Sr. SW Engineer

Visa•Foster City, CA

11d•Hybrid

About The Position

Visa’s Technology Organization is a community of problem solvers and innovators reshaping the future of commerce. We operate the world’s most sophisticated processing networks capable of handling more than 65k secure transactions a second across 80M merchants, 15k Financial Institutions, and billions of everyday people. While working with us you’ll get to work on complex distributed systems and solve massive scale problems centered on new payment flows, business and data solutions, cyber security, and B2C platforms. The Opportunity: We are looking for dedicated, curious, and energetic Software Engineers who embrace solving complex challenges on a global scale. As a Visa Software Engineer, you will be an integral part of a multi-functional development team inventing, designing, building, and testing software products that reach a truly global customer base. While building components of innovative payment technology, you will get to see your efforts shaping the digital future of monetary transactions. Essential Functions: Run high-throughput simulations and analyze results to optimize component design. Improve verification and validation turn around by automating procedures. Design and execute performance and resiliency tests for distributed components. Collaborate with developers to ensure functional readiness, analyzing code hotspot and effective observability / instrumentation for monitoring(metrics) & troubleshooting(logging). Validate integration performance to address distributed computing challenges. Monitor, identify, and resolve performance bottlenecks to develop capacity models. Optimize CPU, memory, disk, network, OS, and application software utilization. Establish and promote performance best practices and emphasis non-functional requirements during requirements gathering phase. Guide development teams on technical solutions to enhance application performance. Document design decisions, best practices, and lessons learned internally. Troubleshoot and resolve performance issues in production. Contribute to capacity planning and disaster recovery efforts. Leverage generative AI tools and frameworks to automate performance analysis, generate synthetic test data, and enhance root cause diagnostics. Integrate AI-driven insights into performance monitoring dashboards for predictive alerting and anomaly detection. Apply natural language processing (NLP) techniques to automate documentation, extract actionable insights from logs, and facilitate faster troubleshooting. Collaborate on the development and deployment of custom AI models to optimize application and infrastructure performance and continuously improve testing strategies. Performance Testing & Analysis (Foundational): Assist in executing performance, load, and basic stress tests under guidance from senior engineers. Use established tools to collect and interpret performance metrics such as response time, throughput, CPU, and memory usage. Help identify simple performance bottlenecks and document findings clearly. GenAI‑Assisted Engineering Practices: Use GenAI tools to: Generate performance test scripts, test data, and scenarios under supervision. Summarize logs, metrics, and test results into clear reports or dashboards. Assist in analyzing performance trends and suggest potential optimization areas. Apply GenAI for code comprehension, helping understand unfamiliar services, APIs, or performance-related code paths. Follow team guidelines for responsible and secure use of GenAI, including data privacy and validation of AI‑generated outputs. Tooling & Automation Support: Maintain and update existing performance test scripts and frameworks. Help integrate performance tests into CI/CD pipelines with support from senior team members. Use GenAI to assist with script refactoring, documentation, and test coverage improvements. Observability & Monitoring (Introductory): Learn to use monitoring and APM tools to observe system behavior under load. Assist in tracking basic KPIs (latency, error rate, resource usage). Use GenAI to help interpret monitoring dashboards and draft concise summaries for the team. Collaboration & Learning: Work closely with developers, QA, and DevOps engineers to understand performance expectations. Participate in design discussions and code reviews as a learner, focusing on performance considerations. Ask questions and incorporate feedback to continuously improve performance engineering skills. Documentation & Reporting: Document test setups, results, and observations in clear, structured formats. Use GenAI tools to help create readable documentation, runbooks, and test summaries. Maintain performance baselines and version-controlled test artifacts. Production & Pre‑Production Support (Exposure Level): Assist with pre-release performance validation. Observe and support performance investigations during incidents, learning root-cause analysis techniques. Use GenAI to help summarize incident data and draft post-incident notes. Quality, Security & Reliability Awareness: Learn how security features (authentication, encryption, rate limiting) impact system performance. Follow best practices to ensure performance tests do not expose sensitive data, especially when using GenAI tools. Growth & Skill Development: Continuously learn performance testing fundamentals, tools, and cloud concepts. Stay curious about emerging GenAI capabilities relevant to testing, analysis, and developer productivity. Build foundational knowledge in system performance, scalability, and reliability engineering. This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager.

