ML Infrastructure Engineering Manager, Safeguards

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

Anthropic is seeking an ML Infrastructure Engineering Manager to lead a critical team within our Safeguards organization. You'll manage and grow a team of infrastructure engineers who build and scale the foundational systems that power our AI safety and trust mechanisms. This role combines deep technical leadership in ML infrastructure with people management, driving both the strategic vision and day-to-day execution of systems that ensure our AI models operate safely and reliably at scale.

Your team will be responsible for the infrastructure backbone that enables real-time safety evaluations and systems to make Claude safe You'll work closely with research teams to translate cutting-edge safety research into production-ready systems, while partnering with Safeguards, Security, and Alignment teams to ensure our infrastructure meets the demanding requirements of safety-critical applications.

Responsibilities:

• Set team vision and roadmap for ML infrastructure that powers Anthropic's safety and trust systems, ensuring scalability, reliability, and performance at production scale
• Lead a team of ML infrastructure and software engineers to build robust platforms supporting real-time safety evaluations, feature stores, model serving, and data pipelines
• Partner with Safeguards, Security, Research, and Product teams to identify infrastructure requirements and translate complex safety research into scalable production systems
• Drive technical strategy for ML infrastructure architecture, making key decisions about technology choices, system design, and platform evolution
• Maintain deep technical expertise in ML infrastructure, distributed systems, and safety-critical applications to provide technical leadership and guidance
• Hire, support, and develop team members through continuous feedback, career coaching, and people management practices
• Collaborate across teams to ensure infrastructure supports rapid experimentation while maintaining production reliability and safety standards
• Champion engineering best practices including automated testing, deployment pipelines, monitoring, and incident response for safety-critical systems

You may be a good fit if you:

• Have 4+ years of management experience leading technical teams focused on ML infrastructure, platform engineering, or distributed systems
• Have 8+ years of hands-on experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment
• Demonstrated ability to lead and manage high-performing technical teams through periods of rapid growth and scaling challenges
• Possess deep technical knowledge of ML serving platforms, feature stores, data pipelines, and distributed systems architecture
• Show excellent communication skills in translating complex technical concepts for various audiences, from individual contributors to executive leadership
• Have strong project management skills with the ability to balance multiple priorities and coordinate across cross-functional teams
• Experience managing teams that bridge research and production, with a track record of productionizing experimental systems

Strong candidates may also:

• Possess knowledge of modern ML frameworks, cloud platforms, and container orchestration in production environments
• Excel at building strong relationships with research teams and translating their needs into infrastructure requirements
• Have experience implementing automated testing, deployment, and monitoring systems for ML models in production
• Demonstrate passion for ensuring the responsible development and deployment of AI systems
• Have managed teams working on real-time, high-throughput systems with strict latency and reliability requirements
• Experience with compliance and security requirements for safety-critical applications

At Anthropic, we value diversity and are committed to creating an inclusive environment for all employees. We encourage applications from candidates of all backgrounds.

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

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.