Top AI & Machine Learning Jobs

Perform original research on LLM training/evaluation, RAG, RL, and AI agents; design, implement, and evaluate new models and methods; collaborate with researchers; and publish and present results at top ML/NLP conferences while influencing product roadmap.

The AI Technical Lead designs and delivers scalable AI systems, providing technical leadership, mentoring teams, and ensuring effective deployment of machine learning solutions across the organization.

Lead and execute the data science and AI roadmap: build production models on GCP, scale MLOps/LLMOps, deploy Gemini agents, mentor a small team, run experiments, collaborate cross-functionally, and drive adoption of AI tools and measurable business impact.

The Data Scientist supports AI-driven initiatives, conducts analytics, builds machine learning algorithms, and develops technical reports. They work with large datasets to drive business improvements and enhance patient lifetime value.

As a Technical Mentor at Udacity, you will provide personalized support through video calls, conduct technical deep-dive sessions, and host group Q&A sessions to assist learners in AI and related fields.

Support development and deployment of generative image models on Azure, including training/fine-tuning, prompt engineering, image preprocessing/post-processing, integration into tools, testing, documentation, and establishing repeatable workflows for industrial imagery.

Responsible for the full lifecycle of AI/machine learning on resource-constrained hardware, including data ingestion, model development, and deployment on embedded devices, while working closely with cross-functional teams.

As a Machine Learning Engineer Intern, you'll develop neural decoders for Brain-Computer Interfaces and collaborate with cross-functional teams on new functionalities.

The Engineer III - AI designs intelligent systems, leads AI reliability efforts, collaborates on performance metrics, mentors engineers, and focuses on effective communication to solve complex issues.

Lead end-to-end ML projects for fraud detection: develop, deploy, monitor, and iterate production models and experimentation frameworks. Improve model performance, build scalable ML pipelines, collaborate with Product/Engineering/Risk, and champion testing, documentation, and model-monitoring best practices to ensure robust, high-impact solutions.