Senior Machine Learning Engineer, Trust & Safety

Responsibilities

• Develop, deploy, and maintain end-to-end machine learning models to identify and mitigate bad actors, remove policy-violating content, and ensure user safety.
• Design and implement scalable systems (e.g., using Spark, Kubernetes) to preprocess data, run inference, and manage post-processing pipelines.
• Define standardized performance metrics, testing protocols, and evaluation processes to measure the effectiveness, identify and mitigate potential risks, and ensure fairness of AI solutions.
• Ensure ongoing assessment and refinement of AI solutions, incorporating user feedback, business impact, and emerging ethical considerations.
• Collaborate closely with Data Scientists, Data Engineers, Product Managers, Backend Engineers, and the AI Platform Team to ensure a comprehensive and coordinated approach to user safety.
• Stay abreast of new trends and research in AI/ML that can be applied to Trust & Safety initiatives to improve detection, user safety, and stay ahead of potential threats.

What We're Looking For

• Strong programming skills: Proficiency in languages like Python, Java or C++ and SQL, proficiency in at least one ML stack (e.g., PyTorch), and strong understanding of data pipelines
• Domain expertise: Deep understanding of machine learning, deep learning, and emerging AI technologies.  Proven track record of building, debugging, and fine-tuning real-time machine learning models for user facing products. Experience with applying expertise to fraud detection, content moderation, or related fields is a plus .
• System design & architecture: Experience training and deploying large scale ML models. Good understanding of distributed computing for learning and inference.
• Evaluation frameworks for LLMs: Experience designing robust testing protocols to ensure effectiveness, fairness, and safety of AI-driven features. 
• Cloud and data platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure. Familiarity with solutions like Databricks, Ray, or KubeFlow is a plus.
• Data engineering knowledge: Skills in handling and managing large datasets including, data cleaning, preprocessing, and storage. Good understanding of batch and streaming pipelines as well as orchestrators like Argo and Airflow.
• Strategic technical leadership skills: Demonstrated track record of guiding teams through complex ML projects in alignment with product and business objectives.
• Collaboration and communication skills: The ability to work effectively in a team and communicate complex ideas clearly with individuals from diverse technical and non-technical backgrounds.
• Strong written communication: The ability to communicate complex ideas and technical knowledge through documentation.


• 4+ years of experience, depending on education, as an MLE or data scientist. Previous experience working in Trust & Safety or related fields (e.g., fraud detection, content moderation, compliance) is preferred.
• 2+ years of experience in applying end-to-end machine learning models, including data collection, model training, deployment, and monitoring in an industry setting
• 1+ years of experience integrating LLMs in real-world applications with the appropriate baseline metrics and evaluation methodologies.
• 1+ years of experience utilizing ML infrastructure components such as a feature store, model training environment, model serving environment, observability, workflow orchestrator, etc.
• A degree in computer science, engineering, or a related field (or equivalent practical experience).