Machine Learning Engineer

Position Overview

• Location Type: Remote
• Employment Type: Full-Time
• Salary: $110K - $250K
• Description: Join us in creating biological reasoning and building foundational models for systems biology, harnessing generative AI to revolutionize our understanding and programming of biology from molecules to organisms. As a Machine Learning Engineer, you'll work alongside our founders and team members to develop and implement cutting-edge AI systems capable of complex biological reasoning across multiple scales.

Requirements

• Education: Bachelor’s in Computer Science, Machine Learning, or a related technical field
• Experience: 3+ years of experience in developing and implementing machine learning models
• Programming Skills: Proficient in Python
• Deep Learning Frameworks: Expertise in at least one major deep learning framework (PyTorch, TensorFlow, or JAX)
• Model Experience: Experience with deep learning and generative architectures such as transformers, diffusion models and autoencoders
• Data Handling: Skilled in working with large-scale datasets and distributed computing environments
• Cloud Experience: Experience with AWS for training, inference and deployment
• ML Fundamentals: Strong understanding of machine learning fundamentals, including various model architectures, optimization techniques, and evaluation metrics
• Data Pipeline Experience: Experience in designing and implementing efficient data pipelines for processing and managing large datasets
• Evaluation Frameworks: Experienced in developing robust evaluation frameworks and ensuring data integrity in machine learning projects
• Software Development Practices: Experienced in code organization, version control, and collaborative software development practices

Responsibilities

• Design and implement advanced machine learning algorithms to enhance model performance and biological understanding.
• Develop and fine-tune generative models for biological applications, exploring innovative architectures to capture the complexities of multi-scale biological systems.
• Work on distributed training systems to scale our models to billions of parameters, optimizing for performance and efficiency across multi-GPU and multi-node setups while handling large-scale biological datasets.
• Engineer efficient data pipelines to manage and process massive biological datasets, addressing challenges in data loading, splitting, and memory optimization.
• Develop and implement robust evaluation frameworks for complex biological models, ensuring data integrity and preventing leakage across dataset splits.

Additional Skills & Qualities

• Proactive approach to problem-solving, thinking beyond the specific task, taking ownership of challenges, and pride in solving them.
• Mature mindset in ambiguous situations, helping to frame questions and seek clarity while making decisions in the face of uncertainty.
• Excellent communication skills and the ability to clearly articulate complex technical concepts.