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.
As the Research Operations & Strategy Lead for Coding & Cybersecurity Data, you'll build and scale data operations that advance Claude's coding and cybersecurity capabilities. You'll partner with research teams to design and execute data strategies, manage vendor relationships, and own the entire data pipeline from requirements to production. This is a zero-to-one role requiring technical depth to understand what makes high-quality training data, but your focus will be on strategy and execution rather than hands-on engineering. Think technical founder who evolved from writing code to building the business.
About the Impact:The data strategies and operations you build will directly determine how well our models can write code, understand software systems, and identify security vulnerabilities. You'll work with world-class researchers on frontier AI capabilities while building the operational infrastructure to scale these efforts.
We're looking for someone who gets excited about the challenge of scaling quality - someone who can think strategically about data needs, build the right partnerships, and execute flawlessly. If you thrive at the intersection of technical depth and operational excellence, we'd love to hear from you.
Responsibilities:- Develop and execute data strategies for coding capabilities, cybersecurity evaluations, and agentic AI research
- Partner with research leaders to translate technical requirements into operational frameworks
- Build data collection and evaluation systems through internal tools, vendor partnerships, and new approaches
- Identify, evaluate, and manage specialized contractors and vendors for technical data collection
- Implement quality control processes to ensure data meets training requirements
- Manage multiple complex projects simultaneously, balancing technical needs with delivery timelines
- Track metrics and communicate progress to stakeholders
- Have 3+ years in technical operations, product management, or entrepreneurial experience building from zero to scale
- Have strong technical foundations - proficiency in Python and understanding of ML workflows and evaluation frameworks
- Have strong communication skills and can effectively engage with both technical and non-technical stakeholders, both internal and external parties
- Are familiar with how LLMs work and could describe how models like Claude are trained
- Are highly organized and can manage multiple parallel workstreams effectively
- Have a high threshold for navigating ambiguity and can balance setting strategic priorities with rapid, high-quality execution
- Thrive in fast-paced research environments with shifting priorities and novel technical challenges
- Are passionate about AI safety and understand the critical importance of high-quality data in building beneficial AI systems
- Experience at companies training AI models, agents, or creating AI training data, evaluations, or environments
- Knowledge of AI safety research methodologies and evaluation frameworks
- Experience with RLHF or similar human-in-the-loop training methods
- Domain expertise in software engineering or cybersecurity
- Track record of building and scaling operations teams
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.
Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
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.
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
Anthropic Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Anthropic and has not been reviewed or approved by Anthropic.
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Strong & Reliable Incentives — Pay is positioned as top-of-market for many technical roles through a mix of high base pay, equity, and occasional bonuses/signing incentives. Benefits like substantial monthly stipends and employer-paid protections further strengthen perceived total rewards.
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Healthcare Strength — Healthcare is described as comprehensive across medical, dental, and vision, with additional mental-health support. Coverage is framed as robust for employees and dependents, which can materially increase the value of the overall package.
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Parental & Family Support — Paid parental leave is described as notably generous, alongside fertility coverage and other family-oriented supports. These elements broaden the rewards package beyond cash compensation and can improve retention for caregivers.
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What We Do
Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.
