AI & ML Engineer - C++ / Java Developer

Role

Vacancies

Education:

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.
  

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.
• Deep understanding of computer architecture, operating systems, data structures, and memory management.
  

What We Offer

• A dynamic and innovative work environment with a AI research-driven team.
• Mentorship, learning, and growth opportunities in AI infrastructure and systems research.
• Competitive compensation with performance-based advancement and technical ownership.
• Opportunity to work on compiler-level AI framework design and large-scale foundation model optimization.
• Access to multi-node GPU clusters and next-gen compute environments for experimentation.
  

Important: