Staff Software Engineer, Compute ML Scheduling and Observability
About Anthropic
Anthropics 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.
About The Role
The mission of the Capacity Engineering & Efficiency team is to provide input into our company-wide cloud infrastructure strategy and efficiency deliverables, with a specialized focus on ML Scheduling and Observability for our Compute infrastructure. You will develop and optimize scheduling systems for our large-scale machine learning workloads, particularly working with our Python-based scheduling architecture and orchestrating workloads across jobs. Your work will contribute to our path toward building RL-aware schedulers while supporting and improving our model development through improved observability and capacity efficiency. You will be expected to work with engineering teams to ensure optimal operation and growth of our infrastructure from both a cost and technology perspective, collaborate with research engineering to scope and understand the observability and capacity needs for model development, and partner cross-functionally with finance and data science teams to analyze and forecast growth.
You May Be a Good Fit If You
Experience instrumenting ML workloads for performance monitoring/efficiency
Experience with high performance, large scaled distributed systems
Experience with LLM inference and Reinforcement Learning
Observability tooling and best practices (logging, metrics, tracing)
10+ years experience in capacity efficiency or performance engineering
10+ years experience in a technical role
Have experience in scripting and building automation tools
Are self-disciplined and thrives in fast paced environments
Have Excellent communication skills
Pick up slack, even if it goes outside your job description
Have attention to detail and a passion for correctness
Strong Candidates May Also Have Experience With
Reinforcement Learning
Cross-Platform accelerators
Pytorch
Python
Kubernetes
Performance optimization across multiple platforms/environments
Representative Projects
Develop self-service tools and dashboards to enable anthropic engineers to understand their capacity, efficiency, and costs, leveraging observability best practices
Investigate capacity requests and recommend right-sizing strategies for performance optimization across multiple platforms/environments
Design and implement observability solutions that provide insights into infrastructure efficiency for large-scale distributed systems
Collaborate with engineering teams to identify and resolve performance bottlenecks in Kubernetes-based ML infrastructure
Partner with research teams to quantify computational requirements for new ML initiatives and develop appropriate capacity plans
Deadline to apply: None. Applications will be reviewed on a rolling basis.
Annual Salary
The expected salary range for this position is:
$320,000 - $405,000 USD
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.
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.
Apply Now
Anthropics 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.
About The Role
The mission of the Capacity Engineering & Efficiency team is to provide input into our company-wide cloud infrastructure strategy and efficiency deliverables, with a specialized focus on ML Scheduling and Observability for our Compute infrastructure. You will develop and optimize scheduling systems for our large-scale machine learning workloads, particularly working with our Python-based scheduling architecture and orchestrating workloads across jobs. Your work will contribute to our path toward building RL-aware schedulers while supporting and improving our model development through improved observability and capacity efficiency. You will be expected to work with engineering teams to ensure optimal operation and growth of our infrastructure from both a cost and technology perspective, collaborate with research engineering to scope and understand the observability and capacity needs for model development, and partner cross-functionally with finance and data science teams to analyze and forecast growth.
You May Be a Good Fit If You
Experience instrumenting ML workloads for performance monitoring/efficiency
Experience with high performance, large scaled distributed systems
Experience with LLM inference and Reinforcement Learning
Observability tooling and best practices (logging, metrics, tracing)
10+ years experience in capacity efficiency or performance engineering
10+ years experience in a technical role
Have experience in scripting and building automation tools
Are self-disciplined and thrives in fast paced environments
Have Excellent communication skills
Pick up slack, even if it goes outside your job description
Have attention to detail and a passion for correctness
Strong Candidates May Also Have Experience With
Reinforcement Learning
Cross-Platform accelerators
Pytorch
Python
Kubernetes
Performance optimization across multiple platforms/environments
Representative Projects
Develop self-service tools and dashboards to enable anthropic engineers to understand their capacity, efficiency, and costs, leveraging observability best practices
Investigate capacity requests and recommend right-sizing strategies for performance optimization across multiple platforms/environments
Design and implement observability solutions that provide insights into infrastructure efficiency for large-scale distributed systems
Collaborate with engineering teams to identify and resolve performance bottlenecks in Kubernetes-based ML infrastructure
Partner with research teams to quantify computational requirements for new ML initiatives and develop appropriate capacity plans
Deadline to apply: None. Applications will be reviewed on a rolling basis.
Annual Salary
The expected salary range for this position is:
$320,000 - $405,000 USD
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.
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.
Apply Now