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.
Agentic systems are becoming an increasingly important part of how AI is deployed. Over the last year, we’ve seen rapid adoption of Claude-powered agentic systems in spaces like coding, research, customer support, network security, and more. We believe this is just the beginning, and we expect Claude to be handling much more complex tasks end-to-end or in cooperation with a human user as time goes on. We have a team striving to make Claude an even more effective agent over longer time horizon tasks, and coordinate with groups of other agents at many different scales to accomplish large tasks. This team endeavors to maximize agent performance by solving challenges at whatever level is needed, whether it’s novel harness design, improved agent affordances and infrastructure, or finetuning.
Given that this is a nascent field, we ask that you share with us a project built on LLMs that showcases your skill at getting them to do complex tasks. Here are some example projects of interest: design of complex agents, quantitative experiments with prompting, constructing model benchmarks, synthetic data generation, or model finetuning. There is no preferred task; we just want to see what you can build. It’s fine if several people worked on it; simply share what part of it was your contribution. You can also include a short description of the process you used or any roadblocks you hit and how to deal with them, but this is not a requirement.
Responsibilities:- Ideate, develop, and compare the performance of different agent harnesses (eg memory, context compression, communication architectures for agents)
- Design and implement rigorous quantitative benchmarks for large scale agentic tasks
- Assist with automated evaluation of Claude models and prompts across the training and product lifecycle
- Work with our product org to find solutions to our most vexing challenges applying agents to our products
- Help create and optimize data mixes for model training that maximize Claude’s performance or ease of use on agentic tasks
- Have experience developing complex agentic systems using LLMs
- Have significant software engineering and ML experience
- Have spent time prompting and/or building products with language models
- Have good communication skills and an interest in working with other researchers on difficult tasks
- Have a passion for making powerful technology safe and societally beneficial
- Stay up-to-date and informed by taking an active interest in emerging research and industry trends.
- Enjoy pair programming (we love to pair!)
- Large-scale RL on language models
- Multi-agent systems
- Design and build a novel agent harness that outperforms existing agents on coding or knowledge work benchmarks
- Design and build agent affordances that unlock new capabilities for internal use and deployed products
- Design and build a novel eval that measures how many agents interact in groups to solve problems
- Build a scaled model evaluation framework driven by model-based evaluation techniques.
- Build the prompting and model orchestration for a production application backed by a language model
- Finetune Claude to maximize its performance using a particular set of agent tools or harness
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 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.
Skills Required
- Bachelor's degree or equivalent combination of education, training, and/or experience
- Field of study relevant to the role (demonstrated via coursework, training, or professional experience)
- Minimum years of experience consistent with internal job level requirements
- Provide a project built on LLMs demonstrating ability to get models to perform complex tasks
- Experience developing complex agentic systems using large language models
- Significant software engineering and machine learning experience
- Experience prompting and/or building products with language models
- Design and implement rigorous quantitative benchmarks and automated evaluation for model/agent performance
- Strong communication and collaboration skills; willingness to pair program
- Experience with large-scale reinforcement learning on language models
- Experience with multi-agent systems
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.
Anthropic Insights
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.
