Top AI & Machine Learning Jobs

Design, develop, deploy, and optimize machine learning models and production services for healthcare products. Build data pipelines, feature workflows, and training datasets; apply testing, validation, MLOps, cloud infrastructure, and monitoring practices. Collaborate with technical and non-technical stakeholders, improve model performance, develop reusable tools and frameworks, evaluate emerging AI technologies, support incident remediation, and provide technical guidance. The role requires production machine learning experience and proficiency in Python, SQL, and Unix environments.

Own the architecture and hands-on implementation of production MLOps systems, scalable inference services, model deployment pipelines, monitoring, and governance. Integrate machine learning into pharmaceutical manufacturing, process control, automation, and scientific or clinical applications. Collaborate with chemical engineers, computational biologists, and platform teams while establishing enterprise CI/CD and MLOps standards. This is a hands-on senior role requiring hybrid onsite work in Indianapolis.

Develop meteorological forecasting and verification tools for hazardous weather using Python, web technologies, AI, and machine learning. Build and deploy models, anomaly detection systems, data pipelines, and web-based forecaster applications using complex environmental and ensemble datasets. Collaborate with NOAA scientists and external partners, transition tools into operations, produce training materials, and support scientific publications and presentations.

Build and maintain AWS-based machine learning platform infrastructure supporting model training, evaluation, deployment, annotation, and multi-tenant data management. Develop full-stack tools, optimize deep learning models for training and inference, implement containerized CI/CD workflows, and improve cloud cost efficiency. The role focuses on vision-based machine learning, edge deployment, serverless architecture, and scalable production pipelines while collaborating with machine learning scientists and cloud engineering teams.

Design, develop, and integrate AI-powered application features, including LLM-based capabilities, automation workflows, and intelligent user experiences. Build scalable backend services, APIs, microservices, and distributed systems that integrate AI/ML models. Implement observability, monitoring, and logging; participate in architecture discussions and code reviews; and drive engineering initiatives from conception through delivery.

Leads and develops AI, machine learning, and advanced analytics solutions that deliver customer value. Translates requirements into deployable solutions, identifies and prioritizes projects, collaborates with functional and IT leaders, mentors junior team members, and supports model validation, reproducibility, deployment, and continuous improvement initiatives.

Manages the enterprise AI portfolio and coordinates use-case intake, prioritization, governance, reporting, and value realization. Supports the design, integration, deployment, and secure adoption of AI and LLM solutions. Engineers and maintains cybersecurity tools, automations, and integrations while supporting vulnerability management, incident response, risk assessments, audits, policy development, and compliance activities.

Own AI product bets end to end, from customer discovery and data analysis through prototyping and production delivery. Build agentic workflows, harness improvements, integrations, backend systems, evaluation frameworks, instrumentation, and quality metrics. Collaborate with customers, go-to-market teams, founders, and platform engineering while making evidence-based product decisions. Requires strong backend and full-stack skills, production LLM experience, and comfort operating in an ambiguous early-stage startup environment.

Conduct research and develop mathematically rigorous algorithms for real-time auctions, dynamic pricing, bid shaping, pacing, traffic allocation, and fraud detection. Apply machine learning, reinforcement learning, online learning, game theory, forecasting, and Bayesian methods. Deploy models at massive scale, run rapid experiments, evaluate long-term system behavior, collaborate with product and engineering teams, and contribute through publications and internal research seminars.

Tenure-track Assistant Professor teaching data science and analytics courses while conducting funded research in machine learning, statistical modeling, data mining, omics, bioinformatics, and population health. The role requires interdisciplinary teaching, publication, service, and contributions to health equity and community-engaged research.