Level

Anthropic

Research Engineer/Research Scientist, Pre-training

Anthropic is hiring a Research Engineer/Research Scientist, Pre-training in San Francisco, Seattle and New York, United States. It pays $350k-$850k a year and Level rates it ; you can apply on Anthropic.

Level's read on this job

  • Builds AI ●●●● is how Level rates this research scientist role: the job is building AI systems.
  • Pay on this posting starts 83% higher than the typical research scientist posting on Level.
  • Some of the work can be done remotely, according to the posting.
  • The posting mentions visa sponsorship.
  • The posting mentions PyTorch, AI safety and AI research among its tools and skills.

AI in this role

pytorch
ai-safetyai-research

About Anthropic

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 Responsibilities:

  • Conduct 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

Qualifications:

  • 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

Preferred Experience:

  • 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

You'll thrive in this role if you:

  • 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.

Sample Projects:

  • 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

At Anthropic, we are committed to fostering a diverse and inclusive workplace. We strongly encourage applications from candidates of all backgrounds, including those from underrepresented groups in tech.

If you're excited about pushing the boundaries of AI while prioritizing safety and ethics, we want to hear from you!

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.

Annual Salary:$350,000—$850,000 USD

Logistics

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.

How we're different

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.

How we rate this

Research Engineer/Research Scientist, Pre-training at Anthropic rates 96 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI SafetyAI ResearchPyTorch

Also common in research scientist jobs at this level

Pay

This job starts at
$350k
Typical research scientist job
$192k

This posting starts 83% above the typical research scientist job. See research scientist jobs

Questions you could be asked

  1. How do you think about the risk of an AI system in this kind of role failing silently?
  2. Tell me about a research question you investigated. What did you find?
  3. What are the limits of PyTorch that you've run into, and how did you work around them?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: AI Safety, AI Research, and PyTorch. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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