Top Machine Learning Engineer Jobs

Develop and sustain cloud software supporting cyber intelligence and AI/ML capabilities on AWS and large-scale compute clusters. Responsibilities include requirements analysis, Java software development, cloud integration, testing, troubleshooting, RESTful services, distributed systems, big-data technologies, Linux development, and technical documentation. The role also involves LLM, RAG, agentic AI, Kubernetes, infrastructure-as-code, CI/CD, and cloud security practices. An active TS/SCI clearance with polygraph is required.

Develop and sustain cloud software supporting cyber intelligence and AI/ML capabilities on large-scale compute clusters and AWS infrastructure. Responsibilities include requirements analysis, software development, integration, testing, validation, deployment, maintenance, monitoring, and issue resolution. The role uses distributed NoSQL technologies, big-data frameworks, cloud services, AI/ML algorithms, LLMs, RAG, and agentic workflows to support national security missions.

Designs AI/ML data-fusion and sense-making solutions for intelligence and space missions. Develops architectures, testing and validation frameworks, responsible AI practices, adoption roadmaps, and mission applications across diverse data modalities. Collaborates with business development and program teams, mentors technical staff, and communicates AI capabilities and limitations to leadership and customers. Requires defense or intelligence experience, Secret clearance with ability to obtain TS/SCI, and full-time onsite work.

Develops and leads systems engineering activities for cyber intelligence and AI/ML capabilities hosted on large-scale compute clusters and AWS Cloud. Responsibilities include requirements analysis, system architecture and design, interface definition, technical documentation, risk and configuration management, systems integration and testing, verification planning, and security and accreditation support. The role provides technical leadership across integrated product teams and coordinates with government stakeholders to ensure system technical integrity.

Develop and maintain cloud software supporting AI/ML and national security missions. Responsibilities include requirements analysis, software development, AWS cloud integration, distributed systems engineering, microservices, testing, troubleshooting, and sustainment. The role uses Java, Linux, Kubernetes, infrastructure-as-code, CI/CD, big-data technologies, RESTful services, and AI/ML frameworks including LLMs and RAG. An active TS/SCI clearance with polygraph is required.

Develop cloud software and AI/ML capabilities on AWS and large-scale compute clusters. Responsibilities include requirements analysis, Java software development, RESTful services, distributed systems, cloud integration, infrastructure as code, containerization, testing, sustainment, and issue resolution. The role requires experience with Hadoop and big-data technologies, Linux, software integration, technical documentation, and secure solutions supporting national security missions.

Develop and integrate cloud software supporting cyber intelligence and AI/ML capabilities on AWS and large-scale compute clusters. Responsibilities include requirements analysis, Java development, RESTful services, distributed systems, cloud deployment, DevOps, container orchestration, testing, integration, Linux scripting, and troubleshooting. The role also involves big-data technologies, Hadoop ecosystems, LLMs, RAG, analytics, and secure software engineering for national security missions.

Develop and sustain cloud software supporting cyber intelligence and AI/ML capabilities on large-scale compute clusters and AWS infrastructure. Responsibilities include requirements analysis, software development, integration, testing, validation, deployment, enhancement, monitoring, and issue resolution. The role uses distributed NoSQL and big-data technologies, cloud architecture, AI/ML tools, microservices, containers, and infrastructure-as-code. Candidates must have at least two years of relevant software engineering experience and an active TS/SCI clearance with polygraph.

Develop and sustain cloud software supporting cyber intelligence and AI/ML capabilities on large-scale compute clusters and AWS infrastructure. Responsibilities include requirements analysis, software development, integration, testing, validation, deployment, enhancement, maintenance, and issue resolution. The role involves distributed NoSQL systems, big-data technologies, cloud scalability, AI/ML algorithms, LLMs, RAG, agentic workflows, and software testing within a mission-driven national security environment.

