Applied AI Scientist

Location

Hybrid or Remote

Role Type

6 months contract to hire

About us

erthos® Inc. is a climate technology company specializing in sustainable material design, with a focus on rapidly reducing the global dependency on plastics by accelerating the scale and widespread adoption of biomaterials. At erthos®, we harness the power of AI, biomaterials, and advocacy to reimagine the building blocks of plastics for the world's largest CPGs and material companies. Join our dynamic team of experts and contribute to groundbreaking advancements in materials science through AI.

Role Overview:

As an Applied AI Scientist at erthos®, you will play a critical role in advancing our sustainable materials discovery platform. You’ll work closely with our Head of Growth, Head of Product, domain scientists, and fellow engineers to develop and deploy machine learning models that push the boundaries of what’s possible in sustainable materials design.

Your work will directly power the platform that enables the world’s largest CPG companies to discover and deploy sustainable packaging solutions faster than ever before. This is a rare opportunity to apply cutting-edge ML to real-world challenges in a mission-driven environment tackling one of the planet’s most urgent challenges.

Welcome to the future of sustainable materials.

About you

• PhD in Machine Learning, Computational Sciences, or a related field — or equivalent industry experience (3+ years).
• Strong background in drug discovery, materials design, or computational chemistry, with experience applying ML to predictive modeling and scientific data analysis.
• Proficient with molecular representations such as SMILES, molecular graphs, and 3D structures; hands-on experience with tools like RDKit and DeepChem.
• Demonstrated contributions to scientific ML research or open-source projects, evidenced by a GitHub profile and/or Google Scholar publications.
• Experience training, fine-tuning, and optimizing deep learning models, including Graph Neural Networks (GNNs) and Transformer-based architectures (e.g., LLMs).
• Skilled in classical supervised and unsupervised learning methods, including XGBoost.
• Familiar with advanced techniques such as uncertainty quantification and Bayesian optimization.
• Proficient in Python.
• Familiarity with MLOps workflows, including model deployment, monitoring, and versioning, as well as experience working with cloud platforms (e.g., AWS) and production ML pipelines
• Highly adaptable and motivated to work in a fast-paced, collaborative startup environment alongside another ML engineer, a machine learning product manager, and domain experts in R&D.

Perks

• Competitive compensation package, including stock options
• A dynamic and supportive work environment
• Opportunities for professional growth and development
• The chance to work on groundbreaking technology that addresses critical environmental challenges
• Free snacks!

Our team is built on diversity and our uniqueness is our strength. We value our entire team's backgrounds, experiences and perspectives. We strive to be the best team players we can be and have fun while doing it.