Founding ML Infrastructure Engineer

📍 San Francisco | Work Directly with CEO & founding team | Report to CEO | OpenAI for Physics | 🏢 5 Days Onsite

Founding ML Infrastructure 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 ML Infrastructure Engineer, you'll be in the arena from day one, building the backbone that powers AI for physics at scale. This is your chance to build and own the entire ML infrastructure stack, from finetuning, training pipelines to low-latency customer deployments that serve foundation models in production.

You'll work directly with the CEO and founding team to build infrastructure that can train on petabytes of simulation data, serve physics models with strict accuracy requirements, and deploy seamlessly into customer environments with enterprise security and compliance needs. You're coming up with new paradigms for how AI models integrate into industrial engineering workflows.

What You'll Do

• Build and scale fine tuning & training infrastructure for foundation models, distributed training across multi-GPU and multi-node clusters, optimizing for throughput, cost, and iteration speed

• Design and implement model serving systems with low latency, high reliability, and the ability to handle complex physics workloads in production

• Build fine-tuning pipelines that let customers adapt our foundation models to their specific use cases, data, and workflows without compromising model quality or security

• Build deployment serving infrastructure for on-premise and cloud environments, working through customer security requirements and compliance constraints

• Create robust data pipelines that can ingest, validate, and preprocess massive CFD datasets from diverse sources and formats

• Instrument everything: Build observability, monitoring, and debugging tools that give our team and customers full visibility into model performance, data quality, and system health

• Work directly with customers on deployment, integration, and scaling challenges, turning their infrastructure pain points into product improvements

• Move fast and ship: Take infrastructure from prototype to production in weeks, iterating based on real customer needs and research team feedback

This is a role for someone who's built ML systems that actually work in production, who understands both the research side and the operational reality, and is ready to solve some of humanity's hardest infrastructure problems.

Qualifications

• 3+ years of hands-on experience building and scaling ML infrastructure for fine tuning, training, serving, or deployment

• Deep experience with cloud platforms (AWS, GCP, Azure) and infrastructure-as-code (Terraform, Kubernetes, Docker)

• Deep expertise in distributed training frameworks (PyTorch Distributed, DeepSpeed, Ray, etc.) and multi-GPU/multi-node orchestration

• Strong foundation in ML serving: Experience building low-latency inference systems, model optimization, and production deployment

• Expert-level coding skills in Python and infrastructure tools, comfortable diving deep into ML frameworks and optimizing performance

• Understanding of ML workflows: Training pipelines, experiment tracking, model versioning, and the full lifecycle from research to production

• Strong communicator capable of bridging customers, engineers, and researchers, translating infrastructure constraints into product decisions

• Outstanding execution velocity: Ships fast, debugs quickly, and thrives in ambiguity

• Exceptional problem-solving ability: Willing to dive deep into unfamiliar systems and figure out what's actually broken

• Comfortable in high-intensity startup environments with evolving priorities and tight deadlines

Bonus Qualifications

• Computer Aided Engineer Software experience.

• Experience deploying ML in enterprise environments with strict security, compliance, and air-gapped requirements

• Built fine-tuning infrastructure for foundation models.

• Experience with model optimization techniques

• Deep understanding of GPU programming and performance optimization (CUDA, Triton, etc.)

• Experience with large-scale data engineering for ML, ETL pipelines, and data validation systems

• Built MLOps platforms or developer tools for ML teams

• Experience at high-growth AI startups (Seed to Series C) or leading AI labs

• Forward deployed experience working directly with customers on complex integrations

• Open-source contributions to ML infrastructure or training frameworks

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 cool tech

• 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 technical concepts 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

What We Offer

• Opportunity to shape the technical foundation of a rapidly growing foundational AI company.

• Work on cutting-edge industrial AI problems with immediate real-world impact.

• 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.

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 join the future of physics simulation, push creative boundaries, and deliver impact, UniversalAGI is the place for you.