Role Description We are hiring a Software Development Engineer in Test to work on a product focused on cloud-based error reporting and analytics systems. The role involves contributing to test automation, application quality, and scalable cloud services within a collaborative engineering environment. This is a remote role; however, the candidate must be present within the Canadian boundary. Key Responsibilities - Redesign and enforce a scalable test strategy aligned with a healthy test pyramid. - Build robust API and integration automation frameworks. - Implement contract testing across microservices. - Validate serverless workflows, including Lambda, event-driven flows, IAM, retries, and DLQs. - Integrate quality gates into CI/CD pipelines. - Reduce regression time and flaky test rates. - Suggest improvements to enhance system reliability, scalability, and performance. - Mentor engineers and drive shared ownership of quality. Qualifications - Hands-on experience in application testing and quality assurance. - Strong experience in test automation development. - Strong programming skills in TypeScript, JavaScript, Node.js, or similar. - Proven experience building automation frameworks. - Experience with Cypress or equivalent for frontend automation. - Hands-on AWS experience including Lambda, API Gateway, SQS, SNS, and IAM. - CI/CD pipeline integration experience. - Strong problem-solving and analytical skills. Nice-to-Have Skills - Exposure to microservices and container-based architectures. - Familiarity with the Microsoft .NET platform. - Aware of modern cloud application testing practices. Company Description NearSource is a global IT staffing and technology services firm helping enterprises build high-performance tech teams. We support innovation at scale by connecting skilled professionals to leading companies and impactful projects. We prioritize transparency, responsiveness, and the right fit. Equal Opportunity Statement NearSource welcomes applicants of all backgrounds, communities, and experiences. Our commitment to diversity, equity, and inclusion drives us to create hiring experiences that are open, fair, and human-first. Interested candidates can apply, and the recruitment team will review profiles based on alignment with client requirements.

• Master Socket workflows, tools, and patching processes • Lead patching efforts for high-impact vulnerabilities across npm packages • Scale patch production to dozens or hundreds of patches per week • Help select and prioritize high-value patches • Provide technical input on patch prioritization based on ecosystem and customer impact • Build and improve automated patching infrastructure and tooling • Design and implement scalable patch generation and delivery systems • Develop automated vulnerability detection and patch creation workflows • Build APIs and integrations to deliver certified packages • Create tooling for patch quality assurance and testing • Work with security researchers to understand and patch critical vulnerabilities • Help shape the technical roadmap for expansion • Give developers quick, safe remediation options for widely-used packages • Help secure the software supply chain for millions of developers

• Lead and participate in novel research to build a massive scale synthetic data generation pipeline and orchestration solution • Optimize the performance, cost, and resource utilization of AI inference workloads by leveraging the most recent advances for compute & memory optimization techniques. • Contribute to the development of our open-source libraries and frameworks for synthetic data generation and distributed RL frameworks. • Publish research in top-tier AI conferences such as ICML & NeurIPS. • Distill highly technical project outcomes in layman approachable technical blogs to our customers and developers. • Stay up-to-date with the latest advancements in AI/ML infrastructure and tools, synthetic data gen research and proactively identify opportunities to enhance our platform's capabilities and user experience.

• Build and optimize the systems infrastructure behind large-scale RL and distributed training workloads. • Improve end-to-end training efficiency across compute, memory, networking, and scheduling layers. • Design and implement low-level performance optimizations, including kernels, communication paths, and runtime improvements. • Work on distributed training systems spanning data, tensor, and pipeline parallel workloads. • Help shape the architecture of our RL training stack, including async rollout and post-training systems. • Contribute to open-source libraries and internal infrastructure used for frontier-scale model training. • Collaborate closely with researchers and infrastructure engineers to translate bottlenecks into concrete systems improvements. • Stay at the frontier of training systems, inference systems, compiler/runtime tooling, and hardware-aware optimization techniques.