Top Artificial Intelligence Jobs

Commercial Counsel Fellowship lawyers draft, review, and negotiate SaaS, data processing, partnership, and vendor agreements. They support sales, improve contract processes and legal technology, research generative AI legal developments, and may contribute to privacy, intellectual property, product counseling, and compliance projects. The fellowship provides hands-on training, mentorship, and potential conversion to a full-time legal role.

As a Backend/AI Engineer, you'll integrate LLMs, optimize document processing pipelines, and improve API design for document understanding tasks.

Build Valius’s technical foundation across full-stack applications, cloud infrastructure, data pipelines, and patient molecular data systems. Partner with founders and computational biologists to set technical direction, develop AI-powered interfaces, manage architecture and security, and deliver reliable, well-tested software. The role requires ownership from design through deployment and offers opportunities for future technical leadership.

Own customer relationships from contract through long-term success, including implementation, platform configuration, team training, ongoing support, usage monitoring, and proactive issue resolution. Serve as the voice of the customer by sharing insights with Product and Sales, while establishing scalable deployment processes. The role requires exceptional organization, AI fluency, warmth, crisp communication, customer obsession, and high agency.

Design and verify superconducting electronic circuits for AI hardware, including Josephson junctions, SQUIDs, superconducting nanowire detectors, and thin-film amplifiers. Adapt circuits for advanced fabrication processes, support electromagnetic and post-layout simulation, develop Python-based layout flows, expand device-primitives libraries, and collaborate with fabrication, foundry, algorithms, and measurement teams to execute successful chip tapeouts.

Mechanical Engineering Intern supporting the design, fabrication, integration, and testing of prototype hardware for humanoid robots. Responsibilities include developing mechanical fixtures, drivelines, and test stands; integrating sensors and motors; creating GD&T drawings; and collaborating on hands-on validation. Candidates should have mechanical prototyping experience, 3D CAD skills, strong hardware aptitude, and documentation ability. CATIA V6, manufacturing inspection, FEA, simulation, and programming experience are beneficial.

Own full-cycle recruiting as the company’s sole recruiter, sourcing and closing passive candidates across engineering, clinical, quality, and regulatory roles. Partner with hiring managers on role definition, compensation, interview design, and hiring decisions. Track recruiting metrics, improve hiring processes, build employer brand initiatives, and ensure a strong candidate experience while supporting an onsite deep-tech organization.

Lead the architecture and evolution of infrastructure powering autonomous driving software, including low-latency IPC middleware, runtime engines, compiler and build toolchains, simulation platforms, crash diagnostics, and developer productivity systems. Drive performance profiling, memory optimization, fault isolation, and cross-team engineering standards while mentoring engineers and guiding technical architecture across autonomy and simulation teams.

Manage daily operations of a hardware engineering lab for a humanoid robotics team. Responsibilities include organizing workspaces, tracking and procuring inventory, kitting parts, supporting equipment setup and calibration, coordinating repairs and deliveries, managing lab access and onboarding, enforcing safety and ESD protocols, and assisting engineers with demos, shipments, and sourcing components.

Build and operate full-stack healthcare products across frontend, backend, event-driven workflows, data services, integrations, and AI-powered features. Partner with Product and clinical, sales, and operations stakeholders to define problems, ship solutions, measure outcomes, and iterate. Own system design, reliability, security, observability, testing, incident response, and production quality, particularly for patient data, clinical logic, billing, and LLM functionality.