Senior Machine Learning Engineer, ATLAS AI

Responsibilities

• Collaborate with researchers and engineers to design, develop, and refine large language models and generative models for various applications.
• Utilize your expertise in machine learning and natural language processing to develop novel algorithms and methodologies for generative modeling tasks.
• Implement, train, and fine-tune LLM and GPT-like models on large-scale datasets to ensure optimal performance and accuracy.
• Stay up to date with the latest research advancements and techniques in the field of language modeling, generative modeling, and machine learning.
• Deliver the next generation of innovation in trustworthy Industrial Data Insights & Collaboration

What We Are Looking For

• Extensive software engineering experience in building high performance, large-scale ML systems (Preferably 3+ years of industry experience in the relevant fields).
• Strong communication skills and the ability to work effectively in a collaborative research environment.
• Demonstrated leadership abilities, with experience in guiding and mentoring a team of ML engineers.
• Excellent problem-solving and troubleshooting abilities, along with a strong analytical mindset and persistence in resolving problems.
• Strong theoretical and practical background in NLP including experience with state-of-the-art LLM architectures optimized for handling large context and low inference cost.
• Advanced degree, ideally a Ph.D., in Computer Science, Electrical Engineering, or a related field with specialized research in areas such as machine learning, NLP, Large Language Models (LLMs), multi-modal foundation models, and generative AI techniques.
• Proficiency in deep learning frameworks (e.g., PyTorch, TensorFlow, etc.) and libraries commonly used in NLP and Generative AI.
• Solid programming skills in Python and the ability to write clean, efficient, and well-documented code.
• 3 years of experience working in a complex, matrixed organization involving cross-functional or cross-business projects.
• Experience with distributed parallel training, large-scale multi-modal foundation and generative models strongly preferred.
• Collaborate on modeling techniques to support the full spectrum of LLM tuning including prompt engineering, instruction tuning, Reinforcement Learning from Human Feedback (RLHF), parameter-efficient tuning, and fine-tuning strongly preferred.
• Familiarity with training multi-modal foundation models and relevant frameworks strongly preferred.
• Familiarity with cloud-based infrastructure and experience deploying large-scale machine learning models in production environments.
• Experience with RAG, dual encoders, or related fields strongly preferred.
• Experience with JAX.
• A track record of publications and contributions to the machine learning and natural language processing communities strongly preferred.