Top Machine Learning Engineer Jobs

Build and deploy manufacturing data systems, machine-vision inspection solutions, and machine-learning models across supplier factories. Integrate MES, equipment, test, image, and genealogy data to improve traceability, yield, quality, process control, and root-cause analysis. Develop automated analytics, dashboards, alerts, and reusable tools; lead deployments through NPI and mass production; collaborate with manufacturers and vendors; and define the long-term manufacturing intelligence roadmap.

Develop and deploy machine learning systems that recover metrically accurate 3D human body and hand motion from egocentric video. Responsibilities include pose modeling, camera calibration, multi-view geometry, temporal consistency, annotation and evaluation tooling, large-scale production inference, and human-to-robot motion retargeting. The role combines computer vision, deep learning, robotics, and real-world multi-camera capture systems.

Build and maintain scalable machine learning models and end-to-end ML pipelines for logistics automation. Responsibilities include data exploration, model deployment and monitoring, production infrastructure improvements, collaboration with engineering and product teams, code reviews, architectural guidance, and technical mentorship.

Lead the design, development, deployment, and operation of production-grade generative AI and LLM solutions. Build data and ML pipelines, fine-tune and evaluate models, implement RAG and prompt engineering, and establish MLOps practices on AWS. Drive use cases across insurance workflows, ensure security and responsible AI compliance, mentor engineers, and collaborate with product, engineering, security, finance, and go-to-market teams.

Develop and deploy production-grade AI and machine learning applications as part of a backend Python team. Responsibilities include integrating ML models and data pipelines, developing LLM prompts, supporting NLP, machine translation, and text-to-speech solutions, building CI/CD pipelines, implementing testing, and collaborating with Product and Engineering teams. The role requires strong Python and C++ skills, production deployment experience, and familiarity with ML frameworks and cloud-native tools.

Build and scale the infrastructure layer for visual intelligence: optimize VLM inference and GPU serving, design multimodal APIs and structured outputs, ensure scalable backend systems, reliability, observability, CI/CD, and developer experience. Drive 0→1 product work with strong testing discipline, schema validation, and performance optimization.

Design, deploy, evaluate, and maintain recommendation, ranking, personalization, and predictive behavior models for Taboola’s advertising platform. Build ML evaluation harnesses, automated testing frameworks, benchmarks, and guardrails. Develop data pipelines and transformations using Kafka, SQL, HDFS, Hadoop, Spark, and distributed big-data systems while collaborating with product and technical teams.

Design, build, deploy, and operate production machine learning models and infrastructure. Develop optimized data pipelines, cloud-based architectures, and distributed ML systems; automate testing and deployment; monitor and retrain models; and apply responsible AI, governance, and explainability practices. Collaborate with Product, Data Science, and Agile engineering teams to solve complex business problems at scale.

Build, deploy, and maintain production machine learning models and infrastructure. Responsibilities include designing ML solutions, optimizing models and data pipelines, operating distributed systems and cloud-based platforms, automating testing and deployment, monitoring production models, and applying responsible AI and software engineering practices. The role partners with Product and Data Science teams in an Agile environment and uses technologies including Python, PyTorch, TensorFlow, Spark, Kubernetes, and major cloud platforms.

Build, deploy, and maintain production machine learning models and infrastructure. Design ML components, optimize algorithms, create data pipelines, operate distributed systems, and manage cloud-based containerized services. Apply testing, CI/CD, monitoring, model governance, responsible AI, and explainability practices while collaborating with product, data science, and cross-functional engineering teams.

Build, deploy, scale, and maintain production machine learning models and platforms. Responsibilities include designing ML systems, developing data pipelines, operating distributed systems and cloud infrastructure, automating testing and deployment, monitoring models, applying responsible AI practices, and collaborating with product, data science, and engineering teams. The role requires advanced programming, machine learning, distributed computing, Kubernetes, and cloud experience.

Build, deploy, scale, and maintain production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing models and data pipelines, operating distributed systems and cloud-based platforms, automating testing and deployment, monitoring production services, and applying responsible AI and governance practices. The role collaborates with product, data science, and Agile engineering teams to solve complex business problems.

Build, deploy, optimize, and monitor machine learning models and infrastructure at scale. Develop production ML applications, data pipelines, cloud architectures, automated testing, and CI/CD workflows. Collaborate with product and data science teams, operate distributed systems, maintain models in production, and apply responsible AI, model governance, security, and explainability practices.

