Top Machine Learning Jobs

Develop, evaluate, and deploy AI/ML models for national security applications. Analyze complex datasets, build scalable backend services and APIs, create visualizations and UIs, and collaborate with researchers, engineers, and customers to transition prototypes into production across cloud, on-prem, and edge environments.

Contribute to development, deployment, and monitoring of production ML models; perform data cleaning, analysis, and visualization; collaborate with cross-functional teams to deliver AI-driven business solutions and communicate insights to stakeholders.

Lead development of data infrastructure, analytics platforms, and data products; deploy predictive and diagnostic models (ML/AI); drive consumer, marketing, business, and operational insights; manage large global teams; partner with IT and cross-functional leaders to inform product, sales, and marketing strategy and enable data-driven decision making.

Develop and scale machine learning training systems for robotics data, optimize training pipelines, and collaborate with ML researchers to enhance autonomy models.

Embed with clinical and operations teams to identify high-impact problems and build production AI systems. Design and deploy LLM/RAG-driven tools, data pipelines, APIs, models, and user-facing applications using messy clinical and claims data. Iterate quickly based on real-world feedback and operational needs.

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.

Perform data exploration, cleaning, analysis, and visualization on structured and unstructured data. Develop predictive models and machine learning algorithms, apply distributed/data-processing tools, collaborate with clients, and lead solution development. Support deliverables, travel up to 25%, and obtain required security clearance.

Lead design, development, and deployment of end-to-end recommendation systems. Build, tune, and evaluate scalable recommendation algorithms, integrate ML into products, lead projects, mentor teams, and communicate solutions to clients and stakeholders while driving innovation in replenishment and supply chain analytics.

Design and deliver end-to-end data science solutions (propensity, forecasting, recommenders). Build production-grade ML models, perform feature engineering and data transformation, collaborate with data engineering and MLOps to operationalize and monitor models, and engage business stakeholders to iterate and validate solutions.

Senior individual contributor responsible for leading pet/P&C insurance pricing model development (GLMs and ML), owning end-to-end projects, performing EDA and root-cause analysis, communicating results to stakeholders, and mentoring junior team members.

Lead and grow a team of ML engineers building applications for a graphically intensive simulation platform. Manage stakeholders, translate customer needs into technical requirements, prioritize between off-the-shelf, open-source, and greenfield solutions, coordinate cross-functional delivery, and oversee the full ML lifecycle from data infrastructure to deployment. Foster team ways of working and performance.

The role involves leading ML project implementations, designing production-grade ML solutions, and optimizing AI applications for clients while ensuring effective communication and project management.

The Machine Learning Engineer will propose technical initiatives, lead design discussions, translate challenges into plans, and uphold engineering standards.

The Applied Scientist will transform raw data into actionable insights by developing machine learning models for personalization, ranking, and optimization to enhance conversion and revenue. This role emphasizes building production systems, frameworks for experimentation, and communication with team members regarding technical challenges.

Develop and maintain geometry processing, simulation, and rendering algorithms; build services to read/process 2D/3D engineering data; curate large datasets; implement ML post-training workflows; and contribute to domain-specific languages for engineering.

Embed with clients to gather requirements and design AI-driven solutions for fraud and fincrime. Architect, train, evaluate, and deploy custom AI agents and workflows, optimize model performance, establish risk metrics and evaluation pipelines, and collaborate with engineering/product teams to scale into production.

The Senior Manager for Machine Learning will lead the ML team, coordinate initiatives, and enhance product development in a collaborative environment.

As a Senior Machine Learning Engineer, you will own ML infrastructure and data systems, scaling them for large volumes of video data and supporting deep learning training and experimentation.

As a Robotics ML Research Engineer, you will develop algorithms for robot manipulation, lead software infrastructure, and innovate in robotics learning research.

Develop and maintain a full-stack platform for computational chemists, focusing on drug discovery through machine learning and data infrastructure. Collaborate with interdisciplinary teams to enhance software tools for visualizing molecules and managing workflows.

Lead end-to-end research applying advanced ML/DL (transformers, GNNs, RL, ensembles) to generate alpha across digital asset markets, build production-grade pipelines, prevent leakage/overfitting, analyze microstructure and on-chain data, and mentor junior researchers.

As Director of AI/ML, lead the ML organization, set technical vision, mentor a high-performing team, and contribute to model development and production deployment.

As a Software Engineer intern, you'll work on AI-driven projects spanning data management, onboard systems, ML infrastructure, simulations, and technical infrastructure, collaborating with experts in various fields.

Build and deploy machine learning solutions (recommendation systems, NLP, time series, optimization), collaborate with stakeholders and engineers, set technical strategy for data-science products, run A/B tests, and communicate results to drive business impact.

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.