Top AI & Machine Learning Jobs

Architect and deliver scalable private markets investment systems and advanced AI solutions. Lead end-to-end feature design, build production-grade LLM/RAG/agentic systems, mentor engineering teams, set standards for software, data, and AI engineering, and partner across platform, data, and security teams to operationalize proprietary private markets data.

Analyze large datasets, build and deploy predictive models, automate data pipelines, collaborate with product and offshore teams, and present data-driven insights to stakeholders to guide SaaS product direction.

Lead backend and generative-AI development: build agentic orchestration and durable AI workflow engines, architect scalable Python/TypeScript backend systems, own agentic coding pipelines and Spec-Driven Development, ship multiple substantial features weekly, collaborate on technical direction, review teammates' work, and ensure reliability, evaluation, and versioned prompt chains for edtech products.

Build and own highly-available data pipelines that move robot video, telemetry, and demonstration data from edge to training sets. Architect ingestion, streaming (MoQ/WebTransport), storage, indexing, and retrieval; improve throughput, reliability, and cost; and build tooling for data discovery and trust while working closely with ML and robotics teams.

Lead architecture and implementation of agentic AI systems powering Zuma's platform. Rebuild onboarding and integration frameworks, implement analytics and observability for LLM performance, translate product needs into technical solutions, mentor engineers, establish engineering standards, and work directly with customers to enable scalable, production-grade AI experiences.

Lead end-to-end ML implementations for clients, design and maintain production-grade MLOps pipelines, deploy and optimize Generative AI/LLM and NLP solutions, manage infrastructure (Docker, orchestration, distributed compute), and communicate technical results to stakeholders.

Partner with enterprise customers to design, build, and deploy production AI applications, LLM-powered systems, agents, and integrations. Lead POCs to production, manage full lifecycle from discovery through adoption, and act as a technical advisor to deliver measurable outcomes.

The Computational Biologist will validate AI outputs, manage digital predictions to experimental protocols, and build validation pipelines at the intersection of software and biology.

Embed onsite with a major automotive customer to identify operational inefficiencies and design, build, deploy, and monitor AI/ML and optimization solutions integrated with TMS/WMS/ERP. Drive automation, translate data into actionable insights, iterate on models, and support adoption across technical and non-technical stakeholders in high-stakes production environments.

The Data Scientist will build models using statistical and machine learning techniques, conduct analyses, collaborate with teams, and improve model performance.