UniversalAGI, Inc.
Founding Deep Learning Researcher
UniversalAGI, Inc., San Francisco, California, United States, 94199
San Francisco | Work Directly with CEO & Founding Team | Report to CEO | OpenAI for Physics |
5 Days Onsite Location:
Onsite in San Francisco
Compensation:
Competitive Salary + Equity
Who We Are UniversalAGI is building OpenAI for Physics . AI startup based in San Francisco and backed by Elad Gil (#1 Solo VC), Eric Schmidt (former Google CEO), Prith Banerjee (ANSYS CTO), Ion Stoica (Databricks Founder), Jared Kushner (former Senior Advisor to the President), David Patterson (Turing Award Winner), and Luis Videgaray (former Foreign and Finance Minister of Mexico). We're building foundation AI models for physics that enable end-to-end industrial automation from initial design through optimization, validation, and production.
We're building a high-velocity team of relentless researchers and engineers that will define the next generation of AI for industrial engineering. If you're passionate about AI, physics, or the future of industrial innovation, we want to hear from you.
About the Role As a founding Deep Learning Researcher, you'll be architecting and training the foundation models that will transform how industries approach physics simulation and engineering design. This isn't a research role in isolation—you'll be shipping models that customers depend on for critical engineering decisions worth millions of dollars.
You’ll work directly with the CEO and founding team to push the boundaries of what AI can do with physics data. You'll design novel architectures that can learn from CFD simulations, build training pipelines that scale to petabytes of data, and iterate rapidly based on customer feedback and real-world performance.
This is your opportunity to define how foundation models learn physics, from the ground up.
What You'll Do
Design and train foundation models
for physics simulation, working with GNNs, CNNs, GCNs, PointNet, RegDGCNN, Neural Operators, transformer architectures, diffusion models, and other cutting‑edge approaches adapted for physical systems
Build training pipelines
from scratch: data preprocessing, tokenization strategies for physics data, loss functions that capture physical accuracy, and training loops that scale to massive datasets
Optimize model architectures
for physics: Balance model capacity, inference speed, and accuracy for industrial use cases with strict performance requirements
Develop novel approaches
to physics‑informed learning: Integrate physical constraints, conservation laws, and domain knowledge directly into model architectures and training objectives
Fine‑tune and adapt models
to customer‑specific domains, data, and requirements while maintaining generalization and avoiding catastrophic forgetting
Collaborate with infrastructure team
to optimize training efficiency, implement distributed training strategies, and ensure models can be served at scale
Validate model performance
against ground truth simulations and real‑world engineering data, building robust evaluation frameworks that customers trust
Work directly with customers
to understand their physics problems, gather domain expertise, and translate engineering requirements into model capabilities
Drive rapid experimentation : Run dozens of training experiments per week, systematically testing hypotheses and improving model performance
Ship models to production : Take responsibility for model quality from initial training through deployment and ongoing monitoring in customer environments
This is a role for someone who lives at the intersection of deep learning research and production ML, who can both read the latest papers and ship models that work reliably in high‑stakes industrial settings.
Qualifications
3+ years of hands‑on experience
training deep learning models, with a track record of shipping models to production
Deep expertise in modern deep learning frameworks
(PyTorch, JAX) and model architectures (Transformers, Diffusion Models, Graph Neural Networks, GNNs, CNNs, GCNs, PointNet, RegDGCNN, Neural Operators, etc.)
Strong foundation in distributed training : Experience with multi‑GPU and multi‑node training, gradient accumulation, mixed precision, and optimization techniques
Expert‑level Python
and proficiency with ML libraries (HuggingFace, PyTorch Lightning, etc.)
