Top Machine Learning Engineer Jobs

Lead the design, development, and deployment of scalable machine learning and generative AI solutions. Partner with stakeholders, data scientists, and software engineers to build AI platforms, tooling, agents, and autonomous workflows. Analyze large datasets, optimize and fine-tune models, apply emerging AI technologies, and establish reusable capabilities and technical standards across the organization.

Develop, deploy, and optimize scalable machine learning solutions, including predictive models, deep learning systems, data pipelines, and model-serving infrastructure. Monitor model performance, manage retraining and lifecycle processes, ensure quality and compliance, and support responsible AI practices. Collaborate with data scientists, engineers, product teams, and business stakeholders while researching emerging AI technologies and improving AI architectures.

Leads the architecture, development, deployment, and optimization of enterprise machine learning solutions. Responsibilities include designing scalable ML systems, developing predictive models, supporting MLOps and model lifecycle management, improving data quality and feature engineering, implementing governance and validation frameworks, and mentoring technical teams. The role also contributes to AI strategy, innovation initiatives, responsible AI practices, and organizational modernization.

Design, implement, and scale end-to-end retrieval-augmented generation (RAG) pipelines. Build and optimize retrieval systems, embeddings, vector search, and LLM orchestration. Collaborate with product, design, and MLOps to productionize robust ML systems across large proprietary datasets.

Design, build, and scale production RAG systems and retrieval pipelines integrating chunking, embeddings, vector databases, and LLM orchestration. Optimize vector search, indexing, and evaluation; collaborate with product, design, and MLOps to deploy and maintain end-to-end ML solutions.

Develop and deploy machine learning models for wafer fabrication yield improvement, defect screening, and process optimization. Integrate AI/ML pipelines with manufacturing and MES systems using edge, cloud, API, and container technologies. Build data pipelines, dashboards, monitoring tools, and analytical methods while supporting experiments and continuous improvement. Collaborate with photonics, manufacturing, reliability, and software engineers, establish ML best practices, and communicate results across sites.

Develop and maintain AI applications and technical solutions, build tools for analyzing large-scale data, troubleshoot and test software, and create technical documentation and user support guides. The role collaborates with developers, staff, leadership, and engineering teams to solve complex customer problems. Candidates need an active Top Secret clearance, a bachelor’s degree, five or more years of relevant experience, object-oriented programming expertise, and proficiency with modern LLM tools.

Design and develop a high-performance, multi-tenant ML training platform for autonomous vehicle systems. Responsibilities include building orchestration, control plane, observability, data and model pipelines, performance optimization, profiling tools, and scalable infrastructure. The role provides technical direction, collaborates across ML, infrastructure, autonomy, and safety teams, and promotes engineering excellence and reusable tooling for machine learning engineers.

Develop and maintain AI applications and technical solutions alongside engineering teams. Build tools for analyzing large-scale data, debug and test software, optimize performance, and create technical documentation and user support guides. Collaborate with staff and leadership to solve complex customer problems and leverage modern LLM tools to improve development workflows and code quality.

Develop machine learning solutions that improve wireless communication systems using RF signal processing, software-defined radio data, probability, statistics, and AI. Build scalable, high-throughput systems for edge devices and data centers, collaborate with designers, infrastructure engineers, and research scientists, integrate ML algorithms into prototypes and products, and apply current research to wireless and embedded systems.

Develop and deploy geospatial AI/ML capabilities for DoD and intelligence customers. Apply machine learning, data analytics, computational modeling, clustering, and statistical methods to structured and unstructured data to generate predictive and prescriptive intelligence insights. Integrate diverse data sources, optimize analytical workflows, support operational missions, lead projects, and translate research and commercial practices into actionable solutions for intelligence analysis and resource allocation.

Design and deploy machine learning models for creating HD maps, utilizing advanced ML techniques and collaborating with other teams to deliver reliable solutions.

Own the full machine learning lifecycle for search ranking and retrieval, including model training, deployment, serving, feature pipelines, evaluation frameworks, hybrid lexical-vector retrieval, large-scale embedding inference, query understanding, and permissions-aware multi-tenant search. Collaborate with infrastructure, AI, and backend teams to improve search relevance across ClickUp’s platform.

