Top AI & Machine Learning Jobs

Research and develop world-model based AI: self-supervised learning from video, predictive architectures, scalable data curation, evaluation benchmarks, and model-based planning algorithms. Design, run, and analyze experiments in large-scale accelerator environments.

Lead and build KLDiscovery's gen-AI architecture and core systems. Design and implement LLMs, agents, RAG, vector search, embeddings, model selection, MLOps, telemetry, and evaluation harnesses. Ship high-impact prototypes and production components, mentor senior engineers, and represent AI strategy to product, customers, and partners.

Remote, part-time role testing an AI tool in Irish: perform guided tasks, report results, follow onboarding and troubleshooting, use MS Teams and basic apps, ~1–3 hours/day up to 15 hrs/week for at least two months, paid $20–$35/hr.

Partner with sales and ML teams to lead technical strategy for complex enterprise deals. Run deep discovery, design integrations, build customized demos and end-to-end POCs, write production-quality Python scripts for data pipelines and API integrations, and advise on security, architecture, and multimodal/robotics data workflows while relaying customer feedback to Product and Engineering.

Build and productionize agentic AI workflows and automation for logistics. Integrate LLMs, orchestration frameworks, and data pipelines with TMS/RPA systems. Implement guardrails, monitoring, evaluation metrics, audit trails, and lightweight front-end prototypes. Collaborate with product, ops, and engineering to scale successful prototypes into reliable production services.

Build and productionize backend-first AI capabilities for a FinOps intelligence platform: design backend services, implement AI features (anomaly detection, recommendations, agents), create evaluation/rollback/monitoring systems, and collaborate with product to ship reliable, cost-efficient, production-ready features.

Conduct applied research on user representation learning, multimodal and transformer-based generative AI. Design, train, evaluate, and deploy large-scale embedding, retrieval, and semantic search systems. Collaborate with engineering and product teams to build production-quality models, establish reproducible benchmarks, and translate research into measurable business impact.

Own and optimize the AI inference and execution layer: quantization, KV-cache, batching, latency/memory tuning, fine-tune and evaluate models, build bounded task runtimes, enable hardware acceleration and partner with systems/backend teams to ship production-grade agentic AI capabilities.

The AI Engineer will build AI-powered solutions to enhance productivity and automation across departments, collaborating closely with leadership and managing integrations with existing SaaS platforms.

Lead end-to-end applied AI engineering projects: define requirements, design and own GCP infrastructure, oversee development and contractors, deploy production ML systems, write PRDs and architecture, drive company-wide AI rollout, training, governance, and scalable operational foundations.