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.
The Discovery team at Anthropic is organized around the north star goal of building an AI scientist – a system capable of solving the long-term reasoning challenges and basic capabilities necessary to push the scientific and technological frontier.
We're expanding our work on engineering and the applied sciences. Our goal is to contribute to training Claude to invent, design, and engineer across every discipline and scale, reliably and safely: new manufacturing and experimental techniques, processes, materials, devices, instruments, machines, energy systems, and factories. We want this work to accelerate science and technology and benefit people widely.
We're seeking exceptional researchers with deep expertise in one or more of these areas. In this role, you'll work at the intersection of frontier AI and your field, developing rigorous methods to measure and improve Claude's performance on complex engineering and scientific tasks. You'll collaborate closely with researchers, engineers, and partners across Anthropic and industry to build AI systems that can engage in all phases of research and development, and to test Claude's designs against real-world feedback.
Key responsibilities- Design and implement evaluation methodologies for assessing AI model capabilities relevant to engineering and applied science research and applications
- Design realistic tasks and training environments, and curate data from your field
- Develop and validate simulations and computational tools for your field, and calibrate them against real-world data
- Work with industry partners, manufacturers, and labs to build and test what Claude invents, designs, and engineers, and bring that real-world feedback into evaluation and training
- Think through how Claude's engineering capabilities can permeate the real world: where they can have the most impact across industry and research, and what it takes to get them adopted
- Advise the team on which problems, tools, and workflows matter most in your field
- Collaborate with domain experts and partners to establish benchmarks and gather high-quality data for training
- Translate between engineering domain knowledge and machine learning objectives
- Deep expertise and hands-on R&D or industry experience in an engineering or applied science discipline, such as mechanical, electrical, materials, chemical, aerospace, structural, industrial, or manufacturing engineering; applied physics; computational simulation
- Experience with AI or machine learning, whether evaluating and training AI systems or applying these tools in your field
- Track record of taking work from concept through to a working result, such as a product, process, device, experiment, or tool
- Hands-on proficiency with the computational design, analysis, or simulation tools used in your field
- Basic programming proficiency, for example in Python
- Ability to navigate ambiguity and develop solutions in a rapidly evolving research environment
- Ability to work independently while collaborating closely with cross-functional teams, and to explain your field clearly to people outside it
- Commitment to using AI to accelerate engineering and scientific progress while maintaining high ethical standards
- 5+ years of hands-on R&D or industry experience in an engineering or applied science discipline
- Ph.D. in an engineering or applied science field, or equivalent industry experience
- Industry experience in product design and manufacturing, or in developing engineering or scientific simulation software
- Breadth across more than one discipline, or across several stages of the path from design to production
- Familiarity with modern AI model training methods
- Track record of building high-fidelity solvers or simulation engines that were validated against real-world data and used in practice
- Experience working with manufacturers, vendors, contract research organizations, or academic labs
- Experience managing data pipelines and working with large-scale technical datasets
- Publications, patents, or contributions to open-source scientific or engineering software
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
- Deep expertise and hands-on R&D or industry experience in an engineering or applied science discipline
- Experience with artificial intelligence or machine learning
- Track record of taking work from concept through to a working result, such as a product, process, device, experiment, or tool
- Hands-on proficiency with computational design, analysis, or simulation tools used in the relevant field
- Basic programming proficiency, such as Python
- Ability to navigate ambiguity and develop solutions in a rapidly evolving research environment
- Ability to work independently, collaborate cross-functionally, and explain the field to nonexperts
- Commitment to using AI to accelerate engineering and scientific progress while maintaining high ethical standards
- Bachelor’s degree or equivalent combination of education, training, and experience
- Field of study relevant to the role through coursework, training, or professional experience
- Five or more years of hands-on R&D or industry experience in an engineering or applied science discipline
- Ph.D. in an engineering or applied science field, or equivalent industry experience
- Industry experience in product design and manufacturing, or engineering or scientific simulation software development
- Breadth across multiple disciplines or several stages from design to production
- Familiarity with modern AI model training methods
- Experience building high-fidelity solvers or simulation engines validated against real-world data and used in practice
- Experience working with manufacturers, vendors, contract research organizations, or academic labs
- Experience managing data pipelines and large-scale technical datasets
- Publications, patents, or contributions to open-source scientific or engineering software
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.
-
Equity Value & Accessibility — Equity grants are described as substantial with strong upside potential, alongside an option to match equity donations at a 1:1 ratio up to part of the grant. Feedback suggests this equity component is a major draw for top technical and research talent.
-
Healthcare Strength — Medical, dental, and vision coverage for employees and dependents is complemented by mental health resources. Feedback suggests the health package is comprehensive and competitive.
-
Parental & Family Support — Policies feature generous paid parental leave and inclusive fertility and family-building benefits. Feedback suggests these family supports are a standout part of the package.
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.
.png){kind=logo}
