Top AI & Machine Learning Jobs

Lead design, development, and deployment of LLM-powered solutions and autonomous procurement agents on the GEP AI platform. Advise stakeholders, run workshops, configure platform ontologies and data pipelines, optimize models for procurement tasks, and oversee cross-functional implementation to drive automation and improved procurement decision-making.

Build and maintain clinical AI benchmarks, evaluation infrastructure, grading systems, and production harnesses. Convert consented patient cases into de-identified evaluation and training data through clinician review workflows. Lead clinical evaluation studies from protocol design through publication, collaborate on reward design and data curation, and develop agentic evaluations covering tool use, long-horizon tasks, memory, records, labs, and escalation behavior.

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.

Collect, clean, and analyze sports data; develop machine learning and predictive models; create visualizations and reports; collaborate with data and sports strategy teams; and monitor advances in sports analytics.

Instructor-led AI and machine learning training covering theory, data preprocessing, algorithms, model building, analytics, real-time projects, case studies, and practical business and IT applications. Participants use Python, TensorFlow, and Scikit-learn and receive certification upon successful completion.

Founding engineer responsible for architecting and building core product systems, writing scalable Python backend code, designing and maintaining AI/ML infrastructure, instituting best development practices (testing, CI/CD), collaborating on product roadmap, and mentoring future engineers.

Own the analytical layer for autonomous-driving model evaluation and release decisions. Analyze simulation and on-road experiment data, improve performance metrics and scoring methods, identify measurement gaps, apply statistical rigor, and translate investigations into actionable recommendations. Partner with Data Science, Validation, Product, engineering, and OEM teams to strengthen evaluation coverage, ground truth, quality investigations, and weekly model-release feedback loops.

Build and ship AI products for enterprise clients as a senior individual contributor. Set technical direction, write production code, own end-to-end technical outcomes, and collaborate directly with CTO-level stakeholders. The role involves approximately 75% coding and 25% client engagement, applying rigorous software engineering practices to frontier AI systems and delivering multiple products across varied domains.

Develop AI-driven products through hands-on software development and product engineering. Build minimum viable products, solve problems creatively, and help shape future AI applications. The role is available remotely or in Boston and Chicago, with contract options ranging from two weeks to three months or full-time, plus potential revenue sharing.

Own the research-engineering loop for turning base models into capable, reliable agents. Design RL environments, training and evaluation data, post-training experiments, reward systems, and regression suites. Analyze model behavior, diagnose reward hacking and other failures, improve distributed training and rollout infrastructure, and translate successful research into reproducible production pipelines. The role requires strong Python and software-engineering skills, modern language-model experience, deep-learning knowledge, and reinforcement-learning intuition.