Drug development shouldn’t be guesswork, not when patients are waiting.

Pathos is building a next-generation biotech with AI at the core. Not as a feature, but as the operating system for how medicines get developed. We believe most drugs don’t fail because the science was wrong. They fail because they were tested in the wrong patients, with the wrong assumptions, in trials that couldn’t answer the real question: who benefits, and why?

Pathos exists to change that. We’re building the largest foundation model in oncology and pairing it with proprietary AI systems, deep oncology expertise, and 200+ petabytes of multimodal data linked to patient outcomes, so we can make development decisions with more precision, much earlier. 

This is not theoretical. We’re well-capitalized and have the leadership to build a generational company. We invest in and advance our own clinical-stage programs, using our AI platform to sharpen trial design, patient selection and biomarker strategy. So therapies reach the patients most likely to benefit, sooner.

If you’re driven by purpose, energized by complexity, and want to apply AI, biology, or both to redefine the future drug development, come build Pathos with us.

About the role

We are hiring Machine Learning Engineer and Research Engineer Interns. You will work alongside senior researchers and engineers on high-impact projects spanning:

• Thousands-of-GPUs-scale training and inference
• Foundation models and representation learning for biomedical and clinical data
• Agentic AI systems that support scientific and operational workflows across the drug development lifecycle

This role is ideal for candidates who want to operate at the intersection of frontier machine learning and real-world, high-stakes research and production systems.

What You Will Do

Depending on your strengths and the team’s needs, you will:

• Build and optimize large-scale ML systems for training and inference
• Contribute to foundation-model development, evaluation, and deployment (e.g., multimodal learning, representation learning, retrieval, alignment, and robustness)
• Develop agentic AI components (tool use, planning, orchestration, evaluation harnesses) to accelerate internal scientific and engineering workflows
• Profile and optimize performance across distributed training stacks
• Collaborate closely with cross-functional partners (research, platform, translational/clinical stakeholders) to translate research ideas into scalable, production-ready systems

Qualifications

We are open to diverse backgrounds. You do not need to meet every item below.

Minimum Qualifications

• Strong programming ability in Python
• Solid fundamentals in machine learning / deep learning through coursework, research, internships, or substantial projects
• Experience with PyTorch and modern training workflows
• Comfort operating in ambiguous problem spaces with a bias toward execution

Preferred Qualifications

• Experience with distributed systems (e.g., multi-node training, large-scale data loaders, cluster scheduling)
• Familiarity with performance optimization (profiling, kernel efficiency, GPU utilization, throughput/latency)
• Research experience (papers, preprints, open-source contributions, or significant independent work)
• Exposure to biomedical, clinical, or multimodal datasets (helpful but not required)

What We Offer

• Hands-on work on hyperscale ML infrastructure and state-of-the-art models
• Opportunities to publish in top-tier venues such as NeurIPS, ACL, and ICML
• Competitive compensation, strong candidates will be considered for full-time roles
• Visa sponsorship, including H-1B, may be available for exceptional candidates, subject to eligibility and company policy

We encourage new and recent graduates to apply

• Undergraduates or graduates seeking frontier ML systems and research exposure
• Individuals ready to build at the boundary of ML research and production systems
• Engineers looking to scale skills in distributed training, model development, and agentic systems

Location

This is a hybrid role, requiring up to 3 days per week onsite, in our NYC Headquarters.