{"id":2916175,"url":"https://alion.io/job/buildxpartners-gpu-compute-mlir-compiler-engineer","title":"GPU Compute & MLIR Compiler Engineer","company":{"id":3800477,"name":"Buildxpartners","domain":"buildxpartners.com","url":"https://alion.io/company/buildxpartners","size_band":"201-500","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Zoho Recruit","truth_index":{"grade":"D","score":54,"open_postings":53,"ghost_share":0.774,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-22T06:10:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19500,"max_usd":53000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":31},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Computer Vision","optional":false},{"name":"LLM","optional":false},{"name":"LLVM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLIR","optional":false},{"name":"ONNX","optional":false},{"name":"OpenCL","optional":false},{"name":"Apache TVM","optional":true},{"name":"IREE","optional":true},{"name":"Quantization","optional":true},{"name":"XLA","optional":true}],"status":"live","first_seen_at":"2026-06-18T00:00:00Z","employer_posted_date":"2026-06-18","last_verified_at":"2026-10-22T12:28:36Z","board_verified":true,"closed_at":null,"days_open":126,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":126},"description":"About Our Client\nOur client is a global technology leader developing next-generation semiconductor and AI computing platforms. Their engineering teams build high-performance hardware and software solutions powering mobile, automotive, AI, and edge computing products used worldwide.\nRole Summary\nWe are looking for a highly skilled GPU Compute, MLIR Compiler, and Kernel Optimization Engineer with deep expertise in GPU compute, MLIR-based code generation, and end-to-end performance optimization for AI workloads. In this role, you will design, optimize, and deploy high-performance GPU compute kernels, build and extend MLIR compiler backends, and collaborate closely with ML, runtime, and hardware teams to push the limits of performance on modern GPU architectures.\nKey Responsibilities\nDevelop and optimize GPU compute kernels targeting OpenCL and Vulkan compute backends for high-throughput AI/ML workloads.\n\nDesign, build, and extend MLIR dialects across multiple abstraction levels-including frontend dialects, graph-level IR, tensor IR (e.g., Linalg, Tensor, TOSA), and runtime/low-level dialects-to enable efficient end-to-end model compilation.\n\nImplement and maintain MLIR-based compiler passes and transformations, including tiling, fusion, bufferization, vectorization, and lowering pipelines targeting OpenCL and Vulkan GPU backends.\n\nConduct profiling and bottleneck analysis of compiled kernels using GPU counters and vendor-specific profilers, and drive performance improvements through compiler-level optimizations.\n\nBuild and maintain GPU runtime infrastructure for both OpenCL and Vulkan, including memory management, pipeline setup, command buffer orchestration, and resource scheduling.\n\nDevelop and extend code generation pipelines, enabling automatic lowering from tensor IR through MLIR to efficient OpenCL and Vulkan GPU kernels.\n\nImplement performance-critical schedules-including tiling, loop fusion, parallelism, and caching strategies-within MLIR-based backends targeting OpenCL and Vulkan runtimes.\n\nCollaborate with framework teams to optimize end-to-end model lowering for computer vision and LLM workloads using MLIR compilation stacks.\n\nDesign and implement robust compiler and runtime components using modern C/C++, leveraging advanced programming paradigms for high-performance systems.\n\nRequired Qualifications\nStrong hands-on experience with the MLIR framework, including authoring and extending custom dialects, writing compiler passes, and building end-to-end lowering pipelines.\n\nDeep expertise across MLIR abstraction levels:\nFrontend dialects - ingestion and representation of ML models (e.g., TOSA, StableHLO, ONNX-MLIR)\n\nGraph-level IR - high-level operation fusion, shape inference, and graph transformations\n\nTensor IR level - structured operation representation using Linalg, Tensor, and Vector dialects; tiling and fusion strategies\n\nRuntime/low-level dialects - Bufferization, MemRef, SCF, GPU, and LLVM dialects for final code generation\n\nStrong hands-on experience in OpenCL programming, including kernel development, memory model, work-group/work-item optimization, and OpenCL runtime management.\n\nSolid understanding of Vulkan compute programming, including descriptor management, compute pipelines, synchronization primitives, and Vulkan runtime internals.\n\nStrong understanding of GPU architecture, memory hierarchies, and asynchronous compute.\n\nProficiency in C/C++ for system-level development.\n\nExperience with kernel profiling and bottleneck analysis on GPU platforms.\n\nStrong background in machine learning fundamentals, covering both Computer Vision (CV) and Large Language Model (LLM) workloads.\n\nGood to Have\nHands-on experience with IREE (Intermediate Representation Execution Environment) or other MLIR-based deployment frameworks such as TVM, XLA, or LLVM.\n\nFamiliarity with IREE compiler and runtime architecture, including HAL (Hardware Abstraction Layer), executable compilation, and dispatch mechanisms, particularly OpenCL and Vulkan HAL backends.\n\nExperience contributing to open-source MLIR or IREE projects.\n\nKnowledge of quantization, mixed-precision inference, and model optimization techniques for edge and server GPU targets.\n\nExposure to multi-target compilation (CPU, GPU, NPU) using MLIR-based toolchains.\n\nFamiliarity with cross-vendor GPU profiling tools such as ARM Streamline, Snapdragon Profiler, and Intel VTune for OpenCL and Vulkan workloads.\n\nMinimum Qualifications\nBachelor's degree in Engineering, Information Systems, Computer Science, or a related field with 4+ years of Systems Engineering or relevant industry experience.\n\nOR\nMaster's degree in Engineering, Information Systems, Computer Science, or a related field with 3+ years of Systems Engineering or relevant industry experience.\n\nOR\nPhD in Engineering, Information Systems, Computer Science, or a related field with 2+ years of Systems Engineering or relevant industry 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