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.
Reward models are a critical component of how we align our AI systems with human values and preferences, serving as the bridge between human feedback and model behavior. In this role, you'll build the infrastructure that enables us to train reward models efficiently and reliably, scale to increasingly large model sizes, and incorporate diverse forms of human feedback across multiple domains and modalities. You will own the end-to-end engineering of reward model training at Anthropic.
You'll work at the intersection of machine learning systems and alignment research, partnering closely with researchers to translate novel techniques into production-grade training pipelines. This is a high-impact role where your work directly contributes to making Claude more helpful, harmless, and honest.
Note: For this role, we conduct all interviews in Python.
Responsibilities:- Own the end-to-end engineering of reward model training, from data ingestion through model evaluation and deployment
- Design and implement efficient, reliable training pipelines that can scale to increasingly large model sizes
- Build robust data pipelines for collecting, processing, and incorporating human feedback into reward model training
- Optimize training infrastructure for throughput, efficiency, and fault tolerance across distributed systems
- Extend reward model capabilities to support new domains and additional data modalities
- Collaborate with researchers to implement and iterate on novel reward modeling techniques
- Develop tooling and monitoring systems to ensure training quality and identify issues early
- Contribute to the design and improvement of our overall model training infrastructure
- Have significant experience building and maintaining large-scale ML systems
- Are proficient in Python and have experience with ML frameworks such as PyTorch
- Have experience with distributed training systems and optimizing ML workloads for efficiency
- Are comfortable working with large datasets and building data pipelines at scale
- Can balance research exploration with engineering rigor and operational reliability
- Enjoy collaborating closely with researchers and translating research ideas into reliable engineering systems
- Are results-oriented with a bias towards flexibility and impact
- Can navigate ambiguity and make progress in fast-moving research environments
- Adapt quickly to changing priorities, while juggling multiple urgent issues
- Maintain clarity when debugging complex, time-sensitive issues
- Pick up slack, even if it goes outside your job description
- Care about the societal impacts of your work and are motivated by Anthropic's mission
- Training or fine-tuning large language models
- Reinforcement learning from human feedback (RLHF) or related techniques
- GPUs, Kubernetes, and cloud infrastructure (AWS, GCP)
- Building systems for human-in-the-loop machine learning
- Working with multimodal data (text, images, audio, etc.)
- Large-scale ETL and data processing frameworks (Spark, Airflow)
- Scaling reward model training to handle models with significantly more parameters while maintaining training stability
- Building a unified data pipeline that ingests human feedback from multiple sources and formats for reward model training
- Implementing fault-tolerant training infrastructure that gracefully handles hardware failures during long training runs
- Developing evaluation frameworks to measure reward model quality across diverse domains
- Optimizing training throughput to reduce iteration time on reward modeling experiments
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.
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.
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.
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
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