ML Infrastructure Engineer, Safeguards
ML Infrastructure Engineer, Safeguards
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$ 300000 - 405000
1 Vacancy
Job Description
About Anthropic
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.
About the role
We are seeking a Machine Learning Infrastructure Engineer to join our Safeguards organization where youll build and scale the critical infrastructure that powers our AI safety systems. Youll 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.
As part of the Safeguards team youll design and implement ML infrastructure that powers Claude safety. Your work will directly contribute to making AI systems more trustworthy and aligned with human values ensuring our models operate safely as they become more capable.
Responsibilities:
- Design and build scalable ML infrastructure to support real-time and batch classifier and safety evaluations across our model ecosystem
- Build monitoring and observability tools to track model 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:
- Have 5 years of experience building production ML infrastructure ideally in safety-critical domains like fraud detection content moderation or risk assessment
- Are proficient in Python and have experience with ML frameworks like PyTorch TensorFlow or JAX
- Have hands-on experience with cloud platforms (AWS GCP) and container orchestration (Kubernetes)
- Understand distributed systems principles and have built systems that handle high-throughput low-latency workloads
- Have experience with data engineering tools and building robust data pipelines (e.g. Spark Airflow streaming 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 have experience with:
- Working with large language models and modern transformer architectures
- Implementing A/B testing frameworks and experimentation infrastructure for ML systems
- Developing monitoring and alerting systems for ML model performance and data drift
- Building automated labeling systems and human-in-the-loop workflows
- Experience in trust & safety fraud prevention or content moderation domains
- Knowledge of privacy-preserving ML techniques and compliance requirements
- Contributing to open-source ML infrastructure projects
Deadline to apply:None. Applications will be reviewed on a rolling basis.
The expected salary range for this position is:
Annual Salary:
$300000 - $405000 USD
Logistics
Education requirements: We require at least a Bachelors 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 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.
How were 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. 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.
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.
Employment Type
Full Time
