- Location
- New York
- Last Published
- Dec. 10, 2024
- Sector
- AI/ML
- Function
- Software Engineering
About Arthur
At Arthur, we are deeply passionate about building technology to make AI understandable, effective, and fair. Arthur makes machine learning work for enterprises through performance monitoring, explainability, and bias detection. We’re built by AI experts and backed by world-class, diverse investors.
This position is a rare (and fun!) opportunity to help shape the future of AI and its real-world impact. We are an equal opportunity employer and believe strongly in front-end ethics: building a company and industry where strong performance and a positive human impact are inextricably linked.
About the role
Arthur is looking for a product-focused Machine Learning Engineer to join our team, to operate with autonomy and with the support of the team to lead and build out our product vision and deliver value to our customers.
What you'll do
- Focus on ML, especially LLMs, model performance, security, and robustness: make LLMs and model-based systems respond accurately, quickly, and comprehensively to general and task-specific interactions by both natural and adversarial users – for example, checking veracity of responses, hardening against prompt injection, query-based routing to different models, and generation of evaluation datasets
- Build out new techniques and tooling in ML model monitoring: data drift detection, high-dimensional density estimation, time series anomaly detection; detection and mitigation of issues in fairness and bias in ML algorithms
- Run machine learning experiments independently and in collaboration with product teams, and communicate results to internal stakeholders as well as the broader ML community via publications in top-tier conferences, industry conference presentations, deep technical blogs, and events
- Drive value creation using our product with our customers in collaboration with our customer teams by engaging in targeted and focused short-term project work to understand, experiment, refine and deploy ML-enabled features for customer use-cases