Senior Machine Learning Engineer

Summary

Join our pioneering Core-LLM platform team, dedicated to pushing the boundaries of Generative AI. As a Senior Manager, you will lead a talented team of machine learning engineers, shaping the future of our AI capabilities and ensuring the ethical and effective deployment of our technology. You will generate and evaluate synthetic data to improve the robustness, performance, and safety of machine learning models. You will also train and fine-tune models, design and implement evaluation metrics, conduct experiments to validate model behavior, collaborate with engineering and research teams, participate in the development and deployment of AI solutions, and contribute to architectural and technology decisions. The role involves promoting modern engineering practices and working with LLMs, SLMs, Large Reasoning Models (LRMs), and SRMs. This position requires 5+ years of experience in machine learning and deep learning.

Requirements

• 5+ years of experience in machine learning, deep learning, and AI systems
• Proficiency in Python and frameworks like PyTorch, TensorFlow, and NumPy
• Experience in synthetic data generation, model training, and evaluation in real-world environments
• Solid understanding of LLM fine-tuning, prompting, and robustness techniques
• Knowledge of AI safety principles and experience identifying and mitigating model risks
• Hands-on experience deploying and optimizing models using platforms such as Triton Inference Server
• Familiarity with CI/CD, automated testing, and container orchestration tools like Docker and Kubernetes
• Experience with prompt engineering: ability to craft, test, and optimize prompts for task accuracy and efficiency

Responsibilities

• Generate and evaluate synthetic data tailored to improve the robustness, performance, and safety of machine learning models, particularly large language models (LLMs)
• Train and fine-tune models using curated datasets, optimizing for performance, reliability, and scalability
• Design and implement evaluation metrics to rigorously measure and monitor model quality, safety, and effectiveness
• Conduct experiments to validate model behavior and improve generalization across diverse use cases
• Collaborate with engineering and research teams to identify risks and recommend AI safety mitigation strategies
• Participate in the development, deployment, and continuous improvement of end-to-end AI solutions
• Contribute to architectural and technology decisions related to AI infrastructure, frameworks, and tooling
• Promote modern engineering practices including continuous integration, continuous delivery, and containerized workflows