Top AI & Machine Learning Jobs

Act as the technical lead in enterprise sales: run technical discovery, design and deliver customized demos and POCs, assess API/cloud/model fit, address technical objections, support contract negotiations, and relay product feedback to engineering while staying current on AI/ML trends.

Lead product efforts to scale AI-native business systems company-wide: extend agent infrastructure and MCP integrations, establish eval and KPI governance, automate operational workflows, prototype technical solutions, and enforce platform governance for a unified operational layer.

Senior individual-contributor responsible for architecting and shipping Steven.com's most complex, AI-native systems. Own end-to-end architecture, set engineering standards, design LLM-powered workflows and intelligent pipelines, mentor engineers, and translate ambiguous product bets into concrete technical plans with clear tradeoffs.

Perform statistical and computational analysis on large, messy datasets to produce defensible conclusions. Build analytic models, perform statistical inference and prediction, translate mission needs into technical requirements, and communicate results to non-technical stakeholders. Maintain awareness of collection/processing limitations and recommend technical solutions.

Design, implement, and maintain AI-driven workflow automations using Microsoft Power Automate and approved AI tools. Discover and document business processes, translate needs into requirements, coordinate custom NEXUS development with software teams, deliver training and documentation, monitor adoption and performance, and ensure Responsible AI and compliance practices across ISAT, CEAS, and Tomarco business units.

Support development of LLM agents, RAG pipelines, and fine‑tuned models. Contribute to portfolio projects, run experiments and ablations, prepare technical documentation and demos, assist discovery workshops, and help implement evaluation and governance pipelines.

Work with the founding engineering team to build and ship a bounded project (eval infrastructure, ERP integrations, dashboards, or agent training pipeline). Partner with mentors, contribute to code reviews and design, learn production LLM/agent systems, and deploy customer-facing code. Must work in-person in the SF office for the internship.

Lead development and optimization of a data‑intensive AI benchmarking stack: build Python-based backend systems and APIs, implement model evaluation and analyses, create interactive data visualizations, run custom enterprise analyses, and help evolve benchmarking methodology.

Collaborate with the founding team to design, prototype, and scale backend and infrastructure for an agentic AI assistant. Implement orchestration logic, API integrations, and LLM-driven workflows; build diagnostics, tests, and monitoring; improve agent reasoning and execution pipelines; participate in product and architecture discussions; document bugs and stabilize the platform for launch-readiness.

Own end-to-end design, build, deploy, and iterate on production AI systems that improve lead quality, reduce refunds, and automate workflows. Ship customer-facing AI features, agentic workflows, retrieval and context layers, reliable event-driven automations, evaluation datasets and monitoring, and reusable components while connecting technical decisions to business KPIs.