Founding Deep Learning Engineer at UniversalAGI

, , United States -

Full Time

Start Date

Immediate

Expiry Date

27 Feb, 26

Salary

0.0

Posted On

29 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Yes

Skills

Deep Learning, Physics Simulation, Model Architectures, Data Preprocessing, Training Pipelines, Distributed Training, Python, Machine Learning Libraries, Debugging, Problem Solving, Communication, Customer Interaction, Rapid Experimentation, Model Deployment, Physics-Informed Learning, Optimization Techniques

Industry

Research Services

Description

📍 San Francisco | Work Directly with CEO & Founding Team | Report to CEO | OpenAI for Physics | 🏢 5 Days Onsite Founding Deep Learning Engineer 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 Engineer, 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.

Responsibilities

As a founding Deep Learning Engineer, you'll architect and train foundation models for physics simulation and engineering design. You'll work directly with the CEO and founding team to push the boundaries of AI with physics data.