Requirements

2+ years of relevant work experience and a Bachelors degree, OR 5+ years of relevant work experience
Proficient knowledge of hardware, software, and distributed applications environments, with a solid understanding of relevant capacity and performance indicators.
Demonstrated expertise with operating system internals, file systems, disk/storage, and networking protocols.
Familiarity with functional programming languages such as Go or Scala, or an aptitude for quickly learning new programming languages.
Experience utilizing scripting languages (e.g., Python) and open-source performance tools.
Working knowledge of the software development lifecycle, including version control, build processes, testing, and code release procedures.
Strong verbal and written communication skills.
Knowledge of generative AI frameworks and tools for building and optimizing machine learning models.
Understanding of integrating GenAI solutions into distributed systems and evaluating their impact on system performance and scalability.
Experience designing, monitoring, and tuning the performance of AI-driven workloads, including inference latency, throughput, and resource utilization.
Ability to assess, benchmark, and troubleshoot performance, Chaos-resiliency in real-time or batch AI processing solutions.
Familiarity with responsible AI practices, including monitoring fairness, explainability, and the ethical implications of deploying generative AI solutions.

Nice To Haves

3 or more years of work experience with a Bachelor’s Degree or more than 2 years of work experience with an Advanced Degree (e.g. Masters, MBA, JD, MD)
Degree in Computer Science, Software Engineering, Information Technology, or a related field
1–2 years of relevant experience in:
Software engineering with Performance Engineering mindset
Test automation
Systems, DevOps, or SRE support roles
Background in distributed databases, distributed computing, and high-frequency transaction environments is highly advantageous.

Responsibilities

Run high-throughput simulations and analyze results to optimize component design.
Improve verification and validation turn around by automating procedures.
Design and execute performance and resiliency tests for distributed components.
Collaborate with developers to ensure functional readiness, analyzing code hotspot and effective observability / instrumentation for monitoring(metrics) & troubleshooting(logging).
Validate integration performance to address distributed computing challenges.
Monitor, identify, and resolve performance bottlenecks to develop capacity models.
Optimize CPU, memory, disk, network, OS, and application software utilization.
Establish and promote performance best practices and emphasis non-functional requirements during requirements gathering phase.
Guide development teams on technical solutions to enhance application performance.
Document design decisions, best practices, and lessons learned internally.
Troubleshoot and resolve performance issues in production.
Contribute to capacity planning and disaster recovery efforts.
Leverage generative AI tools and frameworks to automate performance analysis, generate synthetic test data, and enhance root cause diagnostics.
Integrate AI-driven insights into performance monitoring dashboards for predictive alerting and anomaly detection.
Apply natural language processing (NLP) techniques to automate documentation, extract actionable insights from logs, and facilitate faster troubleshooting.
Collaborate on the development and deployment of custom AI models to optimize application and infrastructure performance and continuously improve testing strategies.
Assist in executing performance, load, and basic stress tests under guidance from senior engineers.
Use established tools to collect and interpret performance metrics such as response time, throughput, CPU, and memory usage.
Help identify simple performance bottlenecks and document findings clearly.
Generate performance test scripts, test data, and scenarios under supervision.
Summarize logs, metrics, and test results into clear reports or dashboards.
Assist in analyzing performance trends and suggest potential optimization areas.
Apply GenAI for code comprehension, helping understand unfamiliar services, APIs, or performance-related code paths.
Follow team guidelines for responsible and secure use of GenAI, including data privacy and validation of AI‑generated outputs.
Maintain and update existing performance test scripts and frameworks.
Help integrate performance tests into CI/CD pipelines with support from senior team members.
Use GenAI to assist with script refactoring, documentation, and test coverage improvements.
Learn to use monitoring and APM tools to observe system behavior under load.
Assist in tracking basic KPIs (latency, error rate, resource usage).
Use GenAI to help interpret monitoring dashboards and draft concise summaries for the team.
Work closely with developers, QA, and DevOps engineers to understand performance expectations.
Participate in design discussions and code reviews as a learner, focusing on performance considerations.
Ask questions and incorporate feedback to continuously improve performance engineering skills.
Document test setups, results, and observations in clear, structured formats.
Use GenAI tools to help create readable documentation, runbooks, and test summaries.
Maintain performance baselines and version-controlled test artifacts.
Assist with pre-release performance validation.
Observe and support performance investigations during incidents, learning root-cause analysis techniques.
Use GenAI to help summarize incident data and draft post-incident notes.
Learn how security features (authentication, encryption, rate limiting) impact system performance.
Follow best practices to ensure performance tests do not expose sensitive data, especially when using GenAI tools.
Continuously learn performance testing fundamentals, tools, and cloud concepts.
Stay curious about emerging GenAI capabilities relevant to testing, analysis, and developer productivity.
Build foundational knowledge in system performance, scalability, and reliability engineering.