Top AI & Machine Learning Jobs

Deploy, maintain, and optimize AI-powered applications and automation workflows. Build and support full-stack integrations with AI platforms, troubleshoot and optimize solutions, collaborate with business and IT teams to implement AI-driven automation, document implementations, and provide technical support. Onsite role requiring English and Spanish fluency.

The Senior Data Scientist will develop and deploy machine learning models for yield optimization, pricing, and inventory allocation in programmatic advertising. Responsibilities include building data pipelines, real-time inference systems, conducting experiments, and collaborating with product and engineering teams to enhance marketplace performance.

As a DeFi Engineer at Flow, you will build and ship DeFi primitives, expand partnerships, enhance existing protocols, prototype innovations, and ensure security while educating the community about DeFi on Flow.

Design and deploy end-to-end machine learning solutions: gather/process large datasets, build predictive and topic-modeling systems, develop data pipelines, use AWS and NLP APIs, containerize and deploy models, lead technical teams, and manage project delivery and stakeholder communication.

Build full-stack features and AI infrastructure for an insurance-specific AI platform. Develop autonomous AI agents, backend services, frontend interfaces, enterprise integrations, and optimize performance and observability.

Source, evaluate, clean, label, and organize diverse, high-quality data inputs for a proprietary model. Explore web sources, validate signal and relevance, and help improve processes that turn raw information into usable datasets.

Open talent network seeking data engineering, data science, and data platform professionals. Work involves SQL and Spark, data cleaning, building production-ready data platforms, and helping government clients use data responsibly. Apply to be notified about future openings.

SoftSnow is seeking an AI Ethicist to define ethical guidelines, assess risks, and ensure compliance in AI implementations across various industries.

Design and build the intelligence layer: retrieval and memory systems, RAG pipelines, feedback and evaluation loops, RL and fine-tuning experiments, LLM integration, observability for AI performance, and collaborate to productize AI features.

Lead production agentic development and build R&D tooling that accelerates team workflows. Ship weekly in the main codebase, create and improve custom agents, MCP integrations, evals, and codegen pipelines, train engineers on new workflows, and raise team standards for prompts, tooling, and autonomous throughput.