Build, deploy, scale, and monitor production machine learning models and infrastructure for risk management. Responsibilities include model development, data pipeline construction, cloud and distributed systems operation, Kubernetes-based deployment, CI/CD, model governance, responsible AI, testing, monitoring, and collaboration with product and data science teams.

Build, deploy, optimize, and maintain production machine learning models and infrastructure. Responsibilities include developing ML applications, designing data pipelines, operating distributed systems and cloud platforms, monitoring and retraining models, automating testing and deployment, and applying responsible AI practices. The role collaborates with Product and Data Science teams to deliver scalable solutions using frameworks such as PyTorch or TensorFlow and platforms including AWS, Kubernetes, Spark, and Ray.

Design, build, deploy, retrain, and monitor production machine learning models and components. Develop scalable ML infrastructure, data pipelines, cloud architectures, and distributed systems using Python and related technologies. Collaborate with Product and Data Science teams, automate testing and deployment, apply CI/CD practices, and ensure model governance, security, responsible AI, and explainability across enterprise platforms.

Design, deploy, and scale responsible AI and machine learning solutions supporting healthcare provider experiences. Lead AI initiatives, provide technical direction to AI engineers, evaluate emerging technologies, and partner with product, data, and business teams. Ensure models meet enterprise standards for performance, security, governance, and scalability while contributing to cloud modernization. The role emphasizes generative AI, LLMs, conversational AI, API development, cloud hosting, and production operations.

Leads enterprise AI/ML architecture and engineering strategy, developing scalable AI-powered applications, agentic and generative AI solutions, streaming data pipelines, and cloud-native platforms. The role uses Python, Java, Spring Boot, Next.js, Kafka, and PostgreSQL while establishing architecture standards, governance, security, and reliability practices. Responsibilities include technical leadership across programs, stakeholder influence, mentoring engineering teams, evaluating emerging technologies, and driving responsible AI adoption.

Develops and deploys advanced machine learning, deep learning, computer vision, and AI models for defense and government missions. Responsibilities include data analysis, model architecture and training, predictive forecasting, anomaly detection, model assessment, and integration with simulation tools. Serves as a technical lead on complex initiatives, influences strategy, briefs senior leaders, and mentors technical staff. Requires expertise in transformers, LLM prompting, unsupervised learning, and deep neural networks.

Build and deploy AI and machine-learning capabilities for cyber threat hunting, anomaly detection, incident response, detection engineering, and security automation. Develop custom models, LLM applications, and agentic systems, managing the full production lifecycle from data preparation through monitoring. Partner with security, engineering, offensive security, and governance teams to evaluate emerging technologies, support responsible AI, advise leadership, and mentor technical staff.

Build and deploy AI agents for logistics and transportation across email, phone, and SMS. Improve model reasoning, fine-tune foundational models, integrate AI tools, and create evaluation frameworks for agent performance. Collaborate with engineering, product, and logistics teams to solve complex operational problems. The role requires Python, cloud AI deployment, model optimization, and familiarity with TensorFlow or PyTorch. A bachelor’s degree in a related field is required.

Leads rapid design, prototyping, deployment, and operationalization of AI/ML proof-of-concepts for Intelligence Community missions. Builds GenAI, NLP, computer vision, and predictive analytics solutions; integrates MLOps pipelines into secure cloud and air-gapped environments; coordinates delivery across multiple programs; and presents demonstrations to senior stakeholders. Requires advanced software engineering, AI/ML frameworks, secure deployment experience, and an active Top Secret clearance with eligibility for TS/SCI.

Develop and evaluate machine learning algorithms for satellite recognition, tracking, sensor scheduling, anomaly detection, prediction, classification, clustering, and optimization. Perform exploratory data analysis, build scalable HPC pipelines, conduct space-related engineering analyses, validate external algorithms, collaborate with satellite and astrodynamics experts, and present technical findings to customers and stakeholders.

Develop and implement AI/ML algorithms across computer vision, natural language processing, time series, graph data, reinforcement learning, and large language models. Analyze large datasets, apply research publications, document technical solutions, and understand AWS-based training and deployment pipelines. Mentor team members and collaborate within interdisciplinary research teams.

Build and own a new AI product and its production infrastructure. Responsibilities include designing validated AI agent systems, secure enterprise software connectors, developer tooling, code analysis capabilities, low-latency model serving, monitoring, retrieval and language-model pipelines, and model tuning. The engineer will work directly with customers, ship production code, establish engineering standards, and influence team culture and product direction.