Top Machine Learning Jobs

Develop and scale machine learning recommendation systems for Xumo streaming products. Responsibilities include feature engineering, big-data analysis, model training and deployment, offline evaluation, online A/B testing, data pipeline management, model serving optimization, technical documentation, proofs of concept, and cross-functional collaboration. The role also mentors junior engineers and contributes to patents and APIs.

Leads the design, development, deployment, and measurement of large-scale machine learning, information retrieval, LLM, and agentic AI solutions. Defines scientific strategy, technical roadmaps, experimentation frameworks, and evaluation methods for web content quality, trust, safety, and understanding. Partners across engineering, product, and research teams, mentors scientists, influences organizational direction, and develops systems addressing spam, abuse, fraud, and low-quality AI-generated content.

Develop and optimize AI/ML algorithms and scalable solutions addressing Trust and Safety challenges across Zoom. Collaborate with researchers and cross-functional teams to translate research into reliable engineering systems, participate in code reviews and technical discussions, improve system performance and scalability, and apply emerging AI/ML advances. The hybrid role requires onsite work four days per week in San Jose, California.

Develop and integrate AI/ML solutions for geospatial intelligence, including spatio-temporal modeling, data fusion, semantic enrichment, trajectory analytics, and remote sensing. Engineer multimodal data pipelines, evaluate models, prototype applications, and implement solutions across hardware and software stacks. Provide technical expertise, mentorship, strategic guidance, and client communication for government mission programs.

Support USASOC data science efforts by developing data-driven and predictive ML solutions, evaluating data sources, building APIs and models, monitoring model performance, collaborating with stakeholders, and mentoring junior staff. Work is on-site at Fort Bragg and requires TS/SCI clearance and 5-8 years of experience.

Design and build realistic simulated workplace environments, evaluation tasks, benchmarks, graders, and scoring systems for training and assessing AI models. Generate synthetic records, files, and communications; prevent models from exploiting evaluation systems; align benchmarks with real-world performance; and convert user feedback into new test cases. The role is early-career and emphasizes reinforcement learning or model post-training experience.

Develops predictive models and data-driven solutions by analyzing complex datasets and applying advanced statistical and machine learning techniques. The role communicates insights to stakeholders, collaborates with cross-functional teams, and integrates analytics into business strategy. It requires extensive data analysis experience, an active TS/SCI clearance with polygraph, and U.S. citizenship. The position is onsite in Herndon, Virginia, with less than 10% travel.

Develops and adapts large language models and NLP methodologies to create intelligence-analysis capabilities. Responsibilities include building, tuning, evaluating, and deploying AI/ML models; preparing data; using deep learning frameworks and GPUs; developing semantic search and text-focused solutions; and communicating modeling results. The role requires strong Python or R, SQL, version control, cloud, and machine learning experience, along with an active TS/SCI clearance with polygraph and onsite work in McLean, Virginia.

Develop and operate production machine learning systems for search and advertising, including relevance, yield optimization, bidding, ranking, recommendation, and ad selection. Own the ML lifecycle from data acquisition and prototyping through deployment, monitoring, optimization, and maintenance. Build low-latency, high-throughput services, feature pipelines, MLOps frameworks, experimentation platforms, and scalable microservices using modern machine learning, deep learning, and reinforcement learning techniques.

Leads machine learning and software engineering teams building models, agentic systems, data pipelines, and ML infrastructure for advertising integrity and recommendations. Owns technical strategy, roadmap, model quality, reliability, privacy, and responsible AI. Partners cross-functionally to deliver measurable outcomes, scales LLM-powered ad review, contributes technically, and hires and mentors high-performing engineers.

Leads AI and automation strategy across Finance, including machine learning proof-of-concepts, model review, MLOps deployment, adoption, training, governance, and enterprise scaling. Manages and mentors data scientists, partners with technology teams, develops AI roadmaps, supports data architecture, and drives change management and stakeholder adoption. The role requires strong financial acumen, hands-on machine learning and MLOps experience, enterprise data strategy knowledge, and at least eight years of progressive experience.

