Top AI & Machine Learning Jobs

Own end-to-end development of an AI-native, agentic enterprise platform. Build reliable agent systems, scope products from user needs, ship backend-heavy full-stack features, review code, and drive AI-first development. The role requires strong product sense, comfort with ambiguity, deep Go expertise, and experience building AI products or systems from scratch.

Develop statistical models, forecasting methods, and machine learning algorithms for airline optimization and travel retailing. Source and manipulate data using multiple programming languages, deploy models in cloud environments, and collaborate with software engineers on production analytics tools. Define KPIs, monitor data and model performance, conduct proof-of-concept studies, and communicate insights to clients and internal stakeholders.

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.

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.

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.

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.

Design, scale, and maintain robust machine learning systems for AI-powered physical machines. Help define coding standards and the technology stack, work across software and hardware, solve ambiguous problems, and mentor new hires. Relevant work may include object recognition, reinforcement learning, and deep neural networks in a fast-growing AI and robotics startup.

Conduct industry-focused AI research on autonomous agents and world models. Develop models involving large language models, active inference, and optimal policies, including architecture experimentation, fine-tuning, deep-learning agent development, and action optimization. Candidates will work on real-world enterprise problems while pursuing cutting-edge research during a fixed-term three- or six-month fellowship.

Design, train, optimize, and deploy diffusion-based image editing and generative modeling systems. Build scalable training and high-performance inference pipelines, improve realism, consistency, controllability, and style fidelity, and ship model enhancements to user-facing AI image editing products. The role also contributes broadly across backend systems, internal tools, debugging, and AI workflows.