Senior Software Engineer - AI​/ML

Xcelerium is seeking a highly skilled and passionate Senior Software Engineer - AI/ML to join our software engineering team. In this role, you will design, develop, and fine-tune cutting-edge AI/ML frameworks that solve complex challenges efficiently. Your expertise will play a pivotal role in steering the direction of our initiatives and driving significant technological innovation.

• Architect, implement, and validate our efficient AI/ML software framework to support business and product goals.
• Collaborate closely with product managers, cross-functional teams, and stakeholders to translate requirements into reliable, scalable AI solutions.
• Run experiments, evaluate model performance, and refine solutions through iterative, data-driven improvements.
• Stay up to date with emerging research, tools, and methodologies in AI, machine learning, deep learning, and compiler technologies.
• Mentor junior engineers, fostering a culture of technical excellence, learning, and collaboration.
• Prepare clear technical documentation and present project insights to internal teams and leadership.

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related discipline.
• 4+ years of professional experience in software engineering with a strong focus on AI/ML development.
• Strong foundation in machine learning concepts, deep learning architectures, NLP, and computer vision.
• Excellent analytical thinking, problem-solving skills, and a collaborative, team-oriented mindset.
• Strong communication skills with the ability to articulate technical concepts effectively.

• Proficiency in Python and hands-on experience with frameworks such as Tensor Flow, Keras, PyTorch, and JAX.
• Experience in C/C++.
• Experience with AI/ML compiler technologies such as MLIR, IREE, Tensor
  
  RT, XLA, or similar optimization frameworks.
• Familiarity with computer architecture and/or hardware accelerators.
• Familiarity with deploying machine learning solutions on custom accelerators.
• Design and implement MLIR-based compiler passes for lowering, optimization, and code generation
• Build domain-specific dialects to represent compute kernels at multiple abstraction levels.
• Develop performance-tuned transformation pipelines targeting vectorization, parallelization, and memory locality.