Top AI & Machine Learning Jobs

Leads data science research for a breast cancer program, developing multimodal clinical data frameworks, data dictionaries, machine learning pipelines, statistical models, and reproducible genomic analyses. Collaborates on computational tools, manages projects, drafts grants, creates training materials, supervises trainees, and supports complex data requests involving clinical, genomic, pathology, imaging, and administrative datasets.

Design, train, and deploy machine learning and perception models for edge-intelligent maritime systems. Optimize inference pipelines for embedded hardware, develop datasets and labeling strategies, support computer vision, signal processing, and sensor-fusion experiments, and build real-time on-device and cloud data pipelines. Create benchmarking and debugging tools, evaluate emerging ML research, and collaborate across hardware, software, and product teams to deliver robust production AI solutions.

Build and improve production AI systems for audit and accounting workflows. Responsibilities include developing LLM-powered agents, retrieval and document intelligence systems, evaluations, monitoring, guardrails, human-in-the-loop review, and reliability infrastructure. The role partners with audit experts, product engineers, customers, and leadership to improve AI accuracy, latency, cost, security, and scalability while debugging failures in live workflows and shipping customer-focused features.

Own major technical initiatives from ambiguous customer and product problems through architecture, implementation, iteration, and production delivery. Build AI-native systems using LLMs, agents, evaluations, retrieval, enterprise context, and workflow automation. Set technical direction, make architectural tradeoffs, review work, unblock engineers, and translate customer signals into technical plans. Ship meaningful production work, clarify technical ownership, and reduce execution bottlenecks in a fast-moving startup environment.

Open application for unconventional talent interested in helping build Ami, a voice-first AI companion for seniors. The role seeks builders who can ship, learn, prioritize amid ambiguity, collaborate across operations and engineering, and contribute to hardware and socially intelligent AI models in a fast-moving startup environment.

Architect and ship production AI agent systems and modeling infrastructure for regulated finance. Build Python/FastAPI tooling, distributed event-driven backends, validation/evaluation pipelines, and compliance/auditability features. Influence system architecture, mentor engineers, and ensure reliable, explainable AI outputs in production.

Build and deploy Palantir Foundry workflows, data pipelines, ontology models, AI/ML and LLM applications, RAG systems, and agentic workflows. Integrate enterprise systems, establish security and governance, identify automation opportunities, and partner directly with business teams to move prototypes into production. The role also measures adoption and ROI, develops reusable engineering patterns, trains stakeholders, and evaluates emerging AI tools.

Lead the development and production deployment of applied machine learning and LLM solutions across product, marketing, and operations. Own ranking, recommendation, filtering, personalization, experimentation, and model evaluation systems. Partner with business and technical teams to identify valuable use cases, establish modeling best practices, and ensure reliable production deployment. Manage and mentor applied ML practitioners while maintaining technical rigor, business alignment, and measurable customer and revenue impact.

Corti is seeking passionate individuals to join their talent community, shaping the future of healthcare through AI technologies. Opportunities include roles in AI technology, machine learning, and software development, focusing on reducing errors and improving patient outcomes.

Develop and productionize AI/ML solutions for healthcare, focusing on multimodal LLMs, conversational AI, document understanding, search, and Q&A. Responsibilities include evaluating and fine-tuning models, building training data pipelines, managing the full ML lifecycle, deploying scalable solutions, troubleshooting integrations, documenting architecture, and ensuring HIPAA compliance. The role requires strong Python and PyTorch skills, experience with LLM training platforms and cloud ML services, and close collaboration in a fast-paced startup environment.