At a Glance
- Tasks: Contribute to software and infrastructure for large-scale ML systems and run scientific experiments.
- Company: Join Anthropic, a forward-thinking tech company focused on impactful engineering.
- Benefits: Flexible work environment, competitive salary, and opportunities for professional growth.
- Other info: Dynamic projects with excellent career advancement opportunities.
- Why this job: Make a real difference in machine learning while collaborating with top experts.
- Qualifications: Experience in software engineering and a passion for collaborative work.
The predicted salary is between 60000 - 80000 £ per year.
As a Software Engineer at Anthropic, you'll touch all parts of our code and infrastructure, whether that’s making the cluster more reliable for big jobs, improving throughput and efficiency, running and designing scientific experiments, or improving our dev tooling. You are excited to write code when you understand the research context and why it’s important.
Responsibilities
- Contribute across the software stack and infrastructure to support large-scale ML systems.
- Improve reliability, throughput, and efficiency of compute resources and pipelines.
- Design and run scientific experiments; participate in building and refining development tooling.
- Collaborate with researchers, policy experts, and other engineers to align software with research goals.
- Engage in pair programming and contribute to a collaborative engineering culture.
Qualifications
- Significant software engineering experience.
- Results-oriented with a bias toward flexibility and impact.
- Ability to pick up tasks outside the explicit job description when needed.
- Enjoy pair programming and collaborative work.
- Interest in machine learning research and societal impacts of engineering work.
Preferred / Strongly Desired Experience
- Experience with high performance, large-scale ML systems.
- Familiarity with GPUs, Kubernetes, PyTorch, or OS internals.
- Experience with language modeling, transformers, or reinforcement learning.
- Experience with large-scale ETL and ML infrastructure (GPUs, TPUs, Trainium) and supporting networking infrastructures (e.g., NCCL).
- Low-level systems experience (e.g., Linux kernel tuning, eBPF).
Representative Projects
- Optimizing the throughput of a new attention mechanism.
- Comparing the compute efficiency of two Transformer variants.
- Creating a Wikipedia dataset in a format models can easily consume.
- Scaling a distributed training job to thousands of GPUs.
- Writing a design document for fault tolerance strategies.
- Creating an interactive visualization of attention between tokens in a language model.
Notes
Deadline to apply: None. Applications will be reviewed on a rolling basis.
Software Engineer employer: Anthropic
At Anthropic, we pride ourselves on fostering a dynamic and inclusive work environment where innovation thrives. As a Software Engineer, you'll not only contribute to cutting-edge machine learning systems but also enjoy ample opportunities for professional growth through collaboration with researchers and engineers alike. Our commitment to employee well-being is reflected in our supportive culture and the chance to engage in meaningful projects that have a real-world impact.
StudySmarter Expert Advice🤫
We think this is how you could land Software Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with other engineers. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to ML systems or collaborative tools. This gives potential employers a taste of what you can do and how you think.
✨Tip Number 3
Prepare for technical interviews by practicing coding challenges and system design questions. Use platforms like LeetCode or HackerRank to sharpen your skills and get comfortable with problem-solving under pressure.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for passionate engineers who want to make an impact. Plus, applying directly can sometimes give you a leg up in the process.
We think you need these skills to ace Software Engineer
Some tips for your application 🫡
Show Your Passion for Coding:When you're writing your application, let your enthusiasm for coding shine through! We want to see that you’re not just a tech whiz but also genuinely excited about the impact of your work in the research context.
Tailor Your Experience:Make sure to highlight your relevant experience with large-scale ML systems and any specific tools like PyTorch or Kubernetes. We love seeing how your background aligns with our needs, so don’t hold back!
Collaborative Spirit is Key:Since we value collaboration, mention any experiences you’ve had with pair programming or working in teams. Show us how you contribute to a positive engineering culture and how you can work alongside researchers and other engineers.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. We can’t wait to hear from you!
How to prepare for a job interview at Anthropic
✨Know Your Code Inside Out
Before the interview, make sure you’re familiar with your past projects and the code you've written. Be ready to discuss specific challenges you faced and how you overcame them, especially in relation to large-scale ML systems or any relevant technologies like PyTorch or Kubernetes.
✨Show Your Collaborative Spirit
Since the role involves a lot of pair programming and collaboration, be prepared to share examples of how you've worked effectively in teams. Highlight any experiences where you aligned software development with research goals or contributed to a collaborative engineering culture.
✨Demonstrate Flexibility and Impact
Anthropic values results-oriented candidates who can adapt to changing tasks. Think of instances where you took on responsibilities outside your job description and made a significant impact. This will show your willingness to contribute wherever needed.
✨Engage with Machine Learning Concepts
Brush up on machine learning concepts, especially those related to language models and transformers. Be ready to discuss how your engineering work can influence societal impacts and how you can contribute to scientific experiments within the team.
