Research Engineer, Machine Learning (Horizons)

Key Skills

Aartificial intelligence, Deep Learning, JAX framework, Machine learning techniques, Python Programming, PyTorch, TensorFlow

Job Description

As a Research Engineer on the Reinforcement Learning Fundamentals team, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models through fundamental research in reinforcement learning, improving reasoning abilities in areas such as code generation and mathematics, and exploring reinforcement learning for agentic / open-ended tasks.

Representative projects:

• Develop and implement novel reinforcement learning techniques to improve the performance and safety of large language models.
• Create tools and environments for models to interact with, enabling them to perform complex, open-ended tasks.
• Design and run experiments to enhance models' reasoning capabilities, particularly in code generation and mathematics

You may be a good fit if you:

• 5+ years of industry-related experience
• Are proficient in Python and have experience with deep learning frameworks such as PyTorch or Jax
• Have a strong software engineering background and are interested in working closely with researchers and other engineers
• Enjoy pair programming (we love to pair!)
• Care about code quality, testing, and performance
• Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems

Strong candidates may also:

• Have a strong background in machine learning, reinforcement learning, or high performance computing
• Have experience with virtualization and sandboxed code execution environments
• Have experience with Kubernetes
• Have contributed to open-source projects or published research papers in relevant fields

Candidates need not have:

• Formal certifications or education credentials
• Experience with LLMs or machine learning research before

Deadline to apply: None. Applications will be reviewed on a rolling basis.

The expected salary range for this position is:

Annual Salary:

£280,000 - £390,000 GBP

Logistics

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.

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.