Machine Learning Engineer
Role details
Job location
Tech stack
Job description
What you'll do Design and develop new methods for compressing and optimizing Large Language Models (LLMs) using advanced AI and quantum-inspired approaches. Conduct evaluations, benchmarks, and fine-tuning of models to maximize performance, accuracy, and efficiency. Build applications powered by LLMs, including retrieval-augmented generation (RAG) systems and AI agents. Identify opportunities for innovation by assessing model strengths/weaknesses and proposing enhancements. Develop and train custom deep learning models for a variety of use cases, including beyond LLMs (e.g., computer vision). Maintain thorough documentation of development processes, experiments, and results. Share knowledge with team members, mentor juniors, and contribute to a culture of collaboration and technical excellence. Stay current with the latest research in LLMs and recommend emerging tools and technologies.
Requirements
What you'll bring Degree (BSc, MSc, or PhD) in Artificial Intelligence, Computer Science, Data Science, or related fields. 2+ years of hands-on experience designing, training, or fine-tuning deep learning models, preferably with transformer or computer vision architectures. Proficiency with transformer models and related libraries (e.g., HuggingFace Transformers, Accelerate, Datasets). Strong mathematical foundations in deep learning algorithms and neural networks. Excellent problem-solving, debugging, and performance analysis skills. Strong Python programming skills and experience with relevant ML frameworks (PyTorch, HuggingFace, etc.). Experience with cloud platforms (ideally AWS), containerization (Docker), and deploying AI solutions in cloud environments. Excellent communication skills and ability to thrive in a fast-paced, collaborative setting. Nice to have Experience running large-scale workloads in HPC clusters. Familiarity with inference/deployment tools (TensorRT, vLLM, etc.). Experience building/evaluating RAG systems. Track record in computer vision, audio, or signal processing applications. Knowledge of responsible AI practices and AI ethics. Experience in DevOps/MLOps for AI product development. Previous research publications in deep learning or related fields.
