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Senior Research Engineer

Pathos · New York City, NY, United States · On Site · Active · $180,000–$200,000 / year · Rippling ATS

Job facts

FieldValue
CompanyPathos
TitleSenior Research Engineer
Normalized title-
Department / teamEngineering
LocationNew York City, NY, United States
Work modelOn Site
Employment typeFull Time
Salary$180,000–$200,000 / year
Statusactive
ATS providerRippling ATS
Posted / first seen2026-02-19 / 2026-05-29
Changed / last seen2026-06-06 / 2026-06-06

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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 2–4 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 seeking exceptional Senior Research Engineers to join our mission-critical team building the world's best oncology foundational models. As an AI-driven drug development company, these models are the engine that powers everything we do, from predicting patient survival, to identifying novel therapeutic targets to optimizing clinical trial design. In this role, you'll be at the intersection of cutting-edge AI research and real-world drug development. You'll work on foundational models that integrate diverse data modalities, known cancer biology, tumor mechanisms, DNA/RNA sequencing, detailed medical notes, and examination results to generate insights that directly inform our clinical-stage programs. You'll participate in both pre-training and post-training of our foundation models, requiring deep expertise in modern architectures and post-training algorithms such as reinforcement learning. You may also operate at the CUDA level, building customized kernels and understanding performance at the hardware-software interface. What You'll Do Design, implement, and optimize large-scale oncology foundation models integrating genomic sequences, medical notes, lab results, imaging, and clinical outcomes Build and experiment with modern architectures optimized for biomedical applications Spearhead pre-training and post-training efforts, including RLHF, DPO, RLAIF, and other alignment techniques Write and optimize custom CUDA kernels; profile and resolve performance bottlenecks across the hardware-software interface Maintain and optimize our 1,000+ H200 GPU cluster for reliability, utilization, and performance Build distributed training and inference pipelines, experiment tracking systems, and evaluation frameworks Develop benchmarks that measure real progress on drug development-relevant tasks Collaborate with oncologists, biologists, and clinical development teams to ground model development in real therapeutic questions Contribute to publications in top-tier ML and biomedical venues (NeurIPS, ICML, ICLR, Nature, Cell, etc.) What We're Looking For Required Ph.D. in Computer Science, Machine Learning, Computational Biology, or a related field, or an M.S. with 5+ years of relevant industry experience Publication record in machine learning, including multiple first-author papers at top-tier venues 3 to 5 years of hands-on deep learning experience (PyTorch, JAX, or TensorFlow) Strong command of modern architectures: Transformers, attention mechanisms, state-space models, mixture-of-experts Hands-on experience with post-training techniques: RLHF, DPO, PPO, or similar Expert-level GPU programming and CUDA, including custom kernel development and performance profiling Practical experience training or fine-tuning large-scale models (multi-billion parameter) in distributed settings (DeepSpeed, FSDP, Megatron, or similar) Experience managing GPU clusters and ML infrastructure (Kubernetes, SLURM, or equivalent) Strong software engineering fundamentals in Python and C++/CUDA Clear communicator, able to present complex technical work to both engineering and scientific audiences Preferred Background in oncology, cancer biology, or drug development Experience with biomedical foundation models (AlphaGenome, GeneFormer, Evo2, etc.) Deep knowledge of cancer genomics, tumor biology, or mechanisms of resistance Contributions to ML systems frameworks (FlashAttention, Triton, xFormers, etc.) Experience with multi-modal learning and cross-modal architectures Familiarity with advanced training techniques: synthetic data generation, curriculum learning, data filtering Familiarity with regulatory considerations in healthcare AI (FDA, HIPAA, GxP) Open-source contributions to ML projects or frameworks Location This is a hybrid role, requiring up to 3-4 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
Providerrippling
Provider Job Keyc069de57-c9af-4f94-bfb4-4c908b2b06ab
TitleSenior Research Engineer
Normalized Title—
Statusactive
Activeyes
Location TextNew York City, NY, United States
DepartmentEngineering
Team—
Employment Typefull_time
Workplace Typeon_site
Remote Policy—
CountryUnited States
RegionNY
CityNew York City
Salary RawUSD 180000-200000 YEAR
Salary Min180,000
Salary Max200,000
Salary CurrencyUSD
Salary Periodyear
Source URLhttps://ats.rippling.com/pathos/jobs/c069de57-c9af-4f94-bfb4-4c908b2b06ab
Apply URLhttps://ats.rippling.com/pathos/jobs/c069de57-c9af-4f94-bfb4-4c908b2b06ab
First Seen At2026-05-29 07:13:53Z
Last Seen At2026-06-06 19:47:28Z
Last Checked At2026-06-06 19:47:28Z
Last Changed At2026-06-06 19:47:28Z
Inactive At—
Source Posted At2026-02-19 21:50:01Z
