Top AI & Machine Learning Jobs

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.

Data Science Intern supporting data collection, cleaning, exploratory analysis, predictive model development, machine learning pipelines, AI/GenAI prototypes, dashboards, reporting, and technical documentation. The role collaborates with data scientists and engineers to turn raw data into business insights and solutions. Candidates must be enrolled in a quantitative undergraduate or graduate program and possess strong communication, reasoning, mathematical, organizational, and computer skills.

Build functional AI prototypes and MVPs using low-code platforms such as Copilot Studio and Power Platform. Translate business needs into technical specifications, user stories, and workflows; partner with engineering to scale validated concepts into production. Roll out AI productivity tools, support go-to-market and customer success workflows, measure adoption and impact, and apply AI security, privacy, governance, and compliance principles while coordinating with product, architecture, legal, and security stakeholders.

Data Science Intern supporting data collection, cleaning, exploratory analysis, predictive modeling, machine learning and AI prototypes, dashboards, reporting, and technical documentation. The role collaborates with data scientists and engineers to develop business solutions and communicate findings to non-technical teams. Candidates must be enrolled in a quantitative undergraduate or graduate degree program and demonstrate analytical, communication, reasoning, and computer skills.

Build agentic workflows for clinical trial automation and reliable systems for document ingestion, data transformation, validation, and auditability. Own frontend, backend, data infrastructure, and AI orchestration across the product. Collaborate with clinical teams to convert complex workflows into production software while meeting regulated healthcare standards. Work directly with founders, move quickly through ambiguous technical problems, and help establish engineering culture at an early-stage company.

This 12-week, full-time internship involves applying analytics to business problems, building dashboards and analytical models, exploring disparate data sources, and using SQL, Python, and Power BI. The intern will collaborate with data and business teams, develop professional skills through mentorship and networking, and present project results to senior leadership.

Develop practical AI solutions for enterprise customers by translating business needs into solution plans, prototypes, workflows, evaluations, and production implementations. Guide trade-offs involving reliability, scalability, governance, and customer impact; coordinate stakeholders, resolve delivery risks, measure adoption and value, and convert recurring implementation lessons into reusable capabilities.

Develop and maintain infrastructure supporting autonomous surface vehicle operations. Curate and validate large datasets, supervise image annotation, build research software tools, and support field deployments. The role requires Python scripting, version control, data-format familiarity, technical documentation, and experience training, testing, and validating machine learning models across platforms.

Own the Interactive Avatar developer product: define and ship a real-time avatar API/SDK from beta to GA, improve integration and runtime quality, work with engineering/research on trade-offs, partner with go-to-market on packaging/pricing, and track adoption, reliability, and commercial metrics.

Develop scalable AI applications for Aircall’s customer communications platform, focusing on agent context, infrastructure, MCP, and agent memory. Collaborate with backend, frontend, infrastructure, product, and leadership teams on requirements analysis and system design. Build secure, scalable, highly available, and performant backend systems using Python, TypeScript/Node.js, or Java. Apply LLMs, AI frameworks, RAG architectures, and potentially AWS services in production.