Top AI & Machine Learning Jobs

Lead development of production agentic AI systems: reasoning, planning, tool orchestration, memory and grounding, evaluation frameworks, and multimodal agent coordination across chat, voice, and UI. Translate research into reliable, testable systems and iterate with founders and customers.

Lead research and translate models into production-ready document AI systems. Own end-to-end ML lifecycle (research, experimentation, training, evaluation, deployment), build data pipelines, optimize inference and serving, and collaborate with engineering and founders to shape product and engineering strategy.

Build and ship internal AI frameworks and staff-facing tools for care navigators: design the Navigator Workbench, optimize data feeds and transcripts, define prompts/decision trees and safety guardrails, integrate with engineering and data architecture, monitor AI output quality, and enable non-technical ops to extend systems.

Lead development of production AI systems for building-system design including vision pipelines for PDF/CAD parsing, LLM+RAG workflows for code-compliant designs, AI agent orchestration, and full-stack features across a Typescript/React/Node/Postgres platform. Significant ownership and technical direction at an early-stage startup.

Design, develop, and deploy real-time computer vision and video-processing algorithms for embedded, resource-constrained platforms. Optimize AI/ML models for high-frame-rate systems with GPU/FPGA and multi-core processing, integrate sensors and cameras, participate in full SDLC, evaluate and optimize performance, debug pipelines, document work, and mentor junior engineers.

Architect and build autonomous AI agents for revenue operations: develop agentic systems using LangChain/LlamaIndex, implement RAG and prompt engineering, integrate with Salesforce and cloud services, optimize LLM inference, and add observability and scalable deployments to drive predictive sales and customer success automation.

Build and own production applied-AI systems focused on information retrieval and LLM pipelines. Design experiment frameworks and evaluation pipelines, optimize for metrics (precision, recall, F1), run benchmarks, perform failure analysis, and iterate to improve real-world system performance at scale.

Architect and formally model complex warehouse vision-AI systems across edge devices, cloud, and user software. Define abstractions, analyze latency/throughput/failure modes, identify ML opportunities to replace heuristics, collaborate across hardware and software teams, use AI tools in daily workflow, and deliver production-quality systems.

Build the Context Management API and semantic modeling infrastructure for AI systems: design retrieval and data-access patterns, orchestrate LLMs and multi-agent systems, implement self-healing data models and evolving knowledge graphs, and integrate enterprise data sources for reliable AI-driven GTM execution.

Design and train spatiotemporal neural simulators for long-form, controllable video/world modeling. Own end-to-end large-scale training of multi-billion-parameter models, manage GPU-cluster experiments, diagnose failure modes, improve data mixtures, and tighten evaluation to drive measurable gains.