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.
Anthropic is at the forefront of AI research, dedicated to developing safe, ethical, and powerful artificial intelligence. Our mission is to ensure that transformative AI systems are aligned with human interests. We are seeking a Research Engineer to join our Pre-training team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems.
Key ResponsibilitiesConduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development
Independently lead small research projects while collaborating with team members on larger initiatives
Design, run, and analyze scientific experiments to advance our understanding of large language models
Optimize and scale our training infrastructure to improve efficiency and reliability
Develop and improve dev tooling to enhance team productivity
Contribute to the entire stack, from low-level optimizations to high-level model design
Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field
Strong software engineering skills with a proven track record of building complex systems
Expertise in Python and experience with deep learning frameworks (PyTorch preferred)
Familiarity with large-scale machine learning, particularly in the context of language models
Ability to balance research goals with practical engineering constraints
Strong problem-solving skills and a results-oriented mindset
Excellent communication skills and ability to work in a collaborative environment
Care about the societal impacts of your work
Work on high-performance, large-scale ML systems
Familiarity with GPUs, Kubernetes, and OS internals
Experience with language modeling using transformer architectures
Knowledge of reinforcement learning techniques
Background in large-scale ETL processes
Have significant software engineering experience
Are results-oriented with a bias towards flexibility and impact
Willingly take on tasks outside your job description to support the team
Enjoy pair programming and collaborative work
Are eager to learn more about machine learning research
Are enthusiastic to work at an organization that functions as a single, cohesive team pursuing large-scale AI research projects
Are working to align state of the art models with human values and preferences, understand and interpret deep neural networks, or develop new models to support these areas of research
View research and engineering as two sides of the same coin, and seek to understand all aspects of our research program as well as possible, to maximize the impact of your insights
Have ambitious goals for AI safety and general progress in the next few years, and you’re working to create the best outcomes over the long-term.
Optimizing the throughput of novel attention mechanisms
Comparing compute efficiency of different Transformer variants
Preparing large-scale datasets for efficient model consumption
Scaling distributed training jobs to thousands of GPUs
Designing fault tolerance strategies for our training infrastructure
Creating interactive visualizations of model internals, such as attention patterns
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.
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