{"id":3459693,"url":"https://alion.io/job/multicoreware-gpu-engineer","title":"GPU Engineer","company":{"id":1764592,"name":"MulticoreWare","domain":"multicorewareinc.com","url":"https://alion.io/company/multicorewareinc","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Zoho Recruit","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chennai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":10000,"max_usd":26000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":18},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"CUDA","optional":false},{"name":"Linux","optional":false},{"name":"OpenCL","optional":false},{"name":"PyTorch","optional":false},{"name":"ROCm","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"TensorRT","optional":false},{"name":"HPC","optional":true},{"name":"Quantization","optional":true}],"status":"live","first_seen_at":"2026-08-30T00:00:00Z","employer_posted_date":"2026-08-30","last_verified_at":"2026-11-04T01:34:59Z","board_verified":true,"closed_at":null,"days_open":66,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":66},"description":"We are looking\nfor an experienced GPU Programming Engineer to develop, optimize, and deploy\nGPU-accelerated solutions for high-performance deep learning workloads. The\nrole focuses on GPU programming, parallel computing, performance tuning, and\ndeep learning system integration across platforms such as NVIDIA CUDA, AMD\nROCm/HIP, and OpenCL.\nResponsibilities:\nDevelop, optimize, and maintain GPU-accelerated\ncomponents for deep learning pipelines using CUDA, HIP, or OpenCL.\nAnalyze and improve GPU kernel performance\nthrough profiling, benchmarking, and resource optimization.\nOptimize memory access, compute, throughput, and\nkernel execution to improve overall system performance on target GPUs.\nPort existing CPU-based implementations to GPU\nplatforms while ensuring correctness and performance scalability.\nWork closely with system architects, software\nengineers, and domain experts to integrate GPU-accelerated solutions.\nRequirements\nEducation:\nBachelor's or master's degree in Computer Science, Electrical Engineering, or a\nrelated field.\nTechnical Skills (Must haves):\n2+ years of hands-on experience in GPU\nprogramming, preferably using CUDA, HIP, OpenCL, or similar GPU APIs.\nStrong understanding of GPU architecture, memory\nhierarchy, shared memory, bank conflicts, and parallel programming models.\nProficiency in C/C++ and hands-on experience\ndeveloping on Linux-based systems.\nFamiliarity with profiling and tuning tools such\nas Nsight, rocprof, or Perfetto.\nNeed to have (Can be bridged):\nKnowledge and experience in SIMD programming.\nGood understanding of neural network (NN)\noperators and hands-on experience with PyTorch, TensorFlow, or TensorRT.\nGood to have (Not essential):\nExposure to deep learning concepts such as\nquantization and pruning.\nExperience working with High Performance\nComputing (HPC) systems.\nPreferred Qualifications (Optional):\nAdditional\nexperience in GPU performance engineering, deep learning system optimization,\nor heterogeneous computing would be 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