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About This Role
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.
About the role
We are seeking exceptional 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.
The pay range for this role is:
180,000 \- 200,000 USD per year(New York Office)
Salary Context
This $180K-$200K range is below the median for Research Engineer roles in our dataset (median: $202K across 58 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 4,133 AI roles we're tracking, Research Engineer positions make up 2% of the market. At PATHOS, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $260,000 based on 442 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($190K) sits 27% below the category median. Disclosed range: $180K to $200K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
PATHOS AI Hiring
PATHOS has 1 open AI role right now. They're hiring across Research Engineer. Based in New York, NY, US. Compensation range: $200K - $200K.
Location Context
AI roles in New York pay a median of $211,000 across 2,760 tracked positions. That's 5% above the national median.
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (1,901) and senior (1,663) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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