Performance Engineer SMTS/LMTS/PMTS - Salesforce Einstein Platform
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Job CategorySoftware Engineering
We’re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good – you’ve come to the right place.
The AI Platform/Einstein Cloud Performance Engineering team is currently seeking a Performance Engineer.
If you’re fired up about software performance, automating everything, and working with great engineers, this is the job for you! If you are a developer that is passionate about performance, or a performance tester that is also interested in contributing to performance improvements, we would love to hear from you.
Salesforce, the Customer Success Platform and world's #1 CRM, empowers companies to connect with their customers in a whole new way. We are the fastest growing of the top 10 enterprise software companies, the World's Most Innovative Company according to Forbes, and one of Fortune's 100 Best Companies to Work For six years running. The growth, innovation, and Aloha spirit of Salesforce are driven by our incredible employees who thrive on delivering success for our customers while also finding time to give back through our 1/1/1 model, which leverages 1% of our time, equity, and product to improve communities around the world. Salesforce is a team sport, and we play to win. Join us!
Come and be a part of our AI Platform Performance Engineering team, where you can specialize in Salesforce's AI Platform! As part of the Salesforce Gen-AI initiative, you will help our customers make the most of Einstein GPT with state-of-the-art AI technology using Large Language, Toxicity, and PII models while collaborating closely with the team and cloud to implement performance and scale initiatives to support our ambitious plans for massive growth this year. Our team is known for being highly technical, friendly, and exceptionally collaborative. Join us for an exciting and rewarding experience!
Our primary focus involves benchmarking the performance and scale of Gen AI models, CTS, and performance optimizations. You will have the opportunity to design performance experiments, collect and analyze data. Develop power tools and automation frameworks for continuous integration, performance measurement and analysis. Contribute to the design and velocity of our global products. Optimize our systems end-to-end by advising infrastructure design, server and application development/tuning, solving complex cluster architecture and data storage challenges that impact features within Artificial Intelligence, Platform, API’s and mobile UI experiences.
The candidate must have the technical prowess to write fast and efficient code, find inefficiencies and influence others to make performance improvements. In addition, this engineer needs to measure and demonstrate performance gains in a scientific way, by building/running workloads or simulations.
You are the performance expert for your scrum team or for a larger program, and should own your team’s performance related problems on production.
- Scope performance engineering efforts, prioritize test cases, develop solid project milestones
- Design and run extensive overhead, scalability, stability, and stress tests using industry standard tools and custom solutions
- Develop robust benchmark workloads based on production traffic patterns and anticipated feature usage with varying load levels and working sets for different models
- Analyze and summarize test results in reports, capacity planning / best practice guides in AI space
- Investigate and analyze performance in production
- Develop and review test plans, results analysis, capacity planning, CTS guides
- Automate test scenarios using Python or Java
- Experience in AWS SpringBoot, JMeter, K6, Shell Scripting, PL/SQL, Jenkins, and Jira.
- Generate data for weekly charts trending product performance against target goals and project milestones
- 6+ years of software development experience — Java preferred
- 3+ years in performance engineering
- Hands-on experience with performance measurement, profiling, analysis, and optimization
- Experience conducting benchmark and/or work-load testing using automation tools and frameworks for performance measurement and analysis (Jmeter)
- Working knowledge of Linux operating system
- Experience performing simulation or lab experimentation to analyze application/ system performance
- Experience working with public cloud technologies, particularly AWS and GCP, as well as knowledge of other cloud platforms.
- Hands-on experience with Kubernetes and AWS EKS (Elastic Kubernetes Service) for container orchestration.
- Practical knowledge of machine learning platforms such as AWS SageMaker and Vertex AI.
- Previous involvement in machine model training, deployment, and inferencing performance testing and optimization.
- Understanding of back-end systems, APIs, developer tools, and compilers.
- Ability to work cross-functionally to articulate, measure and solve performance issues
- Passion for continuous improvement in building knowledge base technically and professionally
- Outstanding attention to detail and communication skills
- Passion for learning and working with others to improve yourself, the team, the product, the company
- Fast learner, ability to work in a distributed and fast-paced environment
- Experience with cloud-based platform and infrastructure (Kubernetes, AWS, GCP, Azure)
- Knowledge of database fundamentals: SQL, schema, internals (Oracle preferred, AWR, ASH)
- JVM and garbage collector tuning, heap dump analysis experience
- Experience with the following performance tools: JMeter, YourKit, Java Mission Control
- Knowledge of back-end systems and API’s, developer tools and compilers
- Knowledge of statistical analysis and experimental design techniques
- Experience analyzing and interpreting large volume of production data using Splunk to understand throughput, latency, memory and CPU utilization
- Familiarity with Python, collaborative tools, notebooks, and machine learning frameworks such as PyTorch, sci-kit-learn, and TensorFlow. Expertise in DeepSpeed and FasterTransformers optimizers is a plus.
- Collaboration with cross-cloud performance engineering teams to ensure end-to-end application performance optimization.
- Expertise in cost-to-serve analysis, including proficiency in Tableau to create dashboards, knowledge of ETL tools, SQL, and big data processing.
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