Principal Machine Learning Scientist

About Us

At Matterworks we are building AI tools to extract insights from the ever-growing corpora of biological data and to unlock opportunities in therapeutic discovery, development, and manufacturing. We are building large-scale deep learning models of biological data to predict the phenotype and behavior of biological systems.

Position Overview

As a Principal Machine Learning Scientist, you will be a technical leader for our research and development efforts. You will be responsible for setting the long-term technical vision and strategy for our deep learning architecture. This is a strategic, hands-on role for a scientist who is passionate about architecting foundational neural networks and can lead ambitious, multi-year research and engineering initiatives. You will be instrumental in guiding the company's scientific direction and mentoring our talented team of scientists.

Key Responsibilities

• Architectural Vision and Strategy: Define and drive the long-term vision for our core deep learning architecture. Make critical decisions on the foundational design of our neural networks to ensure they are scalable, robust, and optimized for interpreting mass spectrometry data.

• Foundational Model Development: Lead the conceptualization, design, and implementation of innovative deep learning models. Your work will involve creating novel network architectures and algorithms tailored to scientific discovery.

• Scientific Leadership: Provide intellectual leadership to the machine learning team and the broader company. Stay at the forefront of academic and industry advancements in deep learning, particularly as they apply to chemistry and biology, and guide the team in applying cutting-edge techniques.

• Cross-Functional Collaboration: Work closely with leadership in engineering, product, and scientific discovery to align machine learning initiatives with company-wide goals.

• Mentorship and Guidance: Mentor and develop our team of machine learning scientists, fostering a culture of technical excellence, creativity, and rigorous scientific inquiry.

Qualifications

• Ph.D. in a quantitative field such as Computer Science, Chemistry, Physics, or a related discipline, or equivalent research experience.

• 10+ years of experience in machine learning, with a significant focus on architecting novel deep learning systems.

• Demonstrated experience in leading the architectural design of complex deep learning systems. This should be evident through publications, patents, or a portfolio of shipped products.

• Expertise or strong familiarity with machine learning for spectral analysis, molecular structure prediction, or novel molecular representation learning is highly preferred.

• A strong publication record in top-tier machine learning or scientific journals (e.g., NeurIPS, ICML, Nature, Science) is highly desirable.

• Proficiency with common scientific data formats (e.g., HDF5, mzML, zarr).