Machine Learning Systems Engineer, Model APIs

Seeking a Machine Learning Systems Engineer to build and maintain Model Evaluations infrastructure and APIs for Research Inference, requiring 5+ years of software engineering experience and proficiency in Python and cloud infrastructure.

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems.

About the Role

We are seeking a Machine Learning Systems Engineer to join our Model APIs team at Anthropic.

Responsibilities

Design, build, and maintain Model Evaluations infrastructure that enables researchers to systematically test and assess model capabilities
Develop and optimize APIs and infrastructure for Research Inference to accelerate the model development lifecycle
Create scalable data pipelines for collecting, processing, and analyzing research outputs
Implement monitoring, logging, and performance optimization for research-focused inference systems
Build intuitive interfaces and tools that allow researchers to configure, run, and analyze complex evaluation workflows
Collaborate with research teams to understand their evolving needs and translate requirements into reliable technical solutions
Improve system performance, reliability, and scalability to handle increasingly complex research needs
Participate in your team's on-call rotation, deliver operationally ready code, and exercise a high degree of customer focus in your work
Document systems thoroughly to enable broader adoption and ease of use

You May Be a Good Fit If You

Have 5+ years of software engineering experience
Are results-oriented, with a bias towards flexibility and impact
Have experience with data infrastructure and processing large datasets
Are comfortable working independently and taking ownership of projects from conception to delivery
Have excellent communication skills and can collaborate effectively with research teams
Are proficient in Python and have experience with cloud infrastructure (AWS, GCP)
Can anticipate the needs of research users and design systems that are both powerful and usable
Pick up slack, even if it goes outside your job description
Enjoy pair programming (we love to pair!)
Care about the societal impacts of your work and are committed to developing AI responsibly

Strong Candidates May Also Have Experience With

High performance, large-scale ML systems
GPUs, Kubernetes, PyTorch, or ML acceleration hardware
Building evaluation frameworks for machine learning models
Working in or adjacent to ML research teams
Distributed systems design and optimization
Real-time inference systems for large language models
Performance profiling and optimization
Infrastructure as Code and CI/CD pipelines

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

The expected {#salary}salary range for this position is:

Annual Salary:

$300,000—$405,000 USD{/id}

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.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate.

We encourage you to apply even if you do not believe you meet every single qualification.

How we're different

We believe that the highest-impact AI research will be big science.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco.

Responsibilities

Design, build, and maintain Model Evaluations infrastructure that enables researchers to systematically test and assess model capabilities

Develop and optimize APIs and infrastructure for Research Inference to accelerate the model development lifecycle

Create scalable data pipelines for collecting, processing, and analyzing research outputs

Implement monitoring, logging, and performance optimization for research-focused inference systems

Build intuitive interfaces and tools that allow researchers to configure, run, and analyze complex evaluation workflows

Collaborate with research teams to understand their evolving needs and translate requirements into reliable technical solutions

Improve system performance, reliability, and scalability to handle increasingly complex research needs

Participate in your team's on-call rotation, deliver operationally ready code, and exercise a high degree of customer focus in your work

Document systems thoroughly to enable broader adoption and ease of use

You May Be a Good Fit If You

Have 5+ years of software engineering experience

Are results-oriented, with a bias towards flexibility and impact

Have experience with data infrastructure and processing large datasets

Are comfortable working independently and taking ownership of projects from conception to delivery

Have excellent communication skills and can collaborate effectively with research teams

Are proficient in Python and have experience with cloud infrastructure (AWS, GCP)

Can anticipate the needs of research users and design systems that are both powerful and usable

Pick up slack, even if it goes outside your job description

Enjoy pair programming (we love to pair!)

Care about the societal impacts of your work and are committed to developing AI responsibly

Strong Candidates May Also Have Experience With

High performance, large-scale ML systems

GPUs, Kubernetes, PyTorch, or ML acceleration hardware

Building evaluation frameworks for machine learning models

Working in or adjacent to ML research teams

Distributed systems design and optimization

Real-time inference systems for large language models

Performance profiling and optimization

Infrastructure as Code and CI/CD pipelines