About Astera

Astera (astera.org) is a private foundation on a mission to steer science and technology toward an abundant future for all. We believe the coming years will bring an era of unprecedented scientific and technological advancement as exponential progress in AI converges with central advances in other fields to dramatically accelerate innovation. This inflection point provides an unparalleled opportunity to fundamentally rethink the institutions, systems, and tools that drive scientific progress. Astera is uniquely positioned to shape the course of rapid scientific and technological innovation: we are willing to deploy our $2.5B endowment to take on experimental projects that might fail, and we support projects and people that other organizations won't or can't.

The Initiative

Join the 1cFE Initiative at Astera: a one-year expedition to ask what would need to be true to achieve sub-$0.01/kWh LCOE from fusion within 10 years. This is wildly ambitious. The field is making real progress on important milestones towards fusion reactors achieving breakeven and from there towards producing electricity competitive with existing sources. We go further: what would need to be true to drop the cost of electricity by an order-of-magnitude. If possible, ultra-low-cost electricity pushes humanity up the Kardashev scale and will positively transform civilization. Achieving sub-cent fusion–if it is indeed possible–within a decade is only plausible with significant AI acceleration across science, prototype engineering, and production deployment. To this end we have access to substantial compute resources via Voltage Park, a 24,000 H100 cluster.

How we work

• LCOE is the north star

• Risk first, then speed

• Open science: code, data, and notes are public, negative results are valuable

The role

Lead TEA development and analysis for the 1cFE initiative. Build, validate, and operate sub-system to plant-level techno-economic models, link physics parameters and operating conditions to LCOE, and run sensitivities that expose the cost drivers. Partner with physicists and engineers, evaluate alpha AI-for-science, AI-for-engineering tools that increase speed, reduce cost and unlock new capabilities.

What you will do

• Architect and own a transparent LCOE model linked to physics outputs and design variables

• Quantify CAPEX, OPEX, availability, duty factor, and learning curves; run global sensitivity and value-of-information analyses

• Define and compare plant configurations including balance of plant, thermal or direct conversion, maintenance, and supply chain assumptions

• Select high-leverage replication targets in TEA claims; reproduce, document assumptions, and release clean artifacts

• Build reduced-order or differentiable surrogates for fast design space exploration; integrate with Python notebooks and CI

• Evaluate and assess how alpha and horizon AI-for-science, AI-for-engineering tools from academia and industry could impact accuracy, speed, or cost of fusion systems development and deployment

• Maintain decision logs, datasets, and dashboards that make trade-offs legible to non-experts

Qualification and experience

• Degree in systems, mechanical, nuclear, or electrical engineering, or equivalent depth in techno-economics and energy systems

• Track record building decision-grade TEA models for complex systems; familiarity with DCF and LCOE mechanics

• Python fluency and testing discipline; capable with NumPy/Pandas; fluency with optimization and uncertainty methods

• Ability to translate physics outputs into cost drivers and plant architecture choices

• Nice to have: reliability engineering, maintenance and outage modeling, direct conversion or thermal cycle experience, cost data acquisition, OSS contributions

How you think and work

• Mission-driven, ambitious, frustrated with the status quo. You think bigger than your peers

• Systems-level optimizer. You instinctively think in terms of trade-offs, sensitivities, and system boundaries. You are just as interested in the cost of concrete and the efficiency of a heat exchanger as you are in plasma beta.

• Independence. Willingness to question explicit and implicit assumptions

• Thrive working in public and responding to critique

• Speed with rigor. You move fast, write tests, and publish clean code. Short loops, frequent releases, written decisions.

How to apply

• Submit your resume along with

  • A short note on why you want to join this initiative.

  • Links to relevant code or papers you have published; if you have no public repo, the deep dive will use your exercise repo

  • One fusion plant-level assumption you believe is underestimated as a cost driver and how you would interrogate it

  • One realistic, not-yet-existing AI-for-engineering capability that could materially improve fusion LCOE

• Optional: a small Python toy TEA comparing thermal vs direct EM capture, unit tests, and a short notebook

Process

30-min screen > 7-day paid replication exercise > technical deep dive on your exercise repo > references > decision.

Compensation Range: $150K - $190K