Top Machine Learning Jobs

Lead the architecture and production deployment of agentic AI systems for classified and mission-critical government environments. Build systems for geospatial reasoning, retrieval, embeddings, persistent memory, orchestration, evaluation, and reliability. Own shared ML infrastructure, define technical direction, partner across engineering and research teams, mentor engineers, and drive ambiguous initiatives from prototype to supported production capability.

The Research Engineer will develop and deploy machine learning applications focused on severe weather, collaborating on system processes, and maintaining production environments.

Develop and deploy computer vision, reinforcement learning, and multimodal machine learning systems for biomedical imaging and pathology research. Train and benchmark models, improve computational workflows, and support closed-loop microscopy experiments. Collaborate with biologists, imaging scientists, and engineers, contribute to manuscripts and grant proposals, and communicate technical concepts to nontechnical stakeholders. The role focuses on applying AI to neurodegeneration and other disease-related research.

Ph.D. interns apply machine learning and AI to semiconductor chip design or enterprise engineering tools. Responsibilities include developing ML models for EDA automation, placement, timing, power, and verification, or deploying LLM, RAG, and agentic systems. Interns will train, evaluate, and deploy models, conduct rigorous experiments, collaborate with engineering and technology stakeholders, assess production performance and safety, and present findings to leadership. The role requires doctoral research in AI, machine learning, computer science, electrical engineering, data science, or a related field.

Research Engineer responsible for developing and productionizing post-training methods for diffusion and flow models, including reward modeling, preference optimization, supervised fine-tuning, distillation, and reinforcement learning. The role owns research planning, evaluation standards, training pipelines, and the feedback loop between model behavior and product data. Models will be deployed to professional designers, requiring rigorous engineering, reproducible evaluations, and close collaboration with product and founders.

Designs and develops secure, production-grade AI solutions for government patent-data workflows. Responsibilities include building generative AI applications, LLM and RAG capabilities, reusable services, APIs, evaluation frameworks, and integrations. Provides architecture guidance, establishes responsible-AI controls, evaluates technical alternatives, mentors engineering teams, and supports government demonstrations, proposals, and stakeholder engagements. The role grows toward end-to-end AI architecture and technical strategy ownership.

Conduct machine learning research operations using existing infrastructure. Characterize neural network quality, failure modes, and edge cases; coordinate experiments with researchers; manage concurrent experiments and deadlines; review results; recommend theoretical or process improvements; and prepare technical reports communicating qualitative and quantitative findings.

Build and maintain scalable data platforms and pipelines that integrate structured and unstructured data. Retrieve, merge, and analyze data from APIs and databases using Python, R, and SQL. Prepare datasets for AI and generative AI models, evaluate machine learning performance, and guide multidisciplinary teams in agile client projects supporting fraud detection, cancer research, and national intelligence missions.

Build and deploy scalable machine learning models and pipelines, integrate ML into products, and develop robust systems using Python, FastAPI, React, and AWS. Lead technical design, code and architecture reviews, agile ceremonies, incident response, and reliability practices. Mentor engineers, improve testing and CI/CD, drive cross-functional alignment, and establish high standards for software quality, documentation, observability, and maintainability.

Owns the architecture, development, evaluation, and production deployment of generative AI, agentic AI, computer vision, and reinforcement learning systems for mission-critical government applications. Responsibilities include building scalable ML infrastructure, improving models through tuning and retraining, developing evaluation benchmarks, partnering with users and research teams, mentoring engineers, and designing for classified and resource-constrained environments.

Leads enterprise AI/ML strategy, architecture, development, deployment, and productization for mission-critical initiatives. Builds and mentors engineering teams, advises customers, develops scalable machine learning solutions, and transforms prototypes into secure production systems. Oversees cloud-native architectures, MLOps, CI/CD pipelines, enterprise integration, and model lifecycle management. Supports NLP, predictive analytics, anomaly detection, computer vision, and generative AI while engaging customer and technical leadership.

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.

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.

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.

Architects enterprise AI/ML systems, including training pipelines, inference services, feature stores, data ingestion, and MLOps frameworks. Leads multidisciplinary engineering teams, defines technical roadmaps, oversees execution, and interfaces with senior government stakeholders. Establishes secure engineering, DevSecOps, testing, and model governance practices while guiding cloud-native integrations, advanced ML tool adoption, responsible AI, architecture reviews, and strategic planning for Defense Intelligence Agency missions.

Join Hiring.Cafe as a Founding Machine Learning/AI Search Engineer to develop AI recommender systems and scalable infrastructure, contributing to a transformative job search engine.

The Senior Analyst will develop AI and ML algorithms, optimize data systems, and present research findings. Requires extensive experience in model development, system architecture, and MLOps.

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.

Build and operate scalable ML inference infrastructure powering Claude’s safety systems. Responsibilities include productionizing safety research, optimizing real-time inference latency and throughput, developing monitoring and observability tools, implementing testing and deployment automation, and supporting reliable classifier rollouts. The role requires collaboration with researchers, inference engineers, security teams, and alignment teams to deliver high-throughput, low-latency, safety-critical ML systems.

Develops AI/ML prototypes, APIs, microservices, integrations, and lightweight user interfaces for federal and DoD-aligned mission workflows. The role integrates and optimizes machine learning models for dashboards and applications, supports data ingestion and preprocessing, validates performance, and helps transition models into production. Collaboration with data scientists, architects, developers, PMO leadership, and cybersecurity teams is required. The position requires five years of relevant experience, a bachelor’s degree, ITIL v4 Foundation certification, and Public Trust or active Secret clearance.

Designs AI/ML pilots, predictive analytics and anomaly detection models, behavioral analytics, content classification, personalization algorithms, and end-to-end machine learning pipelines. Develops prototypes and production-ready solutions, evaluates AI technologies, supports experimentation, integrates ML insights into dashboards, and ensures model governance, explainability, bias mitigation, accuracy, and security compliance in federal and DoD-aligned environments.

Develop and productionize machine learning and hybrid physics-ML methods for seismic imaging, inversion, and velocity model building. The role applies deep learning, generative models, neural operators, and physics-informed approaches to large seismic datasets using GPU and cloud infrastructure. Responsibilities include validating methods against physics-based approaches, quantifying uncertainty, documenting reproducible workflows, and communicating results to geophysicists, data scientists, and asset teams.

Design, develop, deploy, and iterate production machine learning and generative AI solutions. Responsibilities include model development, NLP, prediction, anomaly detection, LLM and RAG integration, data pipelines, feature engineering, evaluation, monitoring, API integration, and architecture decisions. The role collaborates cross-functionally to balance accuracy, latency, cost, scalability, and maintainability.

Design and implement ML-based prototypes, tools, and systems for AI security. Collaborate with researchers to execute experiments, analyze large cybersecurity datasets, support adversarial ML and red teaming, and translate research into deployable, maintainable software solutions.

Develops and deploys AI and machine learning solutions for intelligence and cyber workflows in classified environments. Responsibilities include building LLM-powered workflows and agent-based automation, integrating AI into operational systems, developing data pipelines, evaluating models, optimizing workflows, and conducting operational testing. The role collaborates with mission users, analysts, software developers, and leadership to deliver reliable, scalable, secure, and production-ready AI capabilities.