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 seeking exceptional Senior Research Engineers to join our mission-critical team building the world's best oncology foundational models. As an AI-driven drug development company, these models are the engine that powers everything we do, from predicting patient survival, to identifying novel therapeutic targets to optimizing clinical trial design.</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;\">In this role, you'll be at the intersection of cutting-edge AI research and real-world drug development. You'll work on foundational models that integrate diverse data modalities, known cancer biology, tumor mechanisms, DNA/RNA sequencing, detailed medical notes, and examination results to generate insights that directly inform our clinical-stage programs.</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;\">You'll participate in both pre-training and post-training of our foundation models, requiring deep expertise in modern architectures and post-training algorithms such as reinforcement learning. You may also operate at the CUDA level, building customized kernels and understanding performance at the hardware-software interface.</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: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;\">What You'll Do</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;\">Design, implement, and optimize large-scale oncology foundation models integrating genomic sequences, medical notes, lab results, imaging, and clinical outcomes</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;\">Build and experiment with modern architectures optimized for biomedical applications</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;\">Spearhead pre-training and post-training efforts, including RLHF, DPO, RLAIF, and other alignment techniques</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;\">Write and optimize custom CUDA kernels; profile and resolve performance bottlenecks across the hardware-software interface</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;\">Maintain and optimize our 1,000+ H200 GPU cluster for reliability, utilization, and performance</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;\">Build distributed training and inference pipelines, experiment tracking systems, and evaluation frameworks</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 benchmarks that measure real progress on drug development-relevant tasks</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;\">Collaborate with oncologists, biologists, and clinical development teams to ground model development in real therapeutic questions</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;\">Contribute to publications in top-tier ML and biomedical venues (NeurIPS, ICML, ICLR, Nature, Cell, etc.)</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;\">What We're Looking For</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;\"><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Required</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;\">Ph.D. in Computer Science, Machine Learning, Computational Biology, or a related field, or an M.S. with 5+ years of relevant industry experience</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;\">Publication record in machine learning, including multiple first-author papers at top-tier venues</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;\">3 to 5 years of hands-on deep learning experience (PyTorch, JAX, or TensorFlow)</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;\">Strong command of modern architectures: Transformers, attention mechanisms, state-space models, mixture-of-experts</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;\">Hands-on experience with post-training techniques: RLHF, DPO, PPO, or similar</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;\">Expert-level GPU programming and CUDA, including custom kernel development and performance profiling</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;\">Practical experience training or fine-tuning large-scale models (multi-billion parameter) in distributed settings (DeepSpeed, FSDP, Megatron, or similar)</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 managing GPU clusters and ML infrastructure (Kubernetes, SLURM, or equivalent)</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;\">Strong software engineering fundamentals in Python and C++/CUDA</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;\">Clear communicator, able to present complex technical work to both engineering and scientific audiences</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</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;\">Background in oncology, cancer biology, or drug development</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 biomedical foundation models (AlphaGenome, GeneFormer, Evo2, 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 knowledge of cancer genomics, tumor biology, or mechanisms of resistance</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;\">Contributions to ML systems frameworks (FlashAttention, Triton, xFormers, 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;\">Experience with multi-modal learning and cross-modal architectures</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 advanced training techniques: synthetic data generation, curriculum learning, data filtering</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 regulatory considerations in healthcare AI (FDA, HIPAA, GxP)</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;\">Open-source contributions to ML projects or frameworks</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 3-4 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;\"><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=\"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><span style=\"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><span style=\"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><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><br><br><b><strong style=\"font-size:12pt;white-space:pre-wrap;\">How We Build</strong></b><br><span style=\"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 2–4 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><span style=\"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><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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