Machine Learning Research Engineer | Kilby Labs

Change the world. Love your job.

We are seeking a highly motivated Machine Learning Research Engineer to join our Embedded AI team to work on cutting-edge Large Language Model (LLM) research and development for Edge AI applications. As a key member of our team, you will lead the efforts on advancing the state-of-the-art in LLM architectures, Agentic LLM, and Reasoning LLM. Your work will involve exploring innovative approaches to integrate LLMs with domain knowledge, related tools, and other AI techniques to achieve human-like decision-making capabilities for business impact.

In this machine learning research engineer role, you’ll have the chance to work on the following topics: 

• Explore the application of LLMs in code generation, evaluation, and optimization
• Conduct research and development on novel and efficient LLM architectures, training algorithms, and post-training algorithms
• Design and develop new reasoning models with domain-specific knowledge
• Design and implement advanced Agentic LLM system for complex task automations
• Collaborate with system teams and internal business teams to define and implement AI/ML solutions for core business

Minimum requirements:

• Doctoral degree in Computer Science, Electrical Engineering, Electrical and Computer Engineering, or related field
• 3 years of related experience

Preferred qualifications:

• Strong background in Natural Language Processing, Large Language Models, and Deep Learning frameworks
• Proven track record of designing, developing, and deploying machine learning models/LLMs that drive business value
• Proficiency in Python, C/C++, and software design, including debugging, performance analysis, and optimization
• Excellent understanding of LLM architectures and transformer-based models
• Experience with popular deep learning frameworks (e.g., PyTorch, JAX, ONNX) and LLM-specific libraries (e.g., transformers, trl, vllm)
• Strong foundation in text processing, tokenization, and embedding techniques
• Knowledge of few-shot learning, transfer learning, and fine-tuning
• Knowledge of LLM performance evaluation
• Knowledge of reinforcement learning for LLM; Experience with LLM post-training implementation, including PPO, DPO, and GRPO
• Experience with LLM agent implementation with tool calling
• Excellent communication and interpersonal skills, with the ability to work in a dynamic and distributed team