Top Hybrid Machine Learning Engineer Jobs

Own the strategy, architecture, development, deployment, monitoring, and continuous improvement of production personalization and recommendation systems. Build custom embeddings, ranking models, and shared representations across feeds, search, and recommendations. Lead ambiguous problems from concept to measurable customer impact, establish AI evaluation standards, partner with product and data teams, make cross-team technical decisions, and coach senior engineers.

Develop machine learning capabilities, custom analytics, and mission-critical AI solutions across image, video, text, geospatial, time-series, and structured data. Build and orchestrate data engineering pipelines for agentic and traditional architectures, collaborate with data scientists, software and DevOps engineers, contribute to the Chariot AI operations platform, and support customer-facing deployments.

Lead development and productionization of advanced forecasting and predictive models: assess existing analytics, define data needs, design methodologies, run proofs-of-concept, architect cloud-based model deployments, and monitor model performance while translating results for business stakeholders.

Leads engineering for enterprise agentic AI and LLM platforms. Designs and deploys production-grade LLM services, RAG solutions, autonomous agents, APIs, and cloud-native AWS architectures. Provides technical leadership, architecture guidance, code reviews, MLOps, monitoring, governance, and operational excellence. Drives responsible AI practices and adoption of AI-assisted development across teams, emphasizing security, auditability, human oversight, scalability, and performance.

Build, deploy, and maintain machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Use AI tools to develop scalable services while ensuring code correctness, security, performance, and architectural quality. Mentor collaborators and solve ambiguous, large-scale problems.

Build and deploy machine learning models powering Snapchat products at scale. Own the full ML lifecycle from data analysis through production deployment, apply modern ML techniques, and collaborate with cross-functional teams to launch ML-driven features. Develop scalable, secure, production-ready services using AI-assisted engineering workflows, while contributing to ranking, recommendations, search, content understanding, or image-generation applications.

Build and deploy machine learning models for Snapchat products, contributing across data analysis, training, evaluation, and production deployment. Collaborate with cross-functional teams to launch scalable ML-powered features while maintaining high standards for code quality, security, and reliability. Apply established machine learning techniques to ranking, recommendations, search, content understanding, image generation, and related applications.

Build cloud-based AI and machine learning solutions for intelligent enterprise services. Collaborate with developers, product managers, product owners, and quality engineers to design, implement, test, and deliver scalable software. Apply AI/ML techniques, high-performance algorithms, object-oriented programming, modular design, unit testing, profiling, and code optimization to improve enterprise workflows and user experiences.

Design, build, and deploy production-scale ML models and ranking systems for personalization and advertising. Take end-to-end ownership of models and services, implement evaluation harnesses and automated tests, and maintain data pipelines using Hadoop/Spark/Kafka. Collaborate across teams and present technical findings, while ensuring scalable, reliable system architecture and rapid iteration using AI-assisted coding tools.

Lead large-scale machine learning initiatives, develop and deploy production ML models, optimize data pipelines and distributed systems, and build cloud-based ML platforms. The role uses Python, Java, Go, Scala, and modern ML frameworks and requires technical leadership across architecture, modeling, infrastructure, and engineering talent development.

Build and operate production LLM-powered agents and machine learning systems for customer risk identification and threat detection. Translate security heuristics into agent logic, collaborate with threat researchers, develop data requirements and pipelines, evaluate precision and recall, and deploy solutions through CI/CD. The role also requires production reliability ownership, observability, incident debugging, cloud infrastructure expertise, and senior staff-level technical leadership.

Design, build, and operate scalable machine learning platform capabilities across feature engineering, model training, inference, monitoring, and lifecycle management. Develop reliable software and APIs, improve production reliability and performance, resolve technical issues, and establish scalable platform architecture. Partner with engineering stakeholders to drive adoption of shared ML infrastructure and safely integrate AI/ML-enabled solutions.