ML/LLM Engineering Manager

Collinear AI is a well-funded, VC-backed stealth startup based in the Bay Area, specializing in AI Alignment. With a team boasting expertise from Stanford, Hugging Face, and Salesforce, we are dedicated to customizing open-source LLMs to reflect enterprise-specific values and offerings, moving beyond generic ChatGPT-style responses to unlock the full potential of AI for businesses.

Roles & Responsibilities:

Leadership and Team Management:

• Lead and mentor a team of machine learning engineers, fostering a collaborative and productive environment.
• Oversee project timelines, ensuring efficient allocation of resources and adherence to deadlines.
• Drive strategic initiatives for AI development and deployment within the organization.

Research and Analysis:

• Conduct comprehensive research and analysis to understand customer requirements and challenges in implementing Large Language Models (LLMs) for various applications.
• Stay updated on the latest advancements in machine learning and natural language processing (NLP), exploring innovative approaches and techniques to enhance model performance and capabilities.

Model Development:

• Develop and implement advanced machine learning models, including LLMs, tailored to meet customer needs and industry-specific use cases.
• Utilize 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.

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.
• Conduct rigorous testing and validation of machine learning models to ensure reliability, scalability, and robustness in real-world scenarios.

Deployment and Integration:

• Design and implement customized solutions for customers, ensuring seamless deployment on their servers.
• Deploy machine learning models into production environments, ensuring seamless integration with existing systems and infrastructure.

Performance Monitoring:

• Monitor and analyze the performance of deployed models, identifying opportunities for optimization and improvement.
• Provide ongoing support and continuous improvement to ensure the delivery of high-quality products.

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.
• Present complex technical concepts and project updates to internal stakeholders and customers in a clear and concise manner.

Documentation and Reporting:

• Document model development processes, methodologies, and findings, preparing comprehensive reports and presentations for internal stakeholders and customers.
• Maintain thorough records of project progress, decisions made, and lessons learned.

Continuous Learning and Innovation:

• Eagerly expand knowledge and apply new methods in machine learning, from data processing to low-level optimization.
• Contribute to open-source projects and maintain a strong presence in the AI/ML community.

Who You Are ?

AI Virtuoso:

• With over 10+ 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.

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 networks (CNNs), and transformer models.
• Familiarity with data preprocessing, feature engineering, and model evaluation techniques.
• Experience with version control systems (e.g., Git) and software development best practices.
• Excellent problem-solving and analytical skills, with a keen attention to detail.
• Strong communication and collaboration skills, with the ability to work effectively in cross-functional teams.

Certifications:

• Certifications in machine learning, deep learning, or natural language processing from recognized institutions or platforms such as Coursera, Udacity, or edX are beneficial.

Preference Criteria:

• Open Source Contributions: Candidates who have contributed to open-source projects related to machine learning, natural language processing, or large language models will be given preference.
• GitHub Portfolio/Testimonial Models: Candidates with a strong portfolio of machine learning projects or testimonial models showcased on GitHub will be highly regarded.
• Recognition from Prestigious Institutions: Candidates who have been rewarded or recognized for their contributions to machine learning or AI by prestigious institutions or organizations will be favored.
• Strong Online Presence: Candidates with a strong online presence, such as a well-maintained professional website, active participation in relevant forums or communities, or a substantial following on platforms like LinkedIn or Twitter, will be given preference.
• Experience: Candidates with experience working in AI/ML-based startups, studying/qualifying from top-tier institutes or universities, or working for top-tier companies in the field of AI/ML or related industries will be highly valued.

Perks & Benefits: Your Launchpad to Success

Rewarding Compensation : Not only do we offer a competitive salary, but we also recognise your invaluable contribution with substantial early-stage equity. You're not just an employee; you're a key player in our journey.

Adaptive Workspace : Our hub is in Mountain View, and we are primarily in-person with few days remote each week. However, we do make rare exceptions for candidates not located in the Bay Area.

Health is Paramount ????: We prioritise your wellbeing with top-tier medical, dental, and vision insurance. Your health is our wealth.

Trailblazing Role : Join us and not only be a part of our well-funded, high-potential startup but also shape an industry where few have left their mark. This isn't just a job; it's a legacy.

Join the AI Elite : Are you ready to stand among the best in AI? Embark on this journey with us, grow, and redefine the future.