Strategic Overview
Machine Learning Infrastructure Engineer, Safeguards Research at Anthropic is a aiml opportunity centered on the specialist responsibilities and outcomes described in the employer's listing.
The published role emphasizes work such as 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.
The role is listed for San Francisco, CA / New York City, NY with the working arrangement published with the role, giving candidates the practical context needed to assess fit before applying.
Key information at a glance
About This Role
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.
Key Responsibilities & Objectives
- 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
Required Qualifications & Candidate Blueprint
- 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
Salary & Benefits
Published compensation: $350,000 – $500,000 USD
Salary Benchmark
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Full Job Description
The following is the complete job description supplied by the source. We sanitize unsafe markup and remove external links, but do not shorten or rewrite the employer's source content.
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's 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 Anthropic's Responsible Scaling Policy commitments.
We're 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. You'll 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.
Key responsibilities
- 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
Minimum qualifications
- 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
Preferred qualifications
- 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
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.
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.
Editor’s Market Perspective (Our Custom Analysis)
Why This Position Matters
This aiml opening is a useful opportunity for candidates whose experience matches the role-specific capabilities named in the listing. Its strongest career signal is the scope of the work itself — particularly Build and scale the infrastructure and data pipelines behind Safeguards machine learning research — rather than a generic title alone.
Resume & Application Strategy
For the application, mirror the employer's language where it truthfully matches your background: foreground the role-specific capabilities named in the listing, quantify outcomes from comparable work, and place the most relevant experience near the top of the resume. If the listing specifies tools, platforms, leadership scope, or domain experience, use those exact terms in context instead of presenting an unrelated keyword list.
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