Top AI & Machine Learning Jobs

As a Machine Learning Engineer, design imaging algorithms, build acoustic simulations, and develop computer vision systems to enhance brain imaging technology.

The AI Platform Engineer designs and operates the Microsoft Power Platform ecosystem, delivering AI-driven business solutions and custom integrations while ensuring security and compliance.

Perform data science work on big data systems, providing quantitative analysis and ongoing support across projects and departments. Role emphasizes strong quantitative skills; further technical specifics not provided.

Lead Developer Relations for an early-stage startup building payments infrastructure for AI agents. Own developer advocacy: build demo agents and sandboxes, produce technical content, engage communities (Discord, hackathons), refine APIs via developer feedback, and drive onboarding to production.

Lead development of foundation models and automation tooling for spacecraft design and manufacturing. Build self-supervised, low-data, and RL-based models; create agentic VLM frameworks; integrate with physical simulators and CAD tools; collaborate with spacecraft engineers to validate code on real hardware.

Owner of developer-facing content and reference pipelines for ML training data workflows. Produce cookbooks, benchmarks, demos using real datasets, advocate internally for developer needs, answer user questions, and represent the company at conferences and workshops.

The role involves training and fine-tuning vision and language models, building evaluation and learning pipelines, and optimizing model performance.

Founding product engineer responsible for end-to-end product and infrastructure: build real-time voice pipelines, vector-based memory, LLM-powered agents, CRM integrations, multi-tenant scalable infra, and handle customer onboarding and feedback. Make technical decisions on models and architecture and ship production features across frontend and backend.

Design, implement, and operationalize GenAI/LLM solutions to enable AI-assisted development, codebase intelligence, and legacy-to-cloud modernization. Integrate AI assistants with enterprise platforms, support AI/ML workloads (e.g., SageMaker), ensure secure data handling, and drive AI adoption, governance, and developer productivity across engineering and DevSecOps teams.

Develop and validate ML and deep learning algorithms for neural time-series and neuroimaging data. Perform signal processing, neural signal decoding, and collaborate with scientists, engineers, and physicians to deploy AI-driven neurology tools.