Overview

Role: AI & ML Engineer (Freshers)

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.

Qualifications

• 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.
• 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.

Benefits

• 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.
• 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