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- Tasks: Build and optimize large-scale ML systems while improving code and infrastructure.
- Company: Join a cutting-edge team focused on safe and trustworthy AI solutions.
- Benefits: Enjoy flexible work options, collaborative culture, and opportunities for impactful projects.
- Why this job: Dive into exciting research, enhance your coding skills, and make a difference in society.
- Qualifications: Significant software engineering experience and a passion for machine learning research required.
- Other info: Collaborative environment with pair programming and a focus on societal impact.
The predicted salary is between 43200 - 72000 £ per year.
You want to build large scale ML systems from the ground up. You care about making safe, steerable, trustworthy systems. As a Research Engineer, you’ll touch all parts of our code and infrastructure, whether that’s making the cluster more reliable for our big jobs, improving throughput and efficiency, running and designing scientific experiments, or improving our dev tooling. You’re excited to write code when you understand the research context and more broadly why it’s important. You may be a good fit if you: Have significant software engineering experience Are results-oriented, with a bias towards flexibility and impact Pick up slack, even if it goes outside your job description Enjoy pair programming (we love to pair!) Want to learn more about machine learning research Care about the societal impacts of your work Strong candidates may also have experience with: High performance, large-scale ML systems GPUs, Kubernetes, Pytorch, or OS internals Language modeling with transformers Reinforcement learning Large-scale ETL Representative projects: Optimizing the throughput of a new attention mechanism Comparing the compute efficiency of two Transformer variants Making a Wikipedia dataset in a format models can easily consume Scaling a distributed training job to thousands of GPUs Writing a design doc for fault tolerance strategies Creating an interactive visualization of attention between tokens in a language model #J-18808-Ljbffr
Research Engineer / Research Scientist, Multimodal employer: Anthropic Limited
Contact Detail:
Anthropic Limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Engineer / Research Scientist, Multimodal
✨Tip Number 1
Familiarize yourself with the latest advancements in machine learning, especially in areas like reinforcement learning and language modeling with transformers. This knowledge will not only help you understand the research context better but also demonstrate your passion for the field during discussions.
✨Tip Number 2
Engage with the community by contributing to open-source projects related to high-performance ML systems or tools like PyTorch and Kubernetes. This hands-on experience will showcase your software engineering skills and your ability to work collaboratively.
✨Tip Number 3
Prepare to discuss specific projects you've worked on that align with the responsibilities of the role, such as optimizing throughput or scaling distributed training jobs. Highlighting these experiences will show your results-oriented mindset and technical expertise.
✨Tip Number 4
Be ready to share your thoughts on the societal impacts of machine learning technologies. Understanding the ethical implications of your work will resonate well with our values and demonstrate your commitment to building trustworthy systems.
We think you need these skills to ace Research Engineer / Research Scientist, Multimodal
Some tips for your application 🫡
Understand the Role: Make sure you fully understand the responsibilities and requirements of the Research Engineer / Research Scientist position. Highlight your software engineering experience and any relevant projects that demonstrate your ability to build large-scale ML systems.
Tailor Your CV: Customize your CV to emphasize your experience with high-performance ML systems, GPUs, Kubernetes, and Pytorch. Include specific examples of projects where you've improved throughput, efficiency, or reliability in code and infrastructure.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for machine learning research and your understanding of its societal impacts. Mention your excitement for pair programming and your results-oriented mindset, providing examples of how you've demonstrated these qualities in past roles.
Highlight Relevant Projects: In your application, be sure to detail representative projects that align with the job description, such as optimizing attention mechanisms or scaling distributed training jobs. This will help illustrate your hands-on experience and technical skills.
How to prepare for a job interview at Anthropic Limited
✨Show Your Passion for Machine Learning
Make sure to express your enthusiasm for machine learning research during the interview. Discuss any personal projects or experiences that highlight your interest and understanding of the field, especially in relation to large-scale ML systems.
✨Demonstrate Your Software Engineering Skills
Be prepared to discuss your software engineering experience in detail. Highlight specific projects where you improved system reliability or efficiency, and be ready to explain your thought process and the impact of your contributions.
✨Emphasize Collaboration and Flexibility
Since the role involves pair programming and picking up slack, share examples of how you've successfully collaborated with others in past projects. Show that you're adaptable and willing to take on tasks outside your usual responsibilities.
✨Discuss Societal Impacts
Given the importance of societal impacts in this role, be prepared to talk about how your work in machine learning can affect society. Share your thoughts on ethical considerations and how you ensure your systems are trustworthy and safe.
