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Sobre esta vaga de Senior Software Engineer na Swift Solar

Swift Solar · Presencial · San Carlos, California, United States

About the company

At Swift Solar, our mission is to unlock the full potential of solar energy. We believe solar can provide affordable, reliable, resilient, clean energy to virtually anyone, anywhere. But while today’s solar technology is good, it’s not good enough.

Swift Solar’s breakthrough perovskite-silicon tandem technology will drive solar power to commanding new heights. Our tandem cells stack a perovskite layer on top of a heterojunction (HJT) silicon cell, delivering dramatically higher efficiency than traditional silicon alone. In 2026, we acquired Meyer Burger’s HJT intellectual property and manufacturing assets, giving us the world’s most extensive HJT patent portfolio and a clear path to gigawatt-scale domestic production.

This vertically integrated approach means we can design, manufacture, and scale next-generation solar solutions entirely in the United States—from silicon foundations to perovskite innovations. Our technology enables applications ranging from space solar to utility-scale power generation, creating American-made solutions for a world facing growing energy challenges.

Swift Solar is on a journey to bring revolutionary clean-energy technology to market. We’re in it for the long haul, with support from world-class investors. Join our extraordinary team building the cutting-edge solar technology that will power the future.

A day in the life

You'll build AI products that assist scientists and accelerate our technical progress towards next-generation solar solutions. Every experiment we run costs tool time, materials, and scientists' attention, so choosing what to run next is one of the biggest levers on how fast our cells improve. You'll build the AI that helps our scientists make those choices. Our systems read scientific literature and query our lab and manufacturing data, then propose experiment plans that an automated optimizer uses to execute them.  You will lead development of the planning and reasoning layer, which takes a research objective as input and uses a multi-agent system to guide our scientists and reach goals faster through the best selected experiment campaigns. Additionally, we need to measure what works and how frontier models and our own agents perform on problems our team has already worked through, so we can improve the system over time. 

This is also a place to experiment. When a promising new paper, model, or agent design comes out in AI for science, you can try it here on real problems instead of benchmarks someone else chose. You'll work with scientists who have spent years on perovskites, who know which questions matter and can tell you quickly whether an agent's answer makes sense. Because we develop the process and make the devices ourselves, the data, tools, and people who understand them live in the same company, so an idea can go from your screen to a measured result without waiting on anyone outside.

Most of this is greenfield work, and speed matters. You'll get rough versions in front of scientists quickly, watch how they use them, and turn the ideas that hold up into production systems people rely on every day. Knowing when a prototype is ready to harden, and doing it without losing momentum, is a big part of the job.

Alongside the agent work, you will:

  • Hold AI-written code to a high bar. Nearly all of our code is written with AI coding tools, so critically reviewing the output and keeping the codebase documented and tested matters.
  • Help keep our existing data systems running, including the pipeline that brings instrument data into our database.
  • Review code across the team, and help hire and mentor new engineers as the team grows.
  • Write about what we learn for our public whitepaper series and, when the work holds up, publish it in papers and present it at conferences.

You might be a good fit if you... 

  • Have 5+ years of professional software engineering experience, including systems that real users depend on
  • Have built full-stack web applications, with a Python backend and a TypeScript/JavaScript frontend (React, Vue, Svelte, or similar)
  • Have built applications on frontier LLMs through their APIs, such as agents, multi-step orchestration (e.g., LangGraph), or systems where models call tools
  • Can take an idea from a quick prototype to a production system that people depend on
  • Use AI coding tools like Claude Code every day, and can tell when what they produce is wrong
  • Have a CS degree or a comparable record of open-source or portfolio work
  • Write documented, tested code and are comfortable working in a shared codebase with git
  • Can explain technical tradeoffs to scientists who aren't software engineers
  • Take initiative and work independently
  • Are coachable and comfortable when priorities change quickly
  • Are kind, empathetic, conscientious, and motivated to make an impact on climate change

A strong candidate might bring one or more of the following...

  • Experience with multi-agent systems, especially ranking, review, or memory between runs
  • Experience building evaluations or benchmarks for LLM agents
  • Experience designing complex software architecture and leading projects end-to-end without much oversight
  • Familiarity with Bayesian optimization, surrogate models, fine-tuning of open-weights models, or reinforcement-learning environments
  • Experience analyzing and plotting scientific data
  • An interest in science, shown through conferences, side projects, or papers (a science background is not required)
  • Experience getting skeptical users to adopt internal tools
  • Experience with SQL or database administration, AWS, Azure, or GCP, or CI/CD
  • Experience working at a startup 

The logistics

  • Compensation: Competitive package including salary, equity, and benefits
    • Expected Pay Range: $150,000 - $200,000 per year
    • The actual offer, reflecting total compensation and benefits, will be determined by factors such as relevant experience, skills, and other qualifications 
  • Ideal start date: November - December 2026
  • Job type: Full-time
  • Location: San Carlos, CA (Swift Solar HQ), with some remote days possible

What you’ll love about Swift

  • Work with purpose: Join our mission to unlock the full potential of solar energy and make a global impact. 
  • Lead with innovation: Be part of an extraordinary team building cutting-edge solar technology - growth mindset; culture
  • Thrive with benefits: Have 100% of your monthly premiums for HMO / PPO group healthcare plan options and 75% of your dental and vision insurance premiums covered by us. Employees also benefit from 401(k) matching, clean commuter benefits, and meaningful equity. 
  • Invest in your career: You’ll grow your skills, your network, and your perspective at Swift. We’ll support you in attending conferences and taking online courses, as long as you share your new insights with the team.

We recognize that candidates from underrepresented backgrounds often hesitate to apply if they don’t meet all the criteria. If you’re excited about this role and believe you can contribute to our team, we encourage you to apply—even if you don’t meet every qualification listed. We're eager to meet people from all backgrounds, and your unique skills and experiences could be exactly what we need.

If you believe in our mission but this isn’t the right job for you, please explore other career opportunities on our website and email us at [email protected] with any questions. If you have a friend who would be a perfect fit, share this link with them. Thanks!

Swift is an equal opportunity employer. We value an inclusive work environment and welcome team members of all backgrounds and perspectives.



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Como este salário de Software Engineer se compara

Esta vaga paga $175,000/yr — em linha com da faixa típica para vagas de Software Engineer.

$98,823 a mediana $175,000 $260,000

Faixa típica $134,045–$217,500/yr, com base em 10,842 vagas de Software Engineer comparáveis na JobsRadar (pagamento anualizado em USD). Ver insights salariais de Software Engineer →

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