Research Scientist - Foundation Models - Strativ Group : Job Details

About the Company:

Our client is a Series A startup revolutionizing the intersection of artificial intelligence and science. With over $200 million in funding from leading investors, they are setting a new standard in the development and application of state-of-the-art (SOTA) foundation models. Their cutting-edge research drives innovation across diverse scientific domains, including biology, chemistry, and physics. The team comprises world-class researchers, engineers, and scientists united by a mission to solve some of the most challenging problems in science using AI.

The Role:

The Research Scientist role is an opportunity to join a team dedicated to advancing the limits of foundation models for science. You will work on designing, training, and scaling foundation models that enable groundbreaking research and applications in scientific discovery. As a core contributor, you will publish in top-tier machine learning conferences and collaborate closely with leading domain experts to bridge AI and science.

Key Responsibilities:

• Lead research initiatives to develop novel algorithms and architectures for foundation models.
• Design and conduct experiments to push the boundaries of model performance and efficiency.
• Collaborate with cross-disciplinary teams to integrate foundation models into scientific workflows.
• Publish and present findings at top ML conferences such as NeurIPS, ICML, and ICLR.
• Mentor junior researchers and contribute to fostering a culture of scientific excellence.

About You:

To thrive in this role, you should bring a deep passion for AI research, a rigorous academic background, and a desire to impact scientific discovery.

Required Qualifications:

• PhD in Computer Science, Machine Learning, or a related field from a top-tier university.
• A proven track record of publishing in top ML conferences (e.g., NeurIPS, ICML, ICLR).
• Strong programming skills in Python and deep learning frameworks (e.g., PyTorch, TensorFlow).
• Expertise in foundation models, large-scale training, and distributed systems.
• Ability to work independently on open-ended research questions while delivering impactful results.

Preferred Qualifications:

• Experience applying ML techniques to scientific domains such as biology, chemistry, or physics.
• Familiarity with techniques such as transfer learning, generative modeling, or multi-modal learning.
• A collaborative mindset with excellent communication and problem-solving skills.

Why Join?

• Work at the forefront of AI research on transformative projects.
• Collaborate with a team of exceptional talent in an environment of intellectual curiosity.
• Contribute to applications that directly impact scientific breakthroughs.
• Competitive compensation and equity at a high-growth, well-funded startup.