Top AI & Machine Learning Jobs

Build Pelico’s agentic platform end to end, developing Python backend services, React interfaces, agent tools, orchestration workflows, runtime reliability features, evaluation harnesses, and real-time approval consoles. The role involves shipping production AI capabilities, improving agent trustworthiness, defining technical specifications, and creating reusable full-stack engineering patterns for manufacturing customers.

Facilitate adaptive, hands-on AI workshops for startups, scale-ups, and SMBs; teach AI tools, workflows, adoption mindsets, and behavioral change. Maintain fluency across Claude, Gemini, and ChatGPT, develop learning content, support client relationships, contribute to marketing and thought leadership, and help improve Clarinet’s workshop offerings based on participant feedback.

Conduct quantitative analysis, process complex and geospatial data, develop visualizations, and create data-driven insights. Build Python-based data-cleaning and manipulation workflows while working with AI/ML technologies and multiple file formats. Desired work includes NLP document extraction, computer vision, automated collection and reporting, data standardization, and statistical techniques such as classification, regression, clustering, data reduction, and causal modeling.

Own end-to-end development of AI-native healthcare solutions, from exploratory data analysis and model strategy through production deployment and monitoring. Fine-tune multimodal generative models, orchestrate AI agents, define rigorous evaluations, build scalable backend systems, and monitor data drift and model performance. The role requires strong software architecture, cloud infrastructure knowledge, observability, testing, autonomous execution, and technical mentorship in a high-stakes healthcare environment.

Builds reporting, dashboards, data pipelines, forecasting models, predictive analytics, and AI-assisted workflows for executive decision-making. The role integrates data from project, financial, CRM, and operational systems; validates and reconciles analytical outputs; applies statistical methods and large language models; automates manual processes; and communicates actionable insights across departments. It requires ownership of business intelligence, predictive modeling, data governance, and executive-facing analysis.

Develop and deploy computer vision and machine learning models for semantic and instance segmentation, object and damage detection across 2D and 3D data. Responsibilities include training loops, dataset curation, labeling guidelines, evaluations, ablation experiments, model export, inference code, testing, profiling, and production support. Collaborate with research, backend, operations, and labeling teams while documenting experiments and improving model performance.

Develop and scale vision-language-action models for robotic manipulation, owning dataset construction, training recipes, distributed GPU infrastructure, evaluation, and on-robot validation. Build reproducible ML pipelines, optimize multimodal data processing and model training, diagnose failures across data and robot behavior, and translate research into production-quality systems. Collaborate across research, robotics, hardware, data, and operations teams while mentoring engineers.

Contribute to projects that build advanced technical support tools and AI solutions, helping troubleshoot customer issues faster and improve operational efficiency. The intern will document complex processes, collaborate with stakeholders, present findings, and apply technologies including Python, SQL, cloud platforms, ETL frameworks, backend frameworks, data-processing libraries, Git, and AI productivity tools.

Contribute to building tools and AI solutions that help technical support teams troubleshoot customer issues faster and improve operational efficiency. The intern will document complex processes, collaborate with stakeholders, develop technical skills, and apply technologies including Python, SQL, cloud platforms, ETL frameworks, backend frameworks, data-processing libraries, and version control systems.

Build and deploy LLM-based automation solutions for trading, operations, and support teams. Design agentic workflows, prompts, AI Skills, and MCPs; collaborate with business users; translate needs into technical solutions; and deliver production-ready AI systems. The role requires Python development, LLM experience, stakeholder engagement, documentation, autonomy, and continuous learning in commodities and quantitative finance.