Research Engineer / Senior Research Engineer

Key Responsibilities

• Applied Algorithm Research & Innovation: Investigate and develop novel algorithms for large-scale combinatorial and continuous optimization problems within multi-constraint design flows. Design, prototype, and evaluate machine learning models to enhance prediction, decision-making, and automation in computationally intensive engineering workflows.
• Systems Prototyping: Build robust research prototypes and frameworks to validate novel approaches, ensuring they are scalable and address real-world constraints.
• Cross-Functional Translation: Collaborate closely with product engineering teams to mature promising research concepts into production-ready features.
• Technical Thought Leadership: Maintain deep expertise in state-of-the-art research across algorithm design, AI/ML, and high-performance computing. Contribute to the team's strategic research direction.
• Knowledge Dissemination: Document and communicate findings through internal reports and, where appropriate, external publications (DAC, ICCAD, ASPDAC etc.) or patents.

Required Qualifications

• Education: M.S. or Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or a closely related quantitative field.
• Experience: For M.S.: 3+ years of hands-on research or advanced development experience. For Ph.D.: A strong thesis or publication record demonstrating innovation.
• Technical Skills: Profound understanding of algorithm design, analysis, and implementation for complex systems. Expertise in Python and C/C++ for research prototyping and performance-critical development. Excellent grasp of software engineering principles, data structures, and computational complexity.
• Mindset: Innate curiosity, strong problem-solving intuition, and a drive to see ideas materialize in impactful products.

Preferred Qualifications

• Strong understanding of electronics design concepts, e.g. digital logic synthesis and/or physical design .
• Experience with industrial-grade software tools in domains involving complex optimization (e.g., CAD, logistics, finance, computational biology).
• Experience with modern ML frameworks (PyTorch, TensorFlow, JAX) and techniques relevant to structured data and optimization.
• Specific expertise in applying Graph Neural Networks (GNNs), Reinforcement Learning (RL), or advanced search algorithms to domain-specific problems.
• Familiarity with high-performance and parallel computing concepts.
• A record of publications in top-tier conferences/journals in Design Automation, AI, algorithms, or related computational fields.