Designs, develops, tests, deploys, monitors, and continuously improves machine learning models supporting business and national security mission requirements. Integrates data pipelines, evaluates model performance, ensures data integrity, and transforms prototypes into scalable production systems. Requires expertise in machine learning frameworks, data engineering, software architecture, statistical analysis, and model validation. The role may provide technical leadership for distributed teams and requires an active TS/SCI clearance with polygraph.

Develop machine learning, statistical, data mining, graph-based, and heuristic algorithms for national security applications. Build and optimize AI/ML and large language models, including information retrieval workflows. Analyze varied datasets, create feature vectors, design experiments, evaluate models using standard metrics, produce visualizations, and translate subject matter expertise into production analytics. Guide analytic development efforts and integrate prototype algorithms into analyst workflows.

Design, develop, test, deploy, and operationalize AI/ML algorithms for mission and business requirements. Build scalable production models, integrate data pipelines, evaluate model performance, and manage training, retraining, monitoring, and continuous improvement. Apply statistical analysis, data governance, software architecture, and data modeling principles. Support system integration and verification/validation activities, with potential leadership of distributed teams and technical customer engagement.

Designs, develops, tests, deploys, and improves machine learning models for business and mission requirements. Responsibilities include selecting datasets, performing statistical analysis, integrating data pipelines, training and retraining models, monitoring performance, applying evaluation protocols, and overseeing system integration. The role requires knowledge of model architecture, infrastructure, data engineering, governance, and scalable AI/ML solutions, with potential leadership of distributed teams and customer engagement.

Develops machine learning, statistical, data mining, graph-based, and large language models for national security applications. Analyzes datasets, creates feature vectors, prototypes and validates algorithms, builds experiments, generates visualizations, and implements analytics in production frameworks. The role collaborates with subject matter experts, optimizes GPU-based model training, supports LLM and RAG solutions, and guides analytic development efforts. An active TS/SCI clearance with polygraph is required.

Develop, fine-tune, and deploy visual and language foundation models (e.g., SAM, DINO) to robots in the field; build cloud-based training and inference infrastructure; manage and optimize robot data flows and databases; contribute across the robotics stack including web UI and full-stack deployment.

Design and evaluate memory architectures for foundation models across modalities. Build production-ready, scalable learning models, define memory metrics, collaborate with researchers, and recommend roadmap improvements for long-horizon behavior and efficient storage/retrieval.

Build and productionize ML systems for de-identification: improve NER, entity resolution, extraction, and document understanding. Drive error analysis, dataset creation, experiments, evaluation, safe rollouts, inference optimization, and active-learning/LLM-assisted pipelines to improve precision, recall, and utility in live pipelines.

Build and operate Mercury’s machine learning platform, including low-latency inference services, model deployment infrastructure, CI/CD, staged rollouts, observability, drift detection, retraining triggers, and explainability capabilities. Partner with data science teams to move models into reliable production operation, while shaping a new platform team supporting fraud and financial crime decisioning.

Build, optimize, and deploy production-grade machine learning systems for sports analytics. Improve data pipelines, feature engineering, model training, evaluation, and low-latency prediction services. Collaborate with DevOps and Data Engineering to scale workloads on Kubernetes, maintain cloud-native EDW/ETL solutions, and promote software development best practices.

Design, build, and deploy machine learning models for a payments platform, overseeing end-to-end solutions and experimentation, while monitoring models in production.

This role involves bridging research and applied engineering, overseeing training infrastructure, optimizing workflows, and setting technical strategies for AI/ML deployment at zaimler.

As a Machine Learning Engineer, you will manage multimodal perception, work on data collection, and evaluate robotics tasks, directly impacting model capabilities and the evolution of physical labor.

Develop and maintain LLM pipelines, design RAG architectures, implement agentic systems, and integrate AI features into data pipelines.

The Machine Learning Engineer will build tools for interpretable AI, optimize infrastructure, and integrate workflows into products, ensuring reliability and performance.

As a Machine Learning Engineer, you will fine-tune transformer models, deploy machine learning solutions, and work with cross-functional teams on innovative projects.