Top AI & Machine Learning Jobs

Designs, configures, and deploys Agentforce agents and autonomous workflows within Salesforce Financial Services Cloud. Integrates agents with MuleSoft APIs, external data, and financial planning platforms; implements Einstein AI capabilities; and ensures security, compliance, and reliable agent behavior. The role also tests and monitors agents, mitigates hallucination risks, analyzes usage data, incorporates advisor feedback, and evaluates new Salesforce AI features.

Design, build, and deploy full-stack infrastructure and orchestration for autonomous AI growth agents. Own backend systems, event-driven workflows, data pipelines, front-end interfaces, and generative features from 0→1. Collaborate with founders to shape architecture and ship Hypesonic products that scale AI-driven marketing and invention workflows.

Build and operationalize production-grade image and video generative AI systems. Responsibilities include designing multimodal pipelines, agentic workflows, MCP servers, microservices, prompt management, model integrations, ML infrastructure, and scalable inference services. The role emphasizes reliability, cost, latency, observability, and collaboration with data scientists, designers, product managers, and creative teams to deliver AI-powered visual experiences.

Design and operationalize large language model fine-tuning workflows using supervised learning, DPO, RLHF, LoRA, and QLoRA. Build distributed GPU training pipelines, curate datasets, optimize training performance, and create rigorous safety and capability evaluations. Manage reproducibility, model artifacts, and training reliability while collaborating with cross-functional teams. The role also includes documenting results, mentoring engineers, and translating LLM research advances into production-ready solutions.

Design and deploy deep learning systems for search ranking, personalized recommendations, geospatial search, and pickup-point selection. Translate business goals into ML objectives, lead offline and online evaluation, conduct A/B tests, and partner with engineering on production model serving and latency optimization. Own the production ML lifecycle, including monitoring, drift detection, retraining pipelines, and quality management across international markets.

Leads enterprise Data Science and AI initiatives, including GenAI and agentic AI use-case prioritization, prototyping, evaluation, architecture readiness, governance, and production handoff. Manages data scientists and coordinates business, engineering, platform, and enterprise AI stakeholders. Builds evaluation frameworks using quality, safety, latency, cost, and regression metrics while guiding semantic layers, metadata, data integration, agile governance, executive reporting, and scalable responsible AI adoption.

Leads the enterprise AI and automation strategy, including the RPA portfolio, agentic AI architecture, Microsoft 365 Copilot and Claude Enterprise adoption, emerging technology pilots, and value realization. Oversees AI solution analysts, develops business cases, partners with executives, and manages automation governance through a major ERP transformation. The role is hybrid in Lake Forest, Illinois, with local candidates required.

Leads client relationships and advises stakeholders on AI and advanced analytics strategies, workflows, use cases, and implementation. Coordinates teams, manages complex projects and escalations, evaluates solutions, mentors consultants, and identifies risks and improvement opportunities. Applies business and commercial judgment while tracking project metrics and ensuring consistent service delivery.

Develop product analytics and experimentation for Spotify’s Discovery Mode promotion tools. Build metrics, instrumentation, dashboards, and actionable insights to improve customer engagement and business outcomes. Collaborate with product, engineering, research, design, marketing, and other data science teams to identify opportunities, test hypotheses, and influence product decisions through clear data storytelling.

Build and maintain MLOps infrastructure: CI/CD for models, monitoring, deployment into a NestJS monolith, feature store/versioning, retraining workflows, A/B experimentation, and LLM operationalization.