Anthropic · San Francisco, CA ·
Lead the engineering practice for GTM systems at Anthropic, designing CI/CD pipelines, AI-assisted workflows, and scalable platforms that process sales transactions and organizational data.
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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.
GTM Systems builds and runs the platform that Sales and contracting operate on. These are production systems that process real transactions and produce the data the rest of the company depends on, and the team has grown into a scope where the engineering practices need to grow with it. The platform-developer model got us here. The next stage is a team that operates as software engineers on top of it.
We are hiring a technical lead to build that next stage. You will design and stand up the engineering motions the team runs on, from pull requests and CI/CD through automated UAT, and you will set the technical bar by working alongside the team rather than legislating from the side. Anthropic is also an unusual place to do this work: our own frontier models are part of your toolkit, the team already uses them daily, and a real part of this role is turning that individual usage into a coordinated engineering practice.
Lead architecture reviews for GTM systems that process lead-to-cash transactions and generate the data points the organization runs on, including changes originating in other teams that land on our platform
Architect and mature the engineering pipeline the team develops against: pull request workflow, CI/CD, automated UAT, and the deploy path from source control to production
Establish shared AI-assisted development standards for the team, converting existing individual usage into consistent practice that holds up under review
Work with engineering teams outside GTM on shared architectural problems and common tooling, where the right answer is a platform decision rather than a local one
Build and shape the agentic tooling running on top of our platform, and set the pattern other engineers follow when they build the next one
Trace the dependency chain from upstream data and downstream quoting and provisioning systems, and make sure design decisions account for what they break
Partner with the platform developers on the team as engineering practice deepens, expanding what the team can safely ship
Have strong software engineering fundamentals: version control discipline, testing, code review, deployment automation, and a bias toward simple designs
Have built the delivery pipeline for a team, not just worked within one, and can describe the sequencing choices you made
Have led a team through a step change in engineering practice as its scope grew, including what you did when adoption slowed
Work fluently with AI coding tools and agentic development workflows, and have views on how a team should standardize around them
Can run a technical discussion across teams that do not report to you and leave it with a decision rather than a follow-up meeting
Reason about systems you do not own well enough to predict how your changes affect them
Prefer influence through demonstrated work over positional authority
Have worked on business-critical enterprise platforms where financial correctness and auditability mattered
Have experience with Salesforce or comparable enterprise platforms, including their metadata and deployment models
Have built tooling or agents on top of an enterprise system of record
Have operated in a company under audit or compliance constraints
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.
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 directly for confirmed position openings.
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.
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.
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