Solid understanding of ML fundamentals : Optimization, regularization, generalization, evaluation metrics, and the full training lifecycle
Experience with large‑scale datasets : Building data pipelines, handling data quality issues, and working with diverse data formats
Strong intuition for debugging models : Can diagnose training instabilities, convergence issues, and performance bottlenecks
Research mindset with execution focus : Can read and implement papers quickly, but prioritizes shipping working solutions over perfect ones
Outstanding problem‑solving : Willing to dive deep into unfamiliar domains (physics, CFD, engineering) and learn what's needed
Excellent communicator : Can explain complex model behavior to customers, engineers, and non‑technical stakeholders
Thrives in ambiguity : Comfortable defining what success looks like and figuring out how to get there
Bonus Qualifications
PhD or Masters in ML/AI, Physics, or related field
(or equivalent industry experience)
Published research
in top‑tier ML conferences (NeurIPS, ICML, ICLR) or physics‑ML venues
Experience with physics‑informed methods , neural operators, or other physics‑ML approaches
Background in physics, computational physics, or engineering
(CFD, FEA, multiphysics simulation)
Experience training foundation models
or large‑scale pretrained models (LLMs, vision models, multimodal models)
Deep knowledge of numerical methods : Quantization, pruning, distillation, efficient architectures
Experience with numerical methods and simulation : Finite element methods, finite difference methods, spectral methods, or other computational approaches to solving PDEs
Experience with geometric deep learning , graph neural networks, or models for 3D data
Built custom CUDA kernels
or optimized ML operations for specific domains
Experience at leading AI labs
(OpenAI, DeepMind, Anthropic, Meta AI) or high‑growth AI startups
Open‑source contributions
to ML frameworks or well‑known model implementations
Forward‑deployed experience
working directly with customers on model adaptation and deployment
Cultural Fit
Technical Respect : Ability to earn respect through hands‑on technical contribution
Intensity : Thrives in our unusually intense culture - willing to grind when needed
Customer Obsession : Passionate about solving real customer problems, not just publishing papers
Deep Work : Values long, uninterrupted periods of focused work over meetings
High Availability : Ready to be deeply involved whenever critical issues arise
Communication : Can translate complex model decisions to customers and team
Growth Mindset : Embraces the compounding returns of intelligence and continuous learning
Startup Mindset : Comfortable with ambiguity, rapid change, and wearing multiple hats
Work Ethic : Willing to put in the extra hours when needed to hit critical milestones
Team Player : Collaborative approach with low ego and high accountability
Bias for Action : Ships experiments fast, learns from failures, and iterates quickly
What We Offer
Opportunity to define
the future of physics AI from the ground up
Work on cutting‑edge problems
at the intersection of deep learning and physics simulation
Direct collaboration
with the founder & CEO and ability to influence company strategy
Competitive compensation
with significant equity upside
In‑person first culture
- 5 days a week in office with a team that values face‑to‑face collaboration
Access to world‑class investors and advisors
in the AI space
Cutting‑edge compute resources
for training foundation models
Benefits We provide great benefits, including:
Competitive compensation and equity.
Competitive health, dental, vision benefits paid by the company.
401(k) plan offering.
Flexible vacation.
Team Building & Fun Activities.
Great scope, ownership and impact.
AI tools stipend.
Monthly commute stipend.
Monthly wellness / fitness stipend.
Daily office lunch & dinner covered by the company.
Immigration support.
How We’re Different The credit belongs to the man who is actually in the arena, whose face is marred by dust and sweat and blood; who strives valiantly; who errs, who comes short again and again... who at the best, knows in the end the triumph of high achievement, and who at the worst, if he fails, at least fails while daring greatly – Teddy Roosevelt.
At our core, we believe in being “in the arena.” We are builders, problem solvers, and risk‑takers who show up every day ready to put in the work: to sweat, to struggle, and to push past our limits. We know that real progress comes with missteps, iteration, and resilience. We embrace that journey fully knowing that daring greatly is the only way to create something truly meaningful.
If you're ready to train the models that will revolutionize physics simulation, push the boundaries of what AI can learn, and deliver real impact, UniversalAGI is the place for you.
#J-18808-Ljbffr
5 Days Onsite Location:
Onsite in San Francisco
Compensation:
Competitive Salary + Equity
Who We Are UniversalAGI is building OpenAI for Physics . AI startup based in San Francisco and backed by Elad Gil (#1 Solo VC), Eric Schmidt (former Google CEO), Prith Banerjee (ANSYS CTO), Ion Stoica (Databricks Founder), Jared Kushner (former Senior Advisor to the President), David Patterson (Turing Award Winner), and Luis Videgaray (former Foreign and Finance Minister of Mexico). We're building foundation AI models for physics that enable end-to-end industrial automation from initial design through optimization, validation, and production.
We're building a high-velocity team of relentless researchers and engineers that will define the next generation of AI for industrial engineering. If you're passionate about AI, physics, or the future of industrial innovation, we want to hear from you.
About the Role As a founding Deep Learning Researcher, you'll be architecting and training the foundation models that will transform how industries approach physics simulation and engineering design. This isn't a research role in isolation—you'll be shipping models that customers depend on for critical engineering decisions worth millions of dollars.
You’ll work directly with the CEO and founding team to push the boundaries of what AI can do with physics data. You'll design novel architectures that can learn from CFD simulations, build training pipelines that scale to petabytes of data, and iterate rapidly based on customer feedback and real-world performance.
This is your opportunity to define how foundation models learn physics, from the ground up.
What You'll Do
Design and train foundation models
for physics simulation, working with GNNs, CNNs, GCNs, PointNet, RegDGCNN, Neural Operators, transformer architectures, diffusion models, and other cutting‑edge approaches adapted for physical systems
Build training pipelines
from scratch: data preprocessing, tokenization strategies for physics data, loss functions that capture physical accuracy, and training loops that scale to massive datasets
Optimize model architectures
for physics: Balance model capacity, inference speed, and accuracy for industrial use cases with strict performance requirements
Develop novel approaches
to physics‑informed learning: Integrate physical constraints, conservation laws, and domain knowledge directly into model architectures and training objectives
Fine‑tune and adapt models
to customer‑specific domains, data, and requirements while maintaining generalization and avoiding catastrophic forgetting
Collaborate with infrastructure team
to optimize training efficiency, implement distributed training strategies, and ensure models can be served at scale
Validate model performance
against ground truth simulations and real‑world engineering data, building robust evaluation frameworks that customers trust
Work directly with customers
to understand their physics problems, gather domain expertise, and translate engineering requirements into model capabilities
Drive rapid experimentation : Run dozens of training experiments per week, systematically testing hypotheses and improving model performance
Ship models to production : Take responsibility for model quality from initial training through deployment and ongoing monitoring in customer environments
This is a role for someone who lives at the intersection of deep learning research and production ML, who can both read the latest papers and ship models that work reliably in high‑stakes industrial settings.
