Machine Learning Infrastructure Engineer, Safeguards Research

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.

Anthropics Safeguards team builds the systems that detect and mitigate misuse of our AI models from individual policy violations to sophisticated coordinated attacks. A growing part of that work depends on lightweight detection methods trained on model internals which let us identify harmful behavior cheaply and at scale. This work feeds directly into Anthropics Responsible Scaling Policy commitments.

Were looking for an engineer to own the infrastructure behind that research. This is the tooling our researchers rely on to run experiments train detection methods and select detections for launch. It sits between research and production: researchers depend on it for fast iteration and our detection systems depend on it for reliable correct results as our models continue to change.

Running machine learning workloads at our scale often requires solving novel systems problems. Youll identify those problems and build the abstractions pipelines and tooling that keep the research loop fast as requirements shift underneath you. Strong candidates will have a track record of solving large-scale systems and data problems and will be excited to grow deep machine learning expertise alongside it.

• Build and scale the infrastructure and data pipelines behind Safeguards machine learning research
• Own the training evaluation and scoring workflows researchers use with a focus on cutting the time between an idea and a result
• Design tooling and interfaces including libraries and command line tools that researchers can use directly without needing to understand the systems underneath
• Build correctness and sanity checking into the stack so results stay trustworthy as models and workloads evolve
• Take the highest-value research workflows from experiments to reliable production-grade jobs
• Improve the throughput cost and reliability of large-scale inference and scoring workloads
• Partner closely with researchers and engineers across Safeguards to understand their workflows anticipate how their needs will change and design for that ahead of time

• Strong software engineering fundamentals and hands-on coding ability with proficiency in Python
• Experience building and operating data-intensive or distributed systems in production
• Experience building tooling or infrastructure that other engineers or researchers use as a dependency
• Comfort working across the research-to-deployment pipeline from exploratory experiments to production systems
• Ability to debug performance and correctness problems across an unfamiliar stack
• Strong written and verbal communication skills and a collaborative approach to technical decisions

• Experience with high-performance large-scale machine learning systems
• Familiarity with language modeling and transformers including working with model internals
• Experience with machine learning framework internals GPU or accelerator programming or inference optimization
• Experience building experiment tracking caching layers or evaluation harnesses for research teams
• Experience with probes interpretability or classifier development
• Interest in the misuse risks of AI systems and a desire to work on mitigating them

Minimum education: Bachelors 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 arent 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 youre interested in this work. We think AI systems like the ones were 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 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 youre ever unsure about a communication dont click any linksvisit for confirmed position openings.

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. Were 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.

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 aboutour policy for using AI in our application process.