Role Description We are looking for a Software Development Engineer in Test to join our team, focused on validating a Java codebase migration. Unlike a traditional QA role, this position requires strong Java development skills. You will: - Design and implement test strategies for the Java 8 to Java 21 migration, covering compatibility, regression, and behavioral validation. - Write and maintain automated test code in Java for API, backend services, and integration layers. - Build, extend, and own our existing Java-based automation framework running in Jenkins. - Set up and maintain CI/CD pipelines using Pipeline-as-Code (Groovy/Jenkinsfile). - Validate functional and non-functional behavior of migrated services, identifying edge cases and compatibility risks. - Perform API, integration, and regression testing across the application stack. - Collaborate with Java Developers to review code changes, assess migration risks, and define test coverage. - Manage and triage defects with clear root cause analysis and technical documentation. - Maintain test documentation, test cases, and validation reports. Qualifications - 2-4 years of Software Development in Test (SDET), test automation engineering, or backend development experience, with at least 1-2 years writing automation in Java. - Strong Core Java (JSE) skills, multithreading, collections, memory management; must write clean, maintainable test code, not just execute scripts. - Solid understanding of Java version differences, including module system changes, deprecated APIs, and runtime behavioral differences. - Hands-on experience building or extending automation frameworks. - Strong experience in API testing and backend validation using RestAssured, Postman, or similar tools. - Hands-on Jenkins experience, writing and maintaining Jenkinsfiles, integrating test suites into deployment pipelines. - Working SQL knowledge, writing and validating queries, understanding schema design, verifying data integrity in relational databases. - Familiarity with test management and defect tracking tools (Jira, TestRail, or similar). - Strong English communication skills (written and spoken) for collaboration with a US-based team. - Ability to work fully remote under US time zones. Requirements - Experience in fintech or high-transaction systems (payments, ledgers, financial data pipelines). - Familiarity with NoSQL databases and RDBMS interaction via application layers. - Experience with performance/load testing tools (JMeter, K6, or similar). - Understanding of logging, monitoring, and observability tools (ELK, Grafana, Datadog, or similar). - Exposure to AI-assisted development tools applied in a testing or coding context. Benefits - Fully remote position (work from anywhere under the US time zones). - Opportunity to learn modern Java practices on a real migration project with senior mentorship. - Collaborative and supportive team environment.

Title: Research Scientist / Engineer – Training Infrastructure Job Description: Full Time Experienced Job Title: Research Scientist / Engineer – Training Infrastructure Position Type: Full time Location: Palo Alto, CA • Remote - US • Remote - International Salary Range: $220,000 - $300, 000 (USD) Job ID#: 154559 Job Description: We believe that multimodality is critical for intelligence. To go beyond language models and build more aware, capable and useful systems, the next step function change will come from vision. So, we are working on training and scaling up multimodal foundation models for systems that can see and understand, show and explain, and eventually interact with our world to effect change. We are looking for engineers with significant experience solving hard problems in PyTorch, CUDA and distributed systems. You will work alongside the rest of the research team to build & train cutting edge foundation models on thousands of GPUs that are built to scale from the ground up. Responsibilities - Design, implement, and optimize efficient distributed training systems for models with thousands of GPUs - Research and implement advanced parallelization techniques (FSDP, Tensor Parallel, Pipeline Parallel, Expert Parallel) - Build monitoring, visualization, and debugging tools for large-scale training runs - Optimize training stability, convergence, and resource utilization across massive clusters Requirements: - Extensive experience with distributed PyTorch training and parallelisms in foundation model training - Deep understanding of GPU clusters, networking, and storage systems - Familiarity with communication libraries (NCCL, MPI) and distributed system optimization - (Preferred) Strong Linux systems administration and scripting capabilities - (Preferred) Experience managing training runs across >100 GPUs - (Preferred) Experience with containerization, orchestration, and cloud infrastructure About Us: Founded in 2009, IntelliPro is a global leader in talent acquisition and HR solutions. Our commitment to delivering unparalleled service to clients, fostering employee growth, and building enduring partnerships sets us apart. We continue leading global talent solutions with a dynamic presence in over 160 countries, including the USA, China, Canada, Singapore, Japan, Philippines, UK, India, Netherlands, and the EU. IntelliPro, a global leader connecting individuals with rewarding employment opportunities, is dedicated to understanding your career aspirations. As an Equal Opportunity Employer, IntelliPro values diversity and does not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, genetic information, disability, or any other legally protected group status. Moreover, our Inclusivity Commitment emphasizes embracing candidates of all abilities and ensures that our hiring and interview processes accommodate the needs of all applicants. Learn more about our commitment to diversity and inclusivity at https://intelliprogroup.com/. Compensation: The pay offered to a successful candidate will be determined by various factors, including education, work experience, location, job responsibilities, certifications, and more. Additionally, IntelliPro provides a comprehensive benefits package, all subject to eligibility.

• Lead adversarial testing of AI by designing, scoping, and executing red team assessments against our AI ecosystem using a risk-based prioritization approach to discover and address vulnerabilities before they can be exploited. • Innovate in AI attack techniques by combining cutting-edge academic research with proven offensive security methods to establish new Tactics, Techniques, and Procedures, and operationalize emerging research to keep assessments aligned with the state of the art. • Build and scale security tooling using an automation-first philosophy, driving initiatives to shift security testing left by sharing purpose-built tools with AI security stakeholders across Engineering, Research, and Ethics. • Serve as a strategic partner across the company, providing an offensive security perspective to guide product development, support corporate governance, and contribute to policies such as Salesforce's Generative AI Security Standard.

• In your role as Senior Research (Machine Learning) Engineer, you'll be at the heart of building the next generation of generative video technology. • Your work will push the boundaries of what's possible in AI-driven storytelling, helping Canva users go from idea to finished video with tools that feel genuinely magical. • By developing and shipping cutting-edge video generation models, you'll help redefine how hundreds of millions of people create and tell stories visually, shaping one of the most exciting product frontiers in AI. • Partnering with Research Scientists on research in multimodal generative AI, translating theory into practical applications • Porting experimental Python code to scalable, testable, and production-grade solutions, incorporating performance benchmarks and robust testing practices • Designing, developing, and implementing innovative model architectures and algorithms for multimodal design generation • Building sustainable ML pipelines that support continuous integration, deployment, and monitoring of generative models in production environments • Optimizing and scaling models for efficiency, latency, and throughput across large distributed systems • Enhancing and maintaining high-quality datasets and annotations to fuel multimodal learning • Investigating and addressing production incidents through root cause analysis and sustainable fixes • Collaborating closely with cross-functional stakeholders across Canva to build aligned, technically feasible, and high-impact solutions