Ai Ml Engineer (Freshers) (Chennai)

Role: AI & ML Engineer (Freshers) Compensation: 6 - 10 LPA + ESOPs Eligibility Open Roles: 12 Eligible Batches: 2026 / 2025 Location: Chennai (Mandaveli) On-site Key Responsibilities: - Design and develop compiler frameworks that optimize AI model execution at the kernel, graph, and operator levels. - Architect scalable transformer-based infrastructures for distributed multi-node training and efficient inference. - Build end-to-end AI pipelines including graph optimizations, memory scheduling, and compute distribution. - Collaborate with research teams to translate mathematical models into optimized execution graphs and intermediate representations (IRs). - Implement custom kernels, quantization strategies, and low-level performance optimizations in C/C++ and CUDA. - Analyze and tune runtime performance bottlenecks focusing on parallelization, vectorization, and memory management. - Develop domain-specific compiler passes for tensor operations, automatic differentiation, and operator fusion. - Conduct systematic experiments to explore scaling laws, precision formats, and architectural optimizations for improved computational efficiency. - Contribute to the development and optimization of deep learning frameworks and execution runtimes, focusing on tensor computation graphs, distributed training pipelines, and execution scheduling. - Work on data and training infrastructure including dataset preparation pipelines, tokenization strategies, preprocessing workflows, and evaluation systems for large-scale model training. - Participate in model efficiency research including quantization techniques, sparsity methods, precision formats, and inference optimization for large-scale neural networks. Required Skills & Qualifications Mandatory Requirements: - Strong proficiency in C, C++ or Java, with good command over pointers, memory management, performance optimization, and systems-level programming. - Solid foundation in Mathematics - calculus, probability, statistics, and linear algebra. - Strong logical reasoning, problem-solving ability, high intelligence (IQ), and an analytical mindset. - Ability to operate effectively in a research-driven, high-intensity environment with cross-functional collaboration. Added Advantage: - Experience or strong interest in compiler construction, runtime systems, and code generation. - Proficiency or willingness to learn CUDA and Rust for high-performance and systems-level development. - Understanding of computer architecture, operating systems, parallel computing, and memory hierarchy. - Familiarity with deep learning frameworks, transformer architectures, or distributed training systems. What We Offer - A research-driven engineering environment focused on building AI systems and advancing foundational machine learning capabilities. - Opportunity to contribute to the development of foundation models and the infrastructure required for large-scale training and inference. - Hands-on exposure to core layers of the AI stack including model architectures, data pipelines, deep learning frameworks, compilers, and runtime systems. - Access to high-performance compute infrastructure and multi-node GPU clusters for large-scale experimentation and systems research. - Mentorship and close collaboration with engineering and research teams working on the development of a GPT-scale dense foundation model. - An environment suited for individuals driven by curiosity, rigorous problem solving, and long-term research-oriented engineering work. Benefits:- - Market-competitive salary package - Employee Stock Ownership Plan (ESOP) eligibility upon confirmation - Health insurance coverage - Personal accident and life insurance coverage - Generous parental leave policy - Accommodation / transportation allowance - Complimentary lunch and dinner Role: AI & ML Engineer (Freshers) Compensation: 6 - 10 LPA + ESOPs Eligibility Open Roles: 12 Eligible Batches: 2026 / 2025 Location: Chennai (Mandaveli) On-site Key Responsibilities: - Design and develop compiler frameworks that optimize AI model execution at the kernel, graph, and operator levels. - Architect scalable transformer-based infrastructures for distributed multi-node training and efficient inference. - Build end-to-end AI pipelines including graph optimizations, memory scheduling, and compute distribution. - Collaborate with research teams to translate mathematical models into optimized execution graphs and intermediate representations (IRs). - Implement custom kernels, quantization strategies, and low-level performance optimizations in C/C++ and CUDA. - Analyze and tune runtime performance bottlenecks focusing on parallelization, vectorization, and memory management. - Develop domain-specific compiler passes for tensor operations, automatic differentiation, and operator fusion. - Conduct systematic experiments to explore scaling laws, precision formats, and architectural optimizations for improved computational efficiency. - Cont