Summer interns will build and test AI solutions, LLM applications, NLP tools, machine learning systems, data pipelines, and semantic data models. Responsibilities include evaluating AI models, automating workflows, applying software and data engineering practices, and collaborating with scientists and business teams. Interns will use responsible AI practices and contribute to real-world systems supporting medicine development.

Develop and scale AI/ML platform services supporting model training, serving, and management. Build microservices and paved-path solutions for data scientists and ML engineers, evaluate platform capabilities, and support personalization, recommendation, search, and other machine learning use cases. Collaborate with engineering, data science, product, and agent development teams while contributing to architecture, technical design, code reviews, and improved development practices.

Three-month R&D internship combining formulation chemistry, machine learning, data analysis, DOE design, and laboratory automation. The intern will clean historical formulation data, build predictive models, optimize candidate recipes, create dashboards, design experiments, configure LIMS and ELN workflows, connect laboratory work orders, and organize technical knowledge repositories. The role requires a scientific or engineering degree in progress, Python proficiency, DOE tools, chemistry and materials knowledge, and strong communication skills.

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.

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.

Develop, test, deploy, and serve machine learning models and agentic AI capabilities. Build feature engineering, experimentation, training, monitoring, and REST-based model-serving workflows. Collaborate with data science, engineering, product, and MLOps teams to productionize AI solutions at enterprise scale using distributed computing, Databricks, cloud platforms, and containerized infrastructure. Support model performance improvements, generative AI initiatives, and urgent IT escalations.

Build and lead production AI/ML, Generative AI, and data engineering solutions for healthcare analytics and patient safety platforms. Develop full-stack applications, microservices, APIs, data pipelines, dashboards, self-service analytics, RAG systems, and event-driven architectures using cloud-native technologies. Apply engineering best practices including testing, CI/CD, observability, security, and performance optimization. Mentor engineers, lead technical initiatives, and collaborate with product, analytics, business, and platform teams.

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 the development and global scaling of Fuelight, an AI-enabled resource allocation and investment optimization product. Responsibilities include building production machine learning applications, MLOps capabilities, scalable architectures, decision-support experiences, explainable AI features, and integrations with data and optimization products. Partners with engineering, data science, product, and business stakeholders while establishing engineering standards and ensuring cybersecurity, privacy, responsible AI, and governance compliance.

Lead client-facing data science work: design and develop ML/AI solutions, build and monitor operational data pipelines, query/migrate data via APIs, use SQL/NoSQL and cloud architectures, guide teammates, and translate complex data into actionable insights. Requires TS/SCI with polygraph and extensive data science experience.

Develop and refine machine learning models using Python, analyze datasets with MongoDB, and integrate preprocessing and data pipelines. Evaluate models, tune hyperparameters, benchmark performance, document experiments, and deliver data-driven recommendations. Collaborate remotely with cross-functional contributors to improve and operationalize machine learning solutions in cloud or enterprise environments.

Design and build reinforcement learning environments, evaluation systems, reward functions, datasets, data pipelines, and ML infrastructure for computer-aided design tasks. The role converts expert mechanical and electrical engineering data into reliable model-training assets, automates scoring and content workflows, and runs experiments guiding technical and product decisions. Candidates need machine learning experience, physical-engineering domain exposure, engineering design software or vision-based ML familiarity, and hands-on reinforcement learning environment experience.

Leads the engineering team responsible for autonomous-vehicle trajectory and intent prediction systems. The role guides machine learning and classical robotics architecture, integrates safety metrics and operational design requirements, manages roadmaps and cross-functional delivery, supports root-cause analysis, enforces engineering quality, and develops technical talent. It also requires dashboard and KPI ownership, fleet-data analysis, and collaboration across autonomy, infrastructure, perception, modeling, and remote operations teams.

Build and evaluate secure, robust machine learning systems for government sponsors. Responsibilities include researching AI vulnerabilities, developing adversarial ML defenses, designing prototypes, experimenting with ML frameworks and algorithms, testing system performance and security, transitioning capabilities to sponsors, collaborating across technical teams, and mentoring engineers. The role requires onsite work in Pittsburgh or Arlington, up to 25% travel, and an active Department of War security clearance.