Automation Test Engineer jobs in Canada

• highly available

• deployment

• Identify project technical risks and make recommendations to mitigate

• Use deep technical proven experience of company systems and applications, which could cover applications, services, systems, or frameworks

• a high level of initiative and attention to detail during daily operations

• Collaborate effectively with other engineers, product managers, designers, and QA engineers

• Encourage peers with high-quality, hands-on technical contributions

• methodology , design, and best practices.

• Consistently produce high-quality software with a focus on unit testing, regular code reviews, and continuous integration.

• high standards in quality and operational excellence.

• Develop comprehensive technical documentation and presentations to clearly communicate architectural decisions and design options. Ensure documentation aligns with project scopes, milestones, and deliverables.

• Effectively delegate tasks and responsibilities within the team, considering individual skills and workload

• Lead the development of prototypes and proof-of-concept implementations for new technologies or approaches.

Required Experience

The language of work is English.

• Bachelor of Engineering degree plus 8 or more years of experience ; or MS in Computer Science plus 6 or more years of hands-on experience in developing highly scalable distributed platforms and services and internet scale web application

• Highly experienced in application development in JAVA and Kotlin and its related frameworks such as like Spring, Spring Boot, Hibernate, Stream processing platforms such as Kafka and Flink ;

• in Oracle ADF 12c Framework, JavaScript , HTML and

• in J2EE, SOAP, SOA Services, Design Patterns, OOA/D, Data Structures, XML, REST, JSON, and Internet Protocols ;

• in a programming language such as Scala, solid base in data structures, algorithms and a strong understanding of multithreading, synchronization, concurrent programming; deep architectural understanding of system design and lead ing

• noSQL data technology such as Mongo , ElasticSearch and related toolset ;

• in Spring boot and Hadoop framework ;

• in retail and logistics ;

• ility to troubleshoot performance bottlenecks in

• participating in design and code reviews, coding and unit testing of fault-tolerant applications. Comfortable or have familiarity with all the layers of multi-tier applications to craft complete solutions and maintain products

• Solid understanding of computer science fundamentals. Experience in non-functional skills like Security, Load and Performance Tests, Accessibility, Site Speed optimization, Cross-browser /Cross-platform UX Design

• Excellent verbal and written communication, leadership, and collaboration skills

What you’ll be doing:

• Contribute features to vLLM that empower the newest models with the latest NVIDIA GPU hardware features; profile and optimize the inference framework (vLLM) with methods like speculative decoding,data/tensor/expert/pipeline-parallelism,prefill-decode disaggregation.

• Develop, optimize, and benchmark GPU kernels (hand-tuned and compiler-generated) using techniques such as fusion, autotuning, and memory/layout optimization; build and extend high-level DSLs and compiler infrastructure to boost kernel developer productivity while approaching peak hardware utilization.

• Define and build inference benchmarking methodologies and tools; contribute both new benchmark and NVIDIA’s submissions to the industry-leading MLPerf Inference benchmarking suite.

• Architect the scheduling and orchestration of containerized large-scale inference deployments on GPU clusters across clouds.

• Conduct and publish original research that pushes the pareto frontier for the field of ML Systems; survey recent publications and find a way to integrate research ideas and prototypes into NVIDIA’s software products.

What we need to see:

• Bachelor’s degree (or equivalent expeience) in Computer Science (CS), Computer Engineering (CE) or Software Engineering (SE) with 7+ years of experience; alternatively, Master’s degree in CS/CE/SE with 5+ years of experience; or PhD degree with the thesis and top-tier publications in ML Systems, GPU architecture, or high-performance computing.

• Strong programming skills in Python and C/C++; experience with Go or Rust is a plus; solid CS fundamentals: algorithms & data structures, operating systems, computer architecture, parallel programming, distributed systems, deep learning theories.

• Knowledgeable and passionate about performance engineering in ML frameworks (e.g., PyTorch) and inference engines (e.g., vLLM and SGLang).

• Familiarity with GPU programming and performance: CUDA, memory hierarchy, streams, NCCL; proficiency with profiling/debug tools (e.g., Nsight Systems/Compute).

• Experience with containers and orchestration (Docker, Kubernetes, Slurm); familiarity with Linux namespaces and cgroups.

• Excellent debugging, problem-solving, and communication skills; ability to excel in a fast-paced, multi-functional setting.

Ways to stand out from the crowd

• Experience building and optimizing LLM inference engines (e.g., vLLM, SGLang).

• Hands-on work with ML compilers and DSLs (e.g., Triton,TorchDynamo/Inductor,MLIR/LLVM, XLA), GPU libraries (e.g., CUTLASS) and features (e.g., CUDA Graph, Tensor Cores).

• Experience contributing tocontainerization/virtualizationtechnologies such ascontainerd/CRI-O/CRIU.

• Experience with cloud platforms (AWS/GCP/Azure), infrastructure as code, CI/CD, and production observability.

• Contributions to open-source projects and/or publications; please include links to GitHub pull requests, published papers and artifacts.