Top AI Engineer Jobs

Build and deploy production-grade AI applications across front-end, back-end, and data workflows. Translate customer problems into product requirements, integrate AI models, optimize application performance and reliability, evaluate AI outputs, and iterate using customer feedback. The role supports impactful projects for government, healthcare, and industrial customers, with ownership across the full product lifecycle.

Own production reliability for a Kubernetes-native control plane provisioning and operating GPU inference fleets. Responsibilities include managing host lifecycle state machines, troubleshooting GPU, CUDA, InfiniBand, RDMA, and model-serving failures