Job description

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role At Anthropic, we're building AI systems that are safe, beneficial, and transformative. Our mission is to develop AI that benefits humanity, and we believe the most powerful capabilities emerge when we thoughtfully bridge the gap between research breakthroughs and real-world applications. Frontier AI runs on datacenters, and the world is in the middle of the largest infrastructure buildout in a generation. The binding constraint on progress is increasingly everything around the chips: land, power, permits, equipment, and the design and construction of the buildings themselves. Bringing a single site online involves hundreds of people, thousands of components, and decisions that move enormous amounts of capital. Much of that work still runs on spreadsheets and handoffs. At Anthropic, we're building software and Claude-powered tools to help Anthropic scale out its compute, and we think there's an enormous opportunity to rethink how that work gets done. It's early, it's already having a real impact, and the engineers who join now will shape how one of the most consequential buildouts in the world gets done. We're looking for strong, versatile software engineers who love building for people doing real work in the physical world. You'll embed with the teams that source sites, design buildings, procure equipment, and manage construction, learn their work deeply, and build the tools and systems they rely on every day. Datacenter experience is a big plus, but what matters most is that you can earn the trust of expert operators, model a messy domain cleanly, and move fast. Responsibilities Build the core systems that track a datacenter's lifecycle (sites, designs, bills of materials, equipment, and construction progress) in one trusted, permissioned source of truth Embed with datacenter, energy, and supply chain teams to understand how the work actually gets done, find the critical path, and build the tools they reach for first Bring order to messy data by migrating and reconciling spreadsheets and legacy trackers, and integrate with the industry's tools, like construction management, scheduling, and engineering systems Design for trust: fine-grained permissions, auditability, and data quality for information that steers major investment decisions Ship early, iterate with users, and cut anything that doesn't help bring compute online sooner Partner with research to understand new model capabilities and share where they fall short in engineering-heavy, physical-world domains You may be a good fit if you Have 8+ years of experience building software, with strong full-stack skills and solid grounding in data modeling, APIs, and databases Have built software that teams in a physical-world industry depend on, such as datacenters, construction, energy, manufacturing, supply chain, or logistics Have a track record of zero-to-one work in startup or startup-like environments Are deeply user-centric: you learn the domain from the people doing the work and validate with them before over-investing Bring high agency and good judgment about what matters, and hold strong opinions loosely Communicate clearly across engineering, operations, and research, and care about the societal impacts of your work Strong candidates may also have Experience in datacenters (at a hyperscaler, colocation provider, operator, utility, or engineering firm), or in adjacent fields like construction, logistics, or legal Experience with the domain's tooling, such as construction management (Procore, Autodesk Construction Cloud), scheduling (Primavera P6), PLM/BOM, ERP and procurement, CAD/BIM, DCIM, or power system studies A background in electrical, mechanical, or civil engineering, energy systems, or supply chain alongside a software career Experience building products with large language models Candidates need not have 100% of the skills listed above Formal certifications or education credentials Direct machine learning or AI research experience Deadline to apply: None. Applications will be reviewed on a rolling basis. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Requirements

Must-haves

Build the core systems that track a datacenter's lifecycle (sites, designs, bills of materials, equipment, and construction progress) in one trusted, permissioned source of truth
Embed with datacenter, energy, and supply chain teams to understand how the work actually gets done, find the critical path, and build the tools they reach for first
Bring order to messy data by migrating and reconciling spreadsheets and legacy trackers, and integrate with the industry's tools, like construction management, scheduling, and engineering systems
Design for trust: fine-grained permissions, auditability, and data quality for information that steers major investment decisions
Ship early, iterate with users, and cut anything that doesn't help bring compute online sooner
Partner with research to understand new model capabilities and share where they fall short in engineering-heavy, physical-world domains
You may be a good fit if you
Have 8+ years of experience building software, with strong full-stack skills and solid grounding in data modeling, APIs, and databases
Have built software that teams in a physical-world industry depend on, such as datacenters, construction, energy, manufacturing, supply chain, or logistics
Have a track record of zero-to-one work in startup or startup-like environments
Are deeply user-centric: you learn the domain from the people doing the work and validate with them before over-investing
Bring high agency and good judgment about what matters, and hold strong opinions loosely
Communicate clearly across engineering, operations, and research, and care about the societal impacts of your work
Strong candidates may also have
Experience in datacenters (at a hyperscaler, colocation provider, operator, utility, or engineering firm), or in adjacent fields like construction, logistics, or legal
Experience with the domain's tooling, such as construction management (Procore, Autodesk Construction Cloud), scheduling (Primavera P6), PLM/BOM, ERP and procurement, CAD/BIM, DCIM, or power system studies
A background in electrical, mechanical, or civil engineering, energy systems, or supply chain alongside a software career
Experience building products with large language models
Candidates need not have
100% of the skills listed above
Formal certifications or education credentials
Direct machine learning or AI research experience
Deadline to apply: None. Applications will be reviewed on a rolling basis. The annual compensation range for this role is listed below.
Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learning

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Parental Leave