{"id":769065,"url":"https://alion.io/job/enchargeai-ai-compiler-engineer-2","title":"AI Compiler Engineer","company":{"id":673158,"name":"EnCharge AI","domain":"enchargeai.com","url":"https://alion.io/company/enchargeai","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"C","score":64,"open_postings":25,"ghost_share":0.6,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-10T05:45:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":33000,"max_usd":78000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":26},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Edge AI","optional":false},{"name":"MLIR","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"Quantization","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"TPU","optional":false}],"status":"live","first_seen_at":"2025-07-10T21:09:06Z","employer_posted_date":"2026-07-30","last_verified_at":"2026-10-11T03:38:28Z","board_verified":true,"closed_at":null,"days_open":457,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":456},"description":"EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge’s robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today’s best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems.\nAbout the Role\nEnCharge AI is seeking a highly skilled and experienced AI Compiler Engineer to spearhead the efforts in developing and optimizing graph compilers tailored to cutting-edge AI and ML workloads. You will collaborate with hardware architects, and AI researchers to enhance performance, optimize computation graphs, and enable efficient model deployment on EnCharge’s Inference Accelerators. \nResponsibilities\nArchitect, design, and implement optimizations for AI model execution on graph compilers to improve performance, reduce latency, and maximize hardware utilization.\nWork closely with ML researchers, hardware engineers, and software developers to design and deploy AI models, understanding and addressing hardware-specific challenges.\nWork on performance optimizations for neural network models, such as layer fusion, operator fusion, and graph-level transformations.\nDevelop compiler optimizations and passes that convert high-level AI models (e.g., from TensorFlow, PyTorch) into intermediate representations (IR).\nImplement parsing, semantic analysis, and IR generation for deep learning frameworks.\nResearch and integrate the latest advancements in compiler design, ML model optimizations, and hardware acceleration into graph compilers.\nProvide leadership, mentorship, and technical guidance to a team of engineers focused on graph compiler optimizations. \nQualifications\nBachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field (Ph.D. preferred).\n3+ years in compiler development, with a strong focus on AI or ML graph compilers.\nProficiency in AI graph compiler frameworks (e.g., MLIR, Torch-FX)\nSolid background in hardware architectures (e.g., GPUs, TPUs, ASICs) and optimization techniques such as fusion, quantization, and tiling.\nFamiliarity with neural networks operators and code generation.\nStrong understanding of intermediate representations, code parsing, and semantic analysis in compiler design.\nProficiency in C++, Python, or other programming languages commonly used in compiler development.\nOpen-source contributions to AI software frameworks and libraries is a plus\nDemonstrated experience leading and 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