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ML Engineer (Intern)

Pathos · New York City, NY, United States · On Site · Active · $30–$60 / hour · Rippling ATS

Job facts

FieldValue
CompanyPathos
TitleML Engineer (Intern)
Normalized title-
Department / teamEngineering
LocationNew York City, NY, United States
Work modelOn Site
Employment typeTemporary
Salary$30–$60 / hour
Statusactive
ATS providerRippling ATS
Posted / first seen2026-05-13 / 2026-05-29
Changed / last seen2026-06-18 / 2026-06-18

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City jobsActive postings in New York City.Open
Department jobsActive postings in Engineering.Open
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Linked records

CompanyPathos
Source2d66e9b5-1b63-475c-a03a-943980720722
ATS providerRippling ATS

Description

company 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. How We Build Pathos does not operate like a traditional biotech. There is no middle management. There are no layers of approval. The company is designed, from the ground up, around small teams of 2 to 4 subject matter experts who each command hundreds of AI agents to do the work that used to require dozens of people. Everyone builds. Everyone ships. Every function at Pathos — from clinical execution to asset selection to the foundation model itself — runs on this model. Our product velocity delivers meaningful outcomes in hours instead of weeks. This is not a future aspiration. It is how we operate today. The people who thrive here are operators: deep experts who can specify what needs to happen, orchestrate AI agents to execute at scale, and make high-judgment calls that compound over time. If you have spent your career building and shipping AI systems at scale, this is the environment where that experience becomes a superpower. role About the role We are hiring Machine Learning Engineer Interns. You will work alongside senior researchers and engineers on high-impact projects spanning: Hyper-scale training & inference infrastructure Pre-training & Post-training of multi-modal foundational models Knowledge Graph (KG) & Retrieval Augmented Generation (RAG) Evaluation of reasoning capabilities (logic, metric design, dataset curation) 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: Use Nsight to profile and analyze post-training pipeline, identify process that dominates wall-clock time (rollout GEMM vs KV cache I/O vs weight reloading vs reward compute) Design and prototype an NCCL-based weight broadcast path that streams updated LoRA (and, optionally, full base) weights directly into inference engine’s GPU memory Improve hyper-scale training throughput and efficiency by investigating sharding granularity, mixed-precision policy, communication overlap, gradient bucketing, etc. Deep dive into Mixture-of-Experts training strategies, study how to layout tensor, expert, and data parallel groups on H200 with InfiniBand island. Token vs sequence level routing Design strategies to maintain training stability and load balancing, including aux-loss design, capacity factor, drop/pad policies, router z-loss, expert dropout. Experiment and derive best practice for SFT and RL on top of a pre-trained MoE, router freezing, gradient flow concerns Develop prefill/decode disaggregation serving to decouple long-prompt prefill cost from autoregressive decode loop, deep dive into node replacement, KV cache transfer over NVlink/InfiniBand, scheduling policy, and how to balance pools as load mixes shifts. 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 experience on thousand scale GPUs infrastructure Full cycle multi-modal foundational model training, from Pre-training to Post-training Opportunities to publish in top-tier venues such as NeurIPS, ACL, and ICML Competitive compensation , strong candidates will be considered for full-time roles 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.

