AI formulation Researcher Co-Op (Spring/Summer 2027)

Panther: Better Health Is Skin Deep

Panther is unlocking the tremendous biological power of our skin, delivering efficacious, shelf-stable treatments directly to customers. Panther's precisely engineered smart microarray patches (SmartPatches) address a wide range of needs in Skin Health (cosmetics, medical aesthetics) and are designed to treat conditions in Systemic Health (infectious diseases, allergies, cancer, autoimmune diseases, metabolic disorders). Billions of people around the world lack access to effective treatment, and we will change this.

SmartPatches enable effective, tech-enabled customer experiences, and our AI platform provides valuable insights for improved patient outcomes and the development of even better treatments. SmartPatches are affordable to manufacture and distribute, and easy to apply, resulting in greater access and market expansion. Panther is developing clinically validated products, the first of which launches in 2027.

The Opportunity

This is a 6-month co-op that begins in January 2027, with the option to extend through the summer.

Panther is building the delivery platform that lets any partner place its treatment precisely into the skin, and algorithmic formulation is where every product starts. AI models built on Panther’s own data take a partner’s active ingredient, target release profile, SmartPatch™ geometry and regulatory requirements, and generate candidate formulations. They then screen those candidates in silico for solubility, molecular interactions, mechanical strength and shelf stability.

Only the strongest move on to physical testing (drug release, toxicity, ex vivo human skin and in vivo models). The result is a clinical formula and a scalable manufacturing process, reached faster than with legacy trial-and-error. Partners get a faster path from molecule to product.

Panther is looking for an AI and machine learning researcher with a strong chemistry foundation who wants to apply computational methods to real formulation and drug delivery problems. You will work directly with the R&D team to build models that help us understand and predict how our formulations, active ingredients and patches behave. This is a hands-on role with real ownership. You will scope problems, choose tools, build models and present results to leadership.

The Impact You’ll Make in Your Co-Op

AI formulation engineers accelerate Panther's formulation work from expert trial-and-error into a repeatable, data-driven pipeline. By the end of year one: a closed loop with measurable gains. By the end of the year at least one partner molecule will reach a clinical-ready formula with fewer lab iterations.The work produces invention disclosures that add to the IP library.

What You Will Do

• Build machine learning models that predict formulation properties such as stability, solubility, dissolution and compatibility between actives and polymers.
• Apply physical chemistry and molecular modeling, including molecular dynamics, to study the molecules we work with: small molecules, peptides, proteins and polymers.
• Develop physics-based and generative models of transport, such as how formulations dissolve and diffuse through skin over time.
• Work with small, high-quality experimental datasets, using synthetic data generation to augment and anchor real lab data.
• Benchmark open-source foundation models and scientific AI tools, and recommend which ones Panther should adopt for R&D and formulation work.
• Partner with our formulation and lab teams in Pittsburgh to turn experimental data into models, and models into experiments worth running.
• Document your methods and results clearly, and present findings to the CTO and the wider team.

Qualifications

• Advanced degree in chemical engineering, materials science, pharmaceutical sciences or computational chemistry, or a BS with 2+ years of equivalent industry experience.
• Solid chemistry foundation: physical and organic chemistry, molecular structure and interactions, and how formulation chemistry affects stability and performance. This is a core requirement.
• Hands-on machine learning experience, ideally with generative models and data-scarce problems, including synthetic data strategies.
• Experience with molecular dynamics, cheminformatics or molecular modeling tools.
• Strong foundation in physics and numerical simulation, such as fluid dynamics, transport or multi-scale modeling.
• Proficiency in Python and the scientific computing stack. Familiarity with ML frameworks and open-source models.
• Interest in life sciences, drug delivery or skincare. Biology background is a plus.

Preferred Qualifications

• Experience with dissolvable microneedles, transdermal or intradermal delivery.
• Using LLMs or generative models for materials or formulation discovery.
• Publications, patents or invention disclosures in drug delivery or computational formulation.
• Startup experience: building tools from scratch and switching between cosmetic, medical-device and partner projects.
• Who Thrives here
• Self-starters who are comfortable with ambiguity and can make progress without a large team or established infrastructure.
• Pragmatic problem solvers who pick the right tool for the problem rather than the most complex one.
• Clear communicators who can explain technical work to scientists, engineers and executives.