Top AI & Machine Learning Jobs

Build LLM-powered research and analysis products for political strategists. Partner with users to shape workflows, define quality standards, design evaluations and LLM-as-judge systems, prototype features, and communicate technical tradeoffs to technical and nontechnical stakeholders. The role requires strong Python, SQL, statistical and machine learning skills, hands-on LLM and embedding experience, product judgment, and comfort with ambiguous problems.

Develops and executes the organization’s AI and automation strategy, overseeing projects from prototyping through deployment and maintenance. Leads and mentors AI specialists, data scientists, engineers, and analysts while collaborating with IT, product, operations, and compliance teams. Establishes AI development and governance practices, ensures regulatory and ethical compliance, manages budgets and resources, tracks project risks and outcomes, and reports to senior leadership. Promotes innovation and continuous professional development.

Build and deploy AI-powered tools with lending, deposits, credit, and operations teams. Train employees, support AI champions, create documentation and reusable prompt libraries, answer enterprise AI tool questions, and measure adoption, usage, and time savings. Present findings and recommendations to senior leadership at the end of the internship.

Design, implement, and maintain scalable ML pipelines for training, deployment, and monitoring. Automate data preprocessing, feature engineering, and validation. Productionize models with data scientists and backend engineers, ensuring reliable, secure, and efficient ML infrastructure and optimized training and inference performance.

Develop AI-powered automation workflows, LLM tools, prompt-engineering solutions, analytics dashboards, APIs, and test automation frameworks for semiconductor manufacturing quality and testing. Collaborate with cross-functional teams to identify automation opportunities and implement machine-learning solutions that improve workflow efficiency, test coverage, and manufacturing decisions.

Develop deep learning methods for protein language modeling, structural modeling, and multimodal representation learning of TCR-pMHC interactions. Apply machine learning, statistics, computational biology, and data science to complex biological datasets while collaborating with wet-lab scientists. The role includes training and fine-tuning protein and structure-prediction models, building AI pipelines, and using rational structure-based and physics-informed approaches to support therapeutic discovery.

Design, build, and maintain optimization technology, full-stack systems, and data-intensive scheduling infrastructure. Improve optimization algorithms using linear optimization, machine learning, and generative AI. Lead complex technical initiatives, establish testing and observability, support scalable operations, troubleshoot customer issues, participate in on-call rotations, and mentor engineers while partnering closely with product and engineering teams.

The Data Science Manager will lead a team to build data science foundations, drive marketing innovations, and deliver scalable AI solutions to optimize growth decisions.

Design and implement real-time computer vision algorithms for autonomous drones, including estimation, mapping, tracking, classification, and detection. Research and prototype new approaches, optimize systems for computationally constrained environments, measure vision-system performance, and contribute to software engineering across the platform. The role requires expertise in visual odometry, state estimation, mapping, SLAM, probabilistic inference, 3D geometry, deep learning, and high-proficiency C++.

Develop and productionize machine learning models for Petco’s omnichannel business across customer, pricing, inventory, space, and assortment domains. Build data pipelines, datasets, and analytical frameworks; perform exploratory analysis, feature selection, model validation, and KPI measurement. Present insights to technical and nontechnical stakeholders, document methodologies, collaborate on business solutions, and mentor junior data scientists.