Staff+ Software Engineer, ML Inference Path

FDE Times; San Francisco (CA); Hybrid; USD 320,000 - 485,000; Full time

Job summary

Anthropic is seeking engineers to design and operate production ML infrastructure powering Claude's safety systems. You will work where ML, distributed systems, and AI safety meet, delivering scalable tooling used in every model launch across multiple platforms.

The role emphasizes building reliable, low-latency inference with automated testing and deployment processes, collaborating with researchers to translate safety research into robust production systems.

Qualifications

• Proficient in Python and ML frameworks; production experience.
• Experience building scalable ML infrastructure for real-time safety deployments.

Responsibilities

• Design scalable ML infrastructure for real-time safety deployments across classifier and model ecosystem.
• Build monitoring and observability tools for classifier performance, data quality, and system health.
• Collaborate with research teams to productionize safety research into robust systems.
• Optimize inference latency and throughput for real-time safety evaluations.
• Implement automated testing, deployment, and rollback systems for ML models in production safety apps.
• Partner with Safeguards, Security, and Alignment teams to meet safety and production needs.

Skills

Python

ML frameworks

Distributed systems

Deployment pipelines

A/B testing

Reliability

Education

Bachelor's degree

Tools

PyTorch

TensorFlow

JAX

Job description

About Anthropic

Anthropic’s 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 Safeguards ML Inference Path team designs, builds, and operates the production infrastructure that powers Claude's ML based safety systems. We collaborate closely with safety researchers and inference engineers to bring new classifiers and novel classes of ML defenses to production. We own the research → production transfer of new safety technologies that is on the critical path for every Claude model launch. And we build for scale: serving thousands of ML classifiers, for all requests on the token generation path, and for every platform Claude runs on -- 1P, Bedrock, Vertex, and beyond.

We’re growing the team and looking for engineers who have deep expertise in productionizing ML systems. You'll work at the intersection of machine learning, large-scale distributed systems, and AI safety, developing the platforms and tools that enable our safeguards to operate reliably at scale. And your tooling and infrastructure will be used for every model launch, which are becoming more complex, and more frequent.

Responsibilities

• Design and build scalable ML infrastructure to support real-time safety deployments across our classifier and model ecosystem
• Build monitoring and observability tools to track classifier performance, data quality, and system health for safety-critical applications
• Collaborate with research teams to productionize safety research, translating experimental safety techniques into robust, scalable systems
• Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability standards
• Implement automated testing, deployment, and rollback systems for ML models in production safety applications
• Partner with Safeguards, Security, and Alignment teams to understand requirements and deliver infrastructure that meets safety and production needs
• Contribute to the development of internal tools and frameworks that accelerate safety research and deployment

You may be a good fit if you

• Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX
• Understand distributed systems principles and have built systems that handle high-throughput, low-latency workloads
• Have built automated or self-service deployment pipelines and eval infrastructure allowing researchers to roll out classifiers and models independently
• Have implemented A/B testing frameworks and experimentation infrastructure for ML systems
• Are results-oriented, with a bias towards reliability and impact in safety-critical systems
• Enjoy collaborating with researchers and translating cutting-edge research into production systems
• Care deeply about AI safety and the societal impacts of your work

Strong candidates may also have experience with

• Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment
• Working with large language models and modern transformer architectures
• Developing monitoring and alerting systems for ML model performance and data drift
• Experience in trust & safety, fraud prevention, or content moderation domains
• Knowledge of privacy-preserving ML techniques and compliance requirements

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary

$320,000—$485,000 USD

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

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.

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. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.