Top AI & Machine Learning Jobs

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.

Drive product analytics across feature adoption, usage, pricing, customer health, and experimentation. Partner with product, engineering, and business leaders to identify opportunities, deliver actionable insights, improve self-serve analytics and governance, and strengthen data pipelines and analysis quality. The role requires communicating complex findings clearly and working from Aircall’s San Francisco office two days per week.

Deploy AI/ML drug-discovery workflows for scientists, ML teams, and pharma customers. The engineer scopes customer needs, configures models and infrastructure, runs pilots, debugs data and compute pipelines, supports technical demos, and translates field feedback into product improvements and roadmap inputs.

Lead architecture and end-to-end delivery for Sylvan’s autonomous revenue platform. Build initial versions of complex systems, guide ML and product strategy, establish engineering standards for agent-driven development, and lead a pod of three to four engineers. Responsibilities include sprint planning, technical design, infrastructure decisions, hiring, performance management, and shipping production work with significant autonomy.

Build and launch customer-facing AI applications by developing frontend and backend features, integrating APIs, AI services, databases, and business tools, and improving reliability and usability. Independently manage features from requirements through deployment, troubleshoot AI-generated code, and collaborate with developers. Applicants may be self-taught or early-career and should demonstrate completed applications through employment, freelance work, or personal projects.

Develop NLP, LLM, and machine learning pipelines to abstract clinical concepts from structured, unstructured, and multimodal healthcare data. Query healthcare source systems, curate and validate AI-ready datasets, support patient phenotyping and real-world evidence analyses, and deliver HIPAA-compliant data products. Collaborate with product and engineering teams, communicate findings to technical and clinical audiences, and improve data quality for customer AI applications. The role is remote with occasional quarterly travel.

Founding CTO will architect and build Cure’s AI-native customer engagement and recovery platform. Responsibilities include developing conversational agents, decisioning and personalization systems, evaluation and feedback loops, enterprise integrations, and secure, explainable infrastructure. The CTO will work with customers, lead product and engineering execution, recruit early technical talent, and establish the organization needed to scale a reliable platform for regulated industries.