Top AI & Machine Learning Jobs

Machine Learning Engineer Interns will develop, deploy, and operate production-scale ML models and pipelines. They will lead an end-to-end research-to-production project, apply modern ML techniques to blockchain and crypto use cases, collaborate with senior engineers and product teams, and present findings to stakeholders. The role requires doctoral-level machine learning research, model development experience with PyTorch or TensorFlow, production-quality Python skills, and software engineering fundamentals.

Lead AI transformation engagements for 1–2 strategic enterprise customers. Partner with executives to define outcomes, architect and build production AI solutions, configure monday AI agents and vibe apps, integrate customer systems through MCP, and develop custom scripts. Own deployments from discovery through rollout and adoption, measure business impact, conduct workflow evaluations, and convert deployment insights into reusable assets, product requirements, and roadmap improvements. Collaborate with Sales, Customer Success, and Product as the technical authority.

Lead strategic analytics across Go-To-Market and Account Management by building scalable analytical frameworks, diagnosing performance issues, developing dashboards and automations, and applying experimentation to identify causal relationships. Translate complex data into business strategy, drive measurable outcomes, and establish analytics best practices, terminology, and documentation across the organization.

Build and productionize end-to-end AI agents that automate scientific workflows, including experiment design, data capture, analysis, and reporting. Partner directly with customers to identify use cases, gather feedback, develop evaluations, and onboard scientific teams. Work across backend and frontend systems, improve agent infrastructure, and shape Benchling’s AI engineering practices and product direction.

Design, develop, and deploy production-grade AI applications using LLMs, foundation models, RAG architectures, vector databases, and agentic workflows. Build REST APIs, backend services, and cloud-native systems while integrating APIs, external tools, and enterprise knowledge sources. Collaborate with software engineers, product managers, and AI specialists to translate business requirements into scalable solutions. Apply software engineering best practices, with opportunities to work on multi-agent systems, observability, containerization, and frontend technologies.

Own Fora’s growth operating model for forecasting, scenario planning, goal-setting, and strategic investment decisions. Partner with Strategy & Operations, Finance, founders, and executives to analyze business trends, size opportunities, model pricing and initiative impacts, and guide resource allocation. Mentor data team peers while establishing best practices, tools, processes, and standards.

Design, build, deploy, and manage AI systems and automations that improve customer onboarding, support, engagement, retention, and expansion. Develop predictive customer insights, digital experiences, workflows, and integrations with Salesforce, support systems, and data infrastructure. Translate ambiguous Customer Experience requirements into scalable technical solutions, independently make architecture decisions, monitor production systems, measure adoption and ROI, and continuously optimize based on user feedback and changing business needs.

Build and maintain internal agentic AI platforms and developer-facing tools that integrate LLMs with engineering workflows. Partner with product and infra teams to design standards for agent deployment, reliability, evals, security, onboarding, and adoption measurement to improve developer productivity.

Build and operate production machine learning systems and MLOps infrastructure for financial services. Responsibilities include developing feature and training pipelines, model-serving systems, CI/CD, automated retraining, monitoring, alerting, observability, and progressive model rollouts in GCP and Kubernetes. The role partners with data scientists and business teams to improve reliability, performance, scalability, cost efficiency, and deployment velocity while applying AI coding agents to infrastructure and ML workflows.

Own and improve Tessellate’s physician-to-organization matching engine by evolving rules-based matching toward semantic matching, embeddings, and vector search. Establish precision, recall, and evaluation practices; resolve healthcare organization identities across millions of records; and maintain a reliable, lightweight data platform. The role requires strong Python and SQL, applied machine learning expertise, production recommendation or ranking experience, and end-to-end ownership in an early-stage startup.