Collinear AI, a well-funded and VC-backed stealth startup based in the Bay Area, is dedicated to advancing AI Alignment and Customization. We are seeking a dynamic ML Engineer (LLM) to join our innovative team, comprised of experts from renowned institutions such as Stanford, Hugging Face, and Salesforce.
About Collinear AI
At Collinear AI, we are committed to empowering enterprises to harness the power of AI by tailoring open-source LLMs to authentically reflect their unique values and offerings. Through this customization, we aim to transcend existing limitations and redefine the boundaries of AI capabilities.
What You'll Do:
• Understand: Evaluate customer challenges and requirements in implementing AI Chatbots, identifying areas for improvement and optimization.
• Optimize: Utilize your expertise in Large Language Models (LLMs) and Reinforcement Learning (RLHF) to enhance our SaaS product, aligning it with the customer's industry vertical and specific needs.
• Develop and Deploy: Design and implement customized solutions for customers, ensuring seamless deployment on their servers.
• Support: Collaborate with internal teams and customers, providing ongoing support to ensure the delivery of high-quality products and continuous improvement.
Who You Are:
• AI Virtuoso: With over 3 years of experience in machine learning engineering, you are a leader in the field, shaping the AI revolution from concept to execution.
• Innovative Entrepreneur: Thriving in dynamic startup environments, you excel in cutting-edge engineering practices, bringing agility and precision to high-stakes projects.
• Code Artisan: Your expertise extends beyond coding; you craft elegant and robust machine learning solutions tailored for real-world applications. Proficient in PyTorch, Transformers, Scikit-learn, NumPy, Pandas.
• Collaborative Leader: Approachable and meticulous, you elevate your team with leadership and expertise, fostering a collaborative and productive environment.
• Deployment Wizard: Your expertise in deploying large language models is unmatched, combining deep knowledge with practical application.
• Continuous Learner: Eager to expand your knowledge and apply new methods in machine learning, from data processing to low-level optimization.
• Research Background (Good to Have): Your research contributions are groundbreaking, with publications in top conferences such as ACL, EMNLP, NeurIPS, ICLR, ICML, exploring areas like instruction tuning, reinforcement learning, and multimodal applications.
Roles & Responsibilities
• Research and Analysis: Conduct research and analysis to understand customer requirements and challenges in implementing Large Language Models (LLMs) for various applications.
• Model Development: Develop and implement machine learning models, including LLMs, tailored to meet customer needs and industry-specific use cases.
• Optimization and Fine-Tuning: Optimize and fine-tune machine learning models for improved performance, accuracy, and efficiency, leveraging techniques such as hyperparameter tuning, transfer learning, and reinforcement learning.
• Deployment and Integration: Deploy machine learning models into production environments, ensuring seamless integration with existing systems and infrastructure.
• Testing and Validation: Conduct rigorous testing and validation of machine learning models to ensure reliability, scalability, and robustness in real-world scenarios.
• Performance Monitoring: Monitor and analyze the performance of deployed models, identifying opportunities for optimization and improvement.
• Collaboration and Communication: Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to gather requirements, iterate on solutions, and communicate progress and findings effectively.
• Documentation and Reporting: Document model development processes, methodologies, and findings, and prepare comprehensive reports and presentations for internal stakeholders and customers.
• Continuous Learning and Innovation: Stay updated on the latest advancements in machine learning and natural language processing (NLP), and explore innovative approaches and techniques to enhance model performance and capabilities.
Education, Skills, and Certifications Required:
Education: Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field. Advanced degrees or additional certifications in machine learning or artificial intelligence are preferred.
Skills:
• Proficiency in machine learning techniques and algorithms, with a focus on natural language processing (NLP) and large language models (LLMs).
• Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Transformers, scikit-learn, NLTK, and spaCy.
• Strong programming skills in languages such as Python, Java, or C++.
• Knowledge of deep learning architectures, including recurrent neural networks (RNNs), convolutional neural n
Location: California (+23 others)
Posted: Oct. 17, 2024, 9:25 p.m.
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