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. 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. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. 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.

Requirements

Must-haves

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
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.
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

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningpython

Platform: greenhouse
Effort: quick

Benefits

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