NVIDIA rewards excellence and encourages employees to strive for continuous improvement and high performance.

Team Culture:

The Data Science & ML Platforms team values collaboration, innovation, and a customer-centric approach to problem-solving.
The team is composed of diverse backgrounds, skills, and experiences, with a strong emphasis on learning, growth, and continuous improvement.
The team fosters a culture of blameless postmortems, iterative improvement, and risk-taking, with a focus on driving meaningful change and impact.

📝 Enhancement Note: NVIDIA's culture is driven by innovation, collaboration, and a passion for solving complex problems. The company values diversity, intellectual curiosity, problem-solving, and openness.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Large-Scale Distributed Systems: Design, build, and maintain large-scale distributed systems supporting advanced data science and machine learning applications.
Machine Learning Platforms: Develop and optimize machine learning platforms for training and inferencing, with a focus on scalability, performance, and reliability.
Data Science Workflows: Collaborate with data science teams to understand their needs and provide reliable, performant services for data processing, analysis, and visualization.
Observability & Monitoring: Build and operate large-scale observability platforms for monitoring and logging, with a focus on proactive issue identification and resolution.

Learning & Development Opportunities:

Technical Skill Development: Deepen your expertise in machine learning platforms, data science workflows, and large-scale distributed systems.
Conference Attendance & Certification: Attend industry conferences, obtain relevant certifications, and engage with the broader data science and machine learning community.
Mentorship & Leadership Development: Participate in mentorship programs, develop your leadership skills, and contribute to cross-functional initiatives.

📝 Enhancement Note: This role offers significant technical challenges and growth opportunities, with a focus on large-scale distributed systems, machine learning platforms, and data science workflows. NVIDIA's culture of innovation, collaboration, and continuous learning provides ample opportunities for professional development and growth.

💡 Interview Preparation

Technical Questions:

System Design: Prepare for system design questions focused on large-scale distributed systems, machine learning platforms, and data science workflows.
Problem-Solving: Brush up on your problem-solving skills, with a focus on root cause analysis, optimization, and automation.
SRE Principles: Review SRE principles, including error budgets, SLOs, and SLAs, and be prepared to discuss their application in large-scale distributed systems.

Company & Culture Questions:

Research NVIDIA's products, services, and machine learning platforms, and be prepared to discuss their impact on data science and AI initiatives.
Familiarize yourself with NVIDIA's core values and company culture, and be prepared to discuss how you embody these values in your work.
Prepare for behavioral questions that assess your communication skills, problem-solving approach, and cultural fit within the team.

Portfolio Presentation Strategy:

System Design: Present your experience with large-scale distributed systems, machine learning platforms, and data science workflows, highlighting your ability to design and optimize complex systems.
Problem-Solving: Showcase your problem-solving skills, with a focus on root cause analysis, optimization, and automation, using relevant examples from your past projects.
Collaboration & Communication: Demonstrate your ability to work effectively with cross-functional teams, communicate technical concepts clearly, and drive meaningful change and impact.

📝 Enhancement Note: NVIDIA's interview process is designed to assess your technical skills, problem-solving approach, and cultural fit within the team. Preparation and practice are essential for success in the technical deep dive, system design challenge, and behavioral & cultural fit discussions.

📌 Application Steps

To apply for this Senior Site Reliability Engineer, ML Platforms position at NVIDIA:

Submit Your Application: Visit the NVIDIA careers page and submit your application, including your resume, cover letter, and any relevant portfolio materials.
Prepare Your Portfolio: Highlight your experience with large-scale distributed systems, machine learning platforms, and data science workflows, showcasing your ability to design, build, and maintain reliable, scalable, and performant systems.
Research NVIDIA: Familiarize yourself with NVIDIA's products, services, and machine learning platforms, and be prepared to discuss their impact on data science and AI initiatives.
Prepare for Technical Interviews: Brush up on your knowledge of SRE principles, system design, and problem-solving techniques, focusing on large-scale distributed systems and machine learning workloads.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

ATS Keywords:

Programming Languages: Python, Go, Perl, Ruby, Java, C++, JavaScript, TypeScript
Cloud Platforms: AWS, GCP, Azure, Hybrid Cloud, Multi-Cloud
Distributed Systems & Frameworks: Kubernetes, OpenStack, Kafka, Spark, Hadoop, Docker, Terraform, Ansible
Monitoring & Logging: Prometheus, ELK Stack, Grafana, Nagios, Zabbix, Datadog, New Relic
CI/CD & Automation: Jenkins, GitHub Actions, CircleCI, Travis CI, Bamboo, GitLab CI/CD
Databases: MySQL, PostgreSQL, MongoDB, Redis, Cassandra, HBase, BigQuery, Redshift, Snowflake
Web Technologies: HTML, CSS, JavaScript, React, Angular, Vue.js, Node.js, Express, Flask, Django
Machine Learning: TensorFlow, PyTorch, scikit-learn, Keras, XGBoost, LightGBM, CatBoost, AWS SageMaker, GCP AI Platform, Azure Machine Learning
Industry Terms: Site Reliability Engineering, DevOps, Infrastructure as Code, Continuous Integration, Continuous Delivery, Continuous Deployment, Microservices, Serverless Architecture, Containerization, Orchestration, Automation, Scripting, Configuration Management, Incident Management, Problem Management, Change Management, Release Management, Deployment, Scalability, Performance, Availability, Reliability, Observability, Monitoring, Logging, Alerting, Notifications, Troubleshooting, Root Cause Analysis, Optimization, Problem-Solving, System Design, Architecture, Data Science, Machine Learning, AI, Big Data, Cloud Computing, Hybrid Cloud, Multi-Cloud, Serverless Computing, Serverless Architecture, Serverless Applications, Serverless Functions, Serverless Platforms, Serverless Technologies, Serverless Computing, Serverless Architecture, Serverless Applications, Serverless Functions, Serverless Platforms, Serverless Technologies

This comprehensive list of ATS keywords is designed to help web development and server administration candidates optimize their resumes and portfolios for this Senior Site Reliability Engineer, ML Platforms role at NVIDIA.

Senior Site Reliability Engineer, ML Platforms