Build and productionize machine learning systems that convert complex documents into executable, verifiable logic. Responsibilities include developing parsing and transformation pipelines, experimenting with language models, designing datasets and evaluation metrics, creating adversarial tests, analyzing model failures, and implementing rule-based checks using logical reasoning and program analysis. The role requires close collaboration with founders and subject-matter experts to validate system outputs.

Design, train, deploy, and operate machine learning systems for real-time suspicious-activity detection. Own model development, data pipelines, serving, infrastructure, scalability, and production measurement. Collaborate with technical leadership and customers to identify threats and improve detection signals. Establish engineering standards, ensure correctness, and deliver reliable systems in a high-autonomy, fast-moving team.

Drive applied research to enable continuous learning for agents: design and evaluate LLM post-training methods (fine-tuning, RL, distillation), run large-scale GPU experiments, validate approaches on customer tasks, and help deploy and scale models and infrastructure across the stack.

Build and operate production machine learning systems and MLOps infrastructure for financial services. Responsibilities include developing feature and training pipelines, model-serving systems, CI/CD, automated retraining, monitoring, alerting, observability, and progressive model rollouts in GCP and Kubernetes. The role partners with data scientists and business teams to improve reliability, performance, scalability, cost efficiency, and deployment velocity while applying AI coding agents to infrastructure and ML workflows.

Design, train, and deploy machine learning models and perception software for autonomous aircraft. Develop real-time AI algorithms using deep learning, 3D computer vision, and sensor fusion for navigation and detect-and-avoid systems. Build model training, evaluation, data management, simulation, and analysis tooling while collaborating with software, controls, sensing, and flight engineering teams. Optimize production autonomy systems and mentor engineers through strong software practices.

Set technical vision for Pinterest’s Product ML Infrastructure, leading distributed model training, fine-tuning, evaluation, and high-scale CPU/GPU inference. Improve GPU efficiency, data loading, execution, kernels, memory, compilation, quantization, scheduling, and capacity. Build reliable, observable training and serving platforms, drive architecture decisions and migrations, partner with AI/ML teams, mentor senior engineers, and establish operational standards across the organization.

Develop software and applied RF machine learning algorithms for wireless systems. Responsibilities include SDR prototyping, signal processing, software integration, database interfaces, data workflows, testing, documentation, technical presentations, and research-to-prototype delivery. The role collaborates with researchers and engineers in a customer-facing, classified environment and may lead research activities.

Develop and operationalize AI/ML capabilities for secure Department of Defense mission systems. Responsibilities include building agentic workflows, production APIs, MLOps pipelines, model validation and monitoring processes, and integrations with MSS Foundry and Databricks. The role requires production AI/ML deployment experience, Python proficiency, lifecycle management expertise, and collaboration with engineering and mission teams. Candidates must hold an active TS/SCI clearance and have at least five years of relevant experience.

Own the end-to-end machine learning lifecycle, including data preparation, dbt pipelines, feature engineering, model development, deployment, monitoring, and evaluation. Apply ML to risk assessment, cost prediction, image analysis, and business operations. Contribute to architecture and strategy while partnering with Engineering, Product, Operations, and Business teams. Build scalable solutions using Python, AWS, Kafka, Django or FastAPI, PostgreSQL, and GitHub Actions.

Develops advanced machine learning, deep learning, signal processing, optimization, and numerical modeling algorithms for biomedical signals and next-generation medical devices. Responsibilities include algorithm design, implementation, testing, documentation, data analysis, disease detection and prediction modeling, patentable innovation, and collaboration with hardware and software engineering teams.

The Staff Machine Learning Engineer will lead the development of AI solutions, ensure project delivery, and manage technical teams while interfacing with customers. Responsibilities include designing data pipelines, overseeing machine learning model development, and facilitating customer engagement.

Build and deploy production machine learning systems across computer vision, multimodal modeling, structured data, and agentic AI. Own projects from problem definition through monitoring, develop data and evaluation pipelines, establish feedback loops, and translate experiments into practical technical recommendations. Collaborate with product, engineering, operations, annotation teams, and domain experts while delivering maintainable code, documentation, and scalable ML solutions.