Research Engineer

As a Research Engineer, you will build large-scale ML systems from scratch, design and run scientific experiments, scale and improve the efficiency of our foundation models, and develop our infrastructure and dev tooling. You will have the opportunity to be credited as an author in submissions to peer-reviewed venues (e.g. NeurIPS, ICLR, JMLR).

You’re passionate about code that is not only performant, readable and maintainable, but is also well-motivated and plays a central role in solving fun research and engineering challenges that ultimately advance beneficial AI.

How we are different

• Our research agenda: the physics of intelligence. Our foundational AI research revolves around the idea of rigorously applying the scientific method to large-scale AI experiments in order to arrive at an understanding of AI comparable to that which was achieved for physics in the 20th century.
• Our business model: making safety profitable. We believe that advanced AI systems that are reliable, robust, and transparent are highly demanded by both investors and consumers for their real-world application, providing us with a competitive advantage in our solutions and insights at both the the technical and strategy level.

Requirements

We are a rapidly growing and flexible team. We are looking for highly cross-functional individuals that can quickly adapt to a variety of roles and take on responsibility, learn virtually any skill, and are excited to shape our research and engineering agenda, commercialization and communication strategies, and organizational culture.

We don't have boundaries between research and engineering, and expect our team to contribute to both as needed. Research Engineers are more likely to have a competitive advantage in SWE while being fascinated by science and ML research. In particular, you might be a good fit if you:

• Can write excellent Python code
• Are extremely results-oriented
• Pick up slack, even if it goes outside your job description
• Are passionate about machine learning science and research
• Care deeply about the broader social impact of your work

Strong candidates might also have experience with some of the following areas, or feel confident that they can quickly pick them up through on-the-job learning:

• Training and using large models (>1 billion parameters)
• Transformer architecture
• Hardware accelerators (GPU / TPU), CUDA / CuDNN
• Kubernetes, PyTorch, linux internals
• Distributed training
• Reinforcement learning and reward learning
• Fine-tuning foundation models for domain-specific applications

Representative projects are:

• Extracting a >100GB dataset from publicly available sites (e.g. Wikipedia) and transforming it into a suitable and easily-consumable format
• Scaling distributed training jobs of large language models (LLMs) to hundreds of GPUs using technologies like NVLink and GPUDirect RDMA
• Creating interactive visualizations of model capabilities, vulnerabilities, and failure modes, for a wide variety of audiences, including SWEs, scientists, or the media

Benefits

• Expected base salary range: $60k - $120k
• Equity: we aim to offer very competitive equity compensation through a mixture of stock and options. We indicate equity amounts with the job offer.
• Allowance for education, home office improvements, and wellness: $10k/year
• Fully remote work, with the option of moving in 6 months to one of our locations in London, San Francisco, or Madrid.

This information is based on our good faith estimate at the time of publication and may be modified in the future. The level of pay within the range will depend on factors such as past experience and performance on our interviews or in a work trial.