Qualifications
3+ years of hands‑on experience
training deep learning models, with a track record of shipping models to production
Deep expertise in modern deep learning frameworks
(PyTorch, JAX) and model architectures (Transformers, Diffusion Models, Graph Neural Networks, GNNs, CNNs, GCNs, PointNet, RegDGCNN, Neural Operators, etc.)
Strong foundation in distributed training : Experience with multi‑GPU and multi‑node training, gradient accumulation, mixed precision, and optimization techniques
Expert‑level Python
and proficiency with ML libraries (HuggingFace, PyTorch Lightning, etc.)
Solid understanding of ML fundamentals : Optimization, regularization, generalization, evaluation metrics, and the full training lifecycle
Experience with large‑scale datasets : Building data pipelines, handling data quality issues, and working with diverse data formats
Strong intuition for debugging models : Can diagnose training instabilities, convergence issues, and performance bottlenecks
Research mindset with execution focus : Can read and implement papers quickly, but prioritizes shipping working solutions over perfect ones
Outstanding problem‑solving : Willing to dive deep into unfamiliar domains (physics, CFD, engineering) and learn what's needed
Excellent communicator : Can explain complex model behavior to customers, engineers, and non‑technical stakeholders
Thrives in ambiguity : Comfortable defining what success looks like and figuring out how to get there
Bonus Qualifications
PhD or Masters in ML/AI, Physics, or related field
(or equivalent industry experience)
Published research
in top‑tier ML conferences (NeurIPS, ICML, ICLR) or physics‑ML venues
Experience with physics‑informed methods , neural operators, or other physics‑ML approaches
Background in physics, computational physics, or engineering
(CFD, FEA, multiphysics simulation)
Experience training foundation models
or large‑scale pretrained models (LLMs, vision models, multimodal models)
Deep knowledge of numerical methods : Quantization, pruning, distillation, efficient architectures
Experience with numerical methods and simulation : Finite element methods, finite difference methods, spectral methods, or other computational approaches to solving PDEs
Experience with geometric deep learning , graph neural networks, or models for 3D data
Built custom CUDA kernels
or optimized ML operations for specific domains
Experience at leading AI labs
(OpenAI, DeepMind, Anthropic, Meta AI) or high‑growth AI startups
Open‑source contributions
to ML frameworks or well‑known model implementations
Forward‑deployed experience
working directly with customers on model adaptation and deployment
Cultural Fit
Technical Respect : Ability to earn respect through hands‑on technical contribution
Intensity : Thrives in our unusually intense culture - willing to grind when needed
Customer Obsession : Passionate about solving real customer problems, not just publishing papers
Deep Work : Values long, uninterrupted periods of focused work over meetings
High Availability : Ready to be deeply involved whenever critical issues arise
Communication : Can translate complex model decisions to customers and team
Growth Mindset : Embraces the compounding returns of intelligence and continuous learning
Startup Mindset : Comfortable with ambiguity, rapid change, and wearing multiple hats
Work Ethic : Willing to put in the extra hours when needed to hit critical milestones
Team Player : Collaborative approach with low ego and high accountability
Bias for Action : Ships experiments fast, learns from failures, and iterates quickly
What We Offer
Opportunity to define
the future of physics AI from the ground up
Work on cutting‑edge problems
at the intersection of deep learning and physics simulation
Direct collaboration
with the founder & CEO and ability to influence company strategy
Competitive compensation
with significant equity upside
In‑person first culture
- 5 days a week in office with a team that values face‑to‑face collaboration
Access to world‑class investors and advisors
in the AI space
Cutting‑edge compute resources
for training foundation models
Benefits We provide great benefits, including:
Competitive compensation and equity.
Competitive health, dental, vision benefits paid by the company.
401(k) plan offering.
Flexible vacation.
Team Building & Fun Activities.
Great scope, ownership and impact.
AI tools stipend.
Monthly commute stipend.
Monthly wellness / fitness stipend.
Daily office lunch & dinner covered by the company.
Immigration support.
How We’re Different The credit belongs to the man who is actually in the arena, whose face is marred by dust and sweat and blood; who strives valiantly; who errs, who comes short again and again... who at the best, knows in the end the triumph of high achievement, and who at the worst, if he fails, at least fails while daring greatly – Teddy Roosevelt.
At our core, we believe in being “in the arena.” We are builders, problem solvers, and risk‑takers who show up every day ready to put in the work: to sweat, to struggle, and to push past our limits. We know that real progress comes with missteps, iteration, and resilience. We embrace that journey fully knowing that daring greatly is the only way to create something truly meaningful.
If you're ready to train the models that will revolutionize physics simulation, push the boundaries of what AI can learn, and deliver real impact, UniversalAGI is the place for you.
#J-18808-Ljbffr