Full job record

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Org ID25325165-9d82-45f6-8e64-74c33db17449
Source ID2d66e9b5-1b63-475c-a03a-943980720722
Board ID2d66e9b5-1b63-475c-a03a-943980720722
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Provider Job Key430a21a7-8718-47ff-8963-2095727cce57
TitleML Engineer (Intern)
Normalized Title—
Statusactive
Activeyes
Location TextNew York City, NY, United States
DepartmentEngineering
Team—
Employment Typetemporary
Workplace Typeon_site
Remote Policy—
CountryUnited States
RegionNY
CityNew York City
Salary RawUSD 30-60 HOUR
Salary Min30
Salary Max60
Salary CurrencyUSD
Salary Periodhour
Source URLhttps://ats.rippling.com/pathos/jobs/430a21a7-8718-47ff-8963-2095727cce57
Apply URLhttps://ats.rippling.com/pathos/jobs/430a21a7-8718-47ff-8963-2095727cce57
First Seen At2026-05-29 07:13:53Z
Last Seen At2026-06-18 09:25:01Z
Last Checked At2026-06-18 09:25:01Z
Last Changed At2026-06-18 09:25:01Z
Inactive At—
Source Posted At2026-05-13 18:18:35Z
Source Updated At—
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      "role": "<meta><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:14pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"font-size:14pt;white-space:pre-wrap;\">About the role</strong></b></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">We are hiring </span><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Machine Learning Engineer Interns. </strong></b><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">You will work alongside senior researchers and engineers on high-impact projects spanning:</span></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Hyper-scale training &amp; inference infrastructure</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Pre-training &amp; Post-training of multi-modal foundational models</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Knowledge Graph (KG) &amp; Retrieval Augmented Generation (RAG)</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Evaluation of reasoning capabilities (logic, metric design, dataset curation)</span></li></ul><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">This role is ideal for candidates who want to operate at the intersection of </span><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">frontier machine learning</strong></b><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\"> and </span><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">real-world, high-stakes research and production systems</strong></b><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">.</span></p><h3 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:14pt;font-weight:600;letter-spacing:0.25px;margin-top:14px;margin-bottom:4px;padding-left:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:14pt;white-space:pre-wrap;\">What You Will Do</strong></b></h3><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Depending on your strengths and the team’s needs, you will:</span></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Use Nsight to profile and analyze post-training pipeline, identify process that dominates wall-clock time (rollout GEMM vs KV cache I/O vs weight reloading vs reward compute)</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Design and prototype an NCCL-based weight broadcast path that streams updated LoRA (and, optionally, full base) weights directly into inference engine’s GPU memory</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Improve hyper-scale training throughput and efficiency by investigating sharding granularity, mixed-precision policy, communication overlap, gradient bucketing, etc.</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Deep dive into Mixture-of-Experts training strategies, study how to layout tensor, expert, and data parallel groups on H200 with InfiniBand island. Token vs sequence level routing</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Design strategies to maintain training stability and load balancing, including aux-loss design, capacity factor, drop/pad policies, router z-loss, expert dropout.</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Experiment and derive best practice for SFT and RL on top of a pre-trained MoE, router freezing, gradient flow concerns</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Develop prefill/decode disaggregation serving to decouple long-prompt prefill cost from autoregressive decode loop, deep dive into node replacement, KV cache transfer over NVlink/InfiniBand, scheduling policy, and how to balance pools as load mixes shifts.</span></li></ul><h3 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:14pt;font-weight:600;letter-spacing:0.25px;margin-top:14px;margin-bottom:4px;padding-left:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:14pt;white-space:pre-wrap;\">Qualifications</strong></b></h3><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">We are open to diverse backgrounds. You do not need to meet every item below.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Minimum Qualifications</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Strong programming ability in </span><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Python</strong></b></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Solid fundamentals in </span><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">machine learning / deep learning</strong></b><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\"> through coursework, research, internships, or substantial projects</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Experience with </span><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">PyTorch</strong></b><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\"> and modern training workflows</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Comfort operating in ambiguous problem spaces with a bias toward execution</span></li></ul><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Preferred Qualifications</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Experience with distributed systems (e.g., multi-node training, large-scale data loaders, cluster scheduling)</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Familiarity with performance optimization (profiling, kernel efficiency, GPU utilization, throughput/latency)</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Research experience (papers, preprints, open-source contributions, or significant independent work)</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Exposure to biomedical, clinical, or multimodal datasets (helpful but not required)</span></li></ul><h3 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:12pt;font-weight:600;letter-spacing:0.25px;margin-top:14px;margin-bottom:4px;padding-left:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">What We Offer</strong></b></h3><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Hands-on experience on</span><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\"> thousand scale GPUs </strong></b><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">infrastructure</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Full cycle </span><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">multi-modal foundational model</strong></b><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\"> training, from Pre-training to Post-training</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Opportunities to </span><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">publish in top-tier venues</strong></b><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\"> such as NeurIPS, ACL, and ICML</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Competitive compensation</strong></b><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">, strong candidates will be considered for full-time roles</span></li></ul><h3 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:12pt;font-weight:600;letter-spacing:0.25px;margin-top:14px;margin-bottom:4px;padding-left:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">We encourage new and recent graduates to apply</strong></b></h3><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Undergraduates or graduates seeking frontier ML systems and research exposure</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Individuals ready to build at the boundary of ML research and production systems</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Engineers looking to scale skills in distributed training, model development, and agentic systems</span></li></ul><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:14pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:14pt;white-space:pre-wrap;\">Location</strong></b></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">This is a hybrid role, requiring up to 3 days per week onsite, in our NYC Headquarters.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;text-align:justify;\"><br></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p>",
      "company": "<meta><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Drug development shouldn’t be guesswork, not when patients are waiting.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">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?</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">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.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"font-size:12pt;white-space:pre-wrap;\">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.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">How We Build</strong></b></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Pathos does not operate like a traditional biotech. There is no middle management. There are no layers of approval. The company is designed, from the ground up, around small teams of 2 to 4 subject matter experts who each command hundreds of AI agents to do the work that used to require dozens of people.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Everyone builds. Everyone ships. Every function at Pathos — from clinical execution to asset selection to the foundation model itself — runs on this model. Our product velocity delivers meaningful outcomes in hours instead of weeks. This is not a future aspiration. It is how we operate today.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"font-size:12pt;white-space:pre-wrap;\">The people who thrive here are operators: deep experts who can specify what needs to happen, orchestrate AI agents to execute at scale, and make high-judgment calls that compound over time. If you have spent your career building and shipping AI systems at scale, this is the environment where that experience becomes a superpower.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p>"
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