Machine Learning Cloud Infrastructure Engineer
UniversalAGI
- United States
- United States
À propos
Machine Learning Cloud 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 Machine Learning Cloud 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.
Compétences linguistiques
- English
Avis aux utilisateurs
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