Top AI & Machine Learning Jobs

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.

Participate in a 12-week data science internship by analyzing business problems, building dashboards and analytical models, using SQL, Python, and Power BI, collaborating with analytics teams, communicating findings, and presenting project outcomes to senior leadership.

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.

Designs and validates enterprise AI infrastructure solutions for customers, focusing on GPU-accelerated computing, Kubernetes, Linux, NVIDIA technologies, networking, storage, security, and AI platforms. Leads discovery sessions, architecture workshops, proofs of concept, demonstrations, proposals, and RFP responses throughout the sales cycle. Advises customers on agentic AI, multi-tenancy, governance, and production operations while collaborating with partners, sales teams, product management, and engineering. Develops reusable reference architectures and enables field teams.

Design and optimize scalable multimodal machine learning pipelines for training, fine-tuning, evaluation, experimentation, and deployment. Collaborate with data engineering, research, product, and infrastructure teams across text, image, audio, video, and 3D modalities. Improve distributed training performance, data workflows, reproducibility, model governance, and resource utilization while shaping technical strategy and engineering best practices.

Supports planning, coordination, tracking, reporting, and delivery of research projects involving scientific and technical teams. Responsibilities include milestone and resource tracking, meeting facilitation, status communications, dashboard and documentation maintenance, grant accounting and billing reports, issue escalation, and operational follow-through. The role supports projects involving data science, genetics, genomics, and software development while coordinating stakeholders, timelines, deliverables, and project records.