Top AI & Machine Learning Jobs

One-year fellowship embedded with Hearst’s DevHub to build AI tools for newsrooms and reader experiences. The Fellow will collaborate with product, engineering, journalists, designers, and local newsrooms; develop AI-assisted workflows and products; support responsible AI practices; and enhance interactive tools through data analysis and visualization. The role includes training, mentorship, relocation to the San Francisco Bay Area, and hybrid work.

Set the technical direction for machine learning at Payabli by maturing production risk models and launching new models across the payments lifecycle. Establish experimentation, monitoring, drift detection, benchmarking, retraining, and incident-response foundations. Translate payments challenges into modeling opportunities, connect model performance to business outcomes, influence the ML roadmap, and mentor engineers while partnering with product, engineering, and risk operations.

The Forward Deployed Engineer partners with enterprise clients to identify AI opportunities and deliver production-ready solutions. Responsibilities include building forecasting, optimization, clustering, anomaly detection, generative AI, and agent-based systems; defining data and modeling strategies; implementing monitoring, retraining, and governance; and supporting organizational adoption. The role contributes to multiple implementations while collaborating with BI, Finance, Operations, and business stakeholders.

Develop descriptive, predictive, and causal analytics for local government datasets. Responsibilities include maintaining data pipelines, cleaning and refreshing databases, weighting surveys, building statistical and machine learning models, conducting impact studies, automating cloud workflows, presenting insights, and documenting reproducible processes. The role partners with engineering, marketing, sales, customer success, and survey research teams, and requires Python, SQL, statistics, communication, and applied data science experience.

Design, develop, and apply AI and machine learning models for mission-critical defense applications. Build multimodal sensor, computer vision, NLP, and large language model solutions using Python and ML frameworks. Analyze large datasets, implement computational models and algorithms, collaborate with software engineers in agile teams, transition research into production-ready software, support proposals and technical reports, and present work to customers and professional audiences.

Build and maintain production AI/ML applications, backend services, data pipelines, and machine learning models. Develop agentic AI systems, LLM and RAG solutions, and MLOps capabilities including monitoring, versioning, A/B testing, and retraining. Collaborate with product, software, platform, and data teams to deliver reliable, explainable, and performant AI solutions for financial markets.

Develop and maintain large housing datasets and infrastructure for US housing policy research. Responsibilities include data acquisition, cleaning, analysis, modeling, automation, visualization, dashboard development, web scraping, API use, quality control, and publication. Senior responsibilities add predictive modeling, machine learning, workflow supervision, project management, and establishing data-management best practices.

Build and maintain internal AI-powered applications using Retool, Python APIs, and enterprise LLMs. Integrate Snowflake, dbt, source-system APIs, and operational data while implementing HIPAA-compliant PHI controls, role-based access, masking, audit logging, and data minimization. Develop and evaluate prompts, design workflow-focused user experiences, measure adoption, gather feedback, and continuously improve tools supporting RCM, billing, authorization, and clinical operations.

Build, test, deploy, and maintain AI-enabled workflows for transactional legal work, including diligence, agreement review, redlining, clause extraction, deal summarization, and closing automation. Develop prompts, agents, retrieval pipelines, evaluations, guardrails, and documentation. Partner with legal, engineering, security, knowledge-management, and vendor teams to translate practice needs into reliable production solutions, support adoption, monitor quality and impact, and continuously improve workflows.

Assist with developing, testing, troubleshooting, and securing AI-powered applications. Participate in code reviews, sprint planning, design discussions, and quality checks while learning AI and machine learning concepts. Collaborate with engineering, sales, and marketing teams to translate business needs into AI solutions. The internship includes mentorship, academic alignment, and exposure to responsible AI, data privacy, security, and AI safety practices.