Top Hybrid Machine Learning Engineer Jobs

Build the analytical foundation for Trust & Safety: measure abuse, build datasets and dbt models, develop risk and anomaly-detection models, design offline evaluations and experiments, define thresholds and decision frameworks, detect coordinated abuse, embed predictive risk signals into workflows, monitor model drift, and communicate findings to cross-functional partners.

Lead technical vision and architecture for ML and AI across the organization. Build and productionize end-to-end ML systems (NLP, computer vision, deep learning), agentic/LLM applications, and scalable data solutions. Mentor senior data scientists, set standards, partner with engineering and platform teams, and translate business problems into deployable ML solutions with monitoring and reliability.

Build and maintain production AI/ML models and pipelines for an NDR platform; develop scalable, secure backend services; optimize detection algorithms and reduce false positives; integrate ML features into product architecture; mentor engineers; participate in code reviews, technical strategy, and documentation; operate cloud-native, containerized services and manage infrastructure as code.

Build and deploy ML models and pipelines for entity resolution and a knowledge graph at scale. Fine-tune algorithms, expand data ingestion and enrichment, create evaluation harnesses, and integrate agentic workflows to support research and product teams processing tens of millions of records daily.

Design, build, and operate large-scale RL post-training systems for multimodal foundation models: distributed training, high-throughput rollout generation, environment and reward infrastructure, evaluation and debugging tooling, and efficiency/stability improvements. Collaborate with researchers to productionize RL methods and run stable RL training across thousands of GPUs.

Lead design, development, and delivery of ML, DL, and Generative AI systems for production. Apply prescriptive analytics, optimization, recommendation, and reinforcement learning. Build and evaluate models, run experiments, develop prototypes, benchmark methods, and collaborate with business partners to translate data into actionable recommendations and influence strategy.

Lead design, development, and production deployment of machine learning models for personalization, recommendations, churn and lifetime value. Partner with product, engineering, editorial, and revenue teams to drive measurable outcomes, perform advanced analytics on large datasets, mentor junior staff, and champion new data science methodologies.

Design, implement, and optimize machine learning and deep learning models; preprocess and analyze large datasets; integrate models into production; monitor and maintain model performance; collaborate with engineers and stakeholders; stay current with ML/AI advances.

Lead product strategy and execution for AI-driven expert research: translate investor needs into AI workflows, define roadmap, run user research and experiments, collaborate with engineering/design, and drive launches while optimizing accuracy and reliability of LLM/ML outputs.

Design, train, evaluate, deploy, and monitor large-scale ML systems (LLMs and bandits) for Duolingo Monetization. Collaborate cross-functionally, drive product decisions, mentor engineers, and ensure scalable, high-quality solutions.