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UniversalAGI · 1 month ago

Deep Learning Researcher

San Francisco

Full-time

Onsite

Mid, Senior Level

3+ years exp

UniversalAGI is an AI startup based in San Francisco, building OpenAI for Physics. As a Deep Learning Researcher, you will architect and train foundation models to transform industries' approach to physics simulation and engineering design, working closely with the CEO and founding team.

Research

H1B Sponsored

Responsibilities

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

Qualification

Deep learning frameworksDistributed trainingPython programmingPhysics-informed learningLarge-scale datasetsModel optimizationDebugging modelsProblem-solvingCommunicationGrowth mindset

Required

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

Preferred

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