Top Hybrid Machine Learning Engineer Jobs

Build, train, evaluate, and deploy production machine learning and LLM systems for legal document intelligence. Develop NLP solutions for information extraction, summarization, search, AI agents, and document analysis. Create scalable ML pipelines, evaluation frameworks, and reliability metrics while optimizing model performance. Collaborate with engineering, product, legal, data, and security teams to deliver secure AI capabilities and translate business problems into production solutions.

Lead engineering for a real-to-sim platform supporting embodied AI and robot learning. Own the technical roadmap and requirements, prototype and ship critical features, benchmark policy transfer, diagnose sim-to-real failures, and evaluate robotics systems in real-world conditions. Collaborate with researchers, product leaders, customers, and test labs while advancing simulation, reinforcement learning, visual perception, and 3D scene representation capabilities.

Designs and scales production-grade data and machine learning pipelines for fraud, payment risk, and abuse prevention. Responsibilities include ETL and feature engineering, model training and deployment, data and model monitoring, incident response, code reviews, mentoring, and collaboration with data scientists, engineers, and product managers. The role supports reliable, explainable decision-making across financial and operational systems.

Leads the design and deployment of AI-enabled data engineering frameworks, intelligent agents, automation, and scalable data pipelines. Applies Generative AI, Agentic AI, RAG, vector search, Lakehouse, and Medallion patterns using Databricks, Microsoft Fabric, Spark, Python, and SQL. Establishes secure governance, observability, testing, CI/CD, and production support standards while collaborating across architecture, platform, DevSecOps, governance, and business teams. Mentors engineers and drives enterprise adoption of AI-first data engineering practices.

Design and deploy multimodal 2D/3D perception models for warehouse robots. Own model optimization, quantization, edge inference, production integration, dataset quality, active learning, and pipeline reliability. Lead code reviews, mentor engineers, and shape the perception roadmap while delivering robust computer vision systems for real-world robotic applications.

Design, train, and deploy scalable ML models (generative, classification, regression). Define feature roadmaps, collaborate with engineering to implement code and APIs, adapt ML methods for distributed/GPU environments, and create evaluation/annotation programs for fine-tuning and performance measurement.

Build and ship agent-driven chatbots and Generative AI workflows, owning features from definition and design through implementation, evaluation, and impact measurement. Collaborate with product, machine learning, and engineering teams while using modern AI-assisted development tools. The role emphasizes reliable software, system design, initiative, continuous learning, and improving engineering practices through AI agents.

Conduct machine learning and AI research applied to Pinterest problems. Build and improve large-scale production ML systems spanning algorithms, infrastructure, data engineering, training, inference, and product integration. Develop clean code, validate AI-generated outputs, use AI-assisted engineering tools, investigate bugs, and independently deliver projects while collaborating with engineering and research teams.

As a founding engineer you'll own core systems end-to-end, build full-stack features (React/TypeScript/Python), develop AI-powered multimodal agents (voice, text, email), integrate LLMs and speech-to-text, automate recruiting workflows, work directly with customers, and shape engineering culture and product direction.

Develop deep learning models using imitation and reinforcement learning to generate and evaluate autonomous vehicle trajectories. Build production machine learning pipelines, metrics, and error-analysis tools, leveraging large-scale infrastructure. Collaborate with Planning, Perception, Simulation, and Systems Engineering teams to improve autonomous driving behavior in complex urban environments.