Top Hybrid Machine Learning Engineer Jobs

Develops, tests, evaluates, and deploys machine learning models using established architectures and frameworks. Supports feature engineering, data preprocessing, automated training and monitoring pipelines, and production AI integration. Collaborates with engineering and product teams on AI requirements, researches methods to improve model performance, and supports urgent IT incidents. The role requires strong Python and machine learning experience, knowledge of deep learning and generative AI, and advanced quantitative education.

Develop, test, and deploy machine learning models; support feature engineering, preprocessing, evaluation, and automated training and monitoring pipelines. Collaborate with engineering and product teams to define AI solutions, research methods to improve performance, and support urgent IT escalations. The role requires advanced quantitative education, 5+ years of AI/ML engineering experience, Python expertise, and knowledge of deep learning and generative AI.

Build, test, deploy, and evaluate machine learning models using established frameworks. Support feature engineering, data preprocessing, automated training and monitoring pipelines, and production AI integration. Collaborate with engineering and product teams to define AI requirements, research performance improvements, and respond to critical IT incidents. The role requires advanced quantitative education, 5+ years of AI/ML engineering experience, Python expertise, and knowledge of deep learning and generative AI.

Build and maintain production ML platforms, data and feature pipelines, cloud infrastructure, and MLOps automation on AWS. Support model training, deployment, monitoring, testing, legacy workload migration, and platform reliability. Collaborate with data scientists to deliver scalable capabilities, while promoting secure, responsible use of AI-assisted engineering tools and validating AI-generated outputs. Requires Python, ML frameworks, AWS, Spark, infrastructure-as-code, and deployment automation experience.

Build and scale core ML platform infrastructure for model training, deployment, feature storage, real-time retrieval, inference, and monitoring. Lead architecture and technical execution, write production backend code, conduct design and code reviews, mentor engineers, improve reliability and cost efficiency, and partner with ML, product, data science, infrastructure, and security teams on platform strategy and roadmap.

Leads Snap’s Content Machine Learning Engineering organization, setting multi-year strategy for recommendation, ranking, content understanding, reliability, and experimentation. Oversees distributed teams, develops organizational roadmaps, partners with executives and cross-functional leaders, influences scalable architecture decisions, and builds high-performing engineering teams. The role requires strong expertise in machine learning, deep learning, AI, ranking systems, and large-scale engineering leadership.

Design, train, optimize, and deploy efficient machine learning models for consumer audio products and resource-constrained edge devices. Apply quantization, pruning, distillation, and model compression while balancing accuracy, latency, memory, compute, and power requirements. Collaborate with embedded, DSP, hardware, compiler, and software teams on deployment and integration, troubleshoot model issues, develop repeatable ML workflows, evaluate emerging technologies, document findings, and mentor interns and co-op engineers.

Own numerical analysis and model parity for compressed, quantized, and optimized autonomous-vehicle models. Develop Jacobian/Hessian sensitivity tools, connect numerical differences to driving behavior, diagnose training instabilities, and build low-overhead observability for distributed training. Investigate floating-point errors, edge cases, and implementation divergence while translating analytical results into deployment decisions and production-ready tooling.

Design, develop, deploy, and maintain production machine learning models and infrastructure. Build optimized data pipelines, distributed systems, cloud-based architectures, and automated testing and deployment processes. Monitor and retrain models, ensure responsible and explainable AI practices, and collaborate with Product, Data Science, and Agile engineering teams to deliver scalable ML solutions.

Lead research, design, and production deployment of large-scale search and recommendation ML systems. Architect multi-modal retrieval and multi-stage ranking pipelines, advance intent understanding and personalization, develop sequential recommenders, design offline/online evaluation (A/B, counterfactual), collaborate with engineering/product teams, and mentor scientists to deliver production-ready AI solutions.

Build, deploy, and operate production-grade multimodal machine learning services for document extraction, visual understanding, and agentic automation. Responsibilities include integrating LLMs, designing APIs and pipelines, deploying services on Kubernetes, improving scalability and observability, writing tests, evaluating model quality, resolving production issues, and collaborating across product and engineering teams.

Leads AI strategy, teams, and production-scale machine learning initiatives. Establishes MLOps practices, model lifecycle management, governance, and ethical AI frameworks; evaluates external technologies; integrates AI across products and platforms; and drives measurable business outcomes. Provides technical leadership across deep learning, generative AI, NLP, computer vision, and predictive analytics while mentoring teams and influencing executive stakeholders.