EPM Scientific
Title:
Machine Learning Researcher
Salary:
$225,000-$275,000
Location:
San Francisco, CA
Join a small, mission-driven team pioneering AI tools to predict drug toxicity and reduce reliance on animal testing. EPM Scientifc is supporting a biotech developing technology to help pharma teams identify safety risks earlier and accelerate the path to new medicines.
What You'll Do: Design and build the full ML pipeline-from data generation and preprocessing to model training, deployment, and infrastructure. Lead development of novel models that connect chemical structure with biological outcomes. Architect and scale large multi-modal models trained on chemical and biological image data. Explore underdeveloped areas like molecular graph learning, contrastive methods, and generative models for biology. Help shape a research culture focused on long-term impact and scientific excellence. Contribute to both product and research, shipping high-impact tools that scientists love. What We're Looking For:
Experience in deep learning, especially with multi-modal or self-supervised learning. Strong engineering skills: PyTorch, Python, data pipelines, and cloud infrastructure. Background in applying ML to complex, noisy scientific data-ideally in chemistry or biology. A track record of impactful ML work, either in industry or academia. Curiosity, initiative, and a desire to build from the ground up.
Machine Learning Researcher
Salary:
$225,000-$275,000
Location:
San Francisco, CA
Join a small, mission-driven team pioneering AI tools to predict drug toxicity and reduce reliance on animal testing. EPM Scientifc is supporting a biotech developing technology to help pharma teams identify safety risks earlier and accelerate the path to new medicines.
What You'll Do: Design and build the full ML pipeline-from data generation and preprocessing to model training, deployment, and infrastructure. Lead development of novel models that connect chemical structure with biological outcomes. Architect and scale large multi-modal models trained on chemical and biological image data. Explore underdeveloped areas like molecular graph learning, contrastive methods, and generative models for biology. Help shape a research culture focused on long-term impact and scientific excellence. Contribute to both product and research, shipping high-impact tools that scientists love. What We're Looking For:
Experience in deep learning, especially with multi-modal or self-supervised learning. Strong engineering skills: PyTorch, Python, data pipelines, and cloud infrastructure. Background in applying ML to complex, noisy scientific data-ideally in chemistry or biology. A track record of impactful ML work, either in industry or academia. Curiosity, initiative, and a desire to build from the ground up.