Top AI Engineer Jobs

As an AI Systems Engineer, you'll design production systems for Codex agents, improve reliability and performance, and collaborate closely with research to enhance model capabilities.

The role involves developing AI-driven compiler technologies, optimizing workflows, integrating learning systems, and collaborating on next-gen compiler capabilities for improved performance.

The Senior Associate will build and extend the data platform and AI applications, focusing on data engineering, cloud infrastructure, and AI development.

As a senior software engineer at NVIDIA, you will design and build tools for performance analysis of AI workloads, lead automation efforts, and enhance workflows for engineers.

The Forward Deployed Engineer will work within enterprise customer environments to develop and deploy AI solutions, utilizing a deep understanding of their workflows and business needs. The role involves owning the full stack from solution definition to performance iteration, ensuring impactful outcomes for customers.

The Adversarial AI & Research Engineer leads adversarial testing, innovates AI attack techniques, builds security tooling, and partners across teams to enhance AI security.

Design and implement backend services for an AI-driven automation platform, focusing on LLM orchestration, API development, and system reliability.

The Staff Software Engineer will lead the technical strategy for AI platform development, architect foundational AI systems, ensure alignment between teams, and mentor engineers while driving organizational engineering standards.

As a Senior Software Engineer, you'll design and build data applications while collaborating with cross-functional teams and mentoring junior engineers.

The Senior Software Engineer will enhance AI data applications by collaborating with teams, designing and building data curation and search apps, and mentoring juniors.

The Senior Software Engineer will drive the development of ML dataset generation and data search applications, mentoring junior engineers and collaborating with cross-functional teams.

Design and develop web applications for ML dataset generation and data search. Mentor junior engineers and collaborate with cross-functional teams.

Develop real-time Voice AI systems, mentor juniors, and collaborate with customers to optimize call intelligence and reduce latency.

The role involves developing AI/ML algorithms in various disciplines, analyzing datasets, and mentoring team members within a research environment.

Develop and manage the research infrastructure for AI at Databricks, focusing on large-scale training, orchestration, and improving developer productivity.

Build internal tools, automations, and AI-assisted systems to reduce operational friction. Develop full-stack features in JavaScript/TypeScript with frameworks like Next.js and Postgres, use AI-native developer tools, debug locally and in production, and move solutions through deployment and production-readiness. Work closely with business users to prototype, iterate, and ship practical internal software that connects workflows and reduces manual work.

Design and build automated data validation and monitoring systems across clinical and AI pipelines. Translate business needs into validation frameworks, implement Python/C#/SQL checks, perform root-cause analysis, and embed data quality and compliance (HIPAA/SOC2) into data lifecycles. Collaborate with stakeholders to prioritize, deploy, and document automation and AI-assisted tooling.

Lead design, training, fine-tuning, and deployment of ML and generative models (LLMs, diffusion). Build scalable MLOps pipelines, vectorization and data ingestion, integrate models into microservices for low-latency inference, implement evaluation/alignment/bias-mitigation, optimize models (quantization, distillation), and mentor engineers while influencing the AI product roadmap.

Design, prototype, and validate high-voltage DC power conversion and distribution systems (including 800V-class). Develop gate drivers, protection/sensing, and perform SPICE-based simulation, PCB/layout optimization, and high-power lab testing. Integrate with embedded control platforms and collaborate across thermal, mechanical, and system architecture teams to deliver validated hardware quickly.

Build and maintain integrations and automations connecting CRM, Microsoft 365, e-signature, custodial/banking systems and reporting. Use LLMs (Claude) and automation tools to extract, process, and verify document-heavy workflows with human-in-the-loop review, monitoring, and auditability, within security and compliance frameworks. Partner across teams and help set AI/automation standards.

Build scalable, resilient agentic AI and LLM/ML solutions. Translate requirements into high-level and detailed architectures. Implement solutions using Python/C++ and ML frameworks, applying AI/ML fundamentals, data structures, algorithms, and distributed systems.

Build and own end-to-end AI features across the stack: prompt engineering, evals, vector search, backend/data models, and frontend interactions. Ensure accuracy, safety, speed, and on-brand AI outputs. Collaborate with Product and Design to iterate on generative AI features used daily (coaching, candidate summaries, natural-language assistant).

Embed with internal teams to identify high-leverage manual workflows and build production-grade agentic AI systems, integrations, and automation (LLM-powered interfaces, observability, APIs, dashboards) that reduce manual effort across CX, finance, ops, and logistics.

Build, operate, and secure the firm's AI infrastructure and developer tooling to enable Python-based AI applications. Implement Azure AI Foundry, integrate LLM APIs (Anthropic, Azure OpenAI), design data pipelines (ADF, Databricks), manage cloud infrastructure (IaC, Kubernetes), and enforce security, governance, and compliance for regulated financial environments.

Design, build, and deploy AI/ML pipelines and models for mission-driven programs in restricted or classified environments. Lead multidisciplinary teams to integrate AI capabilities (including LLMs) into production workflows, implement secure analytics ecosystems, and apply DevSecOps practices adapted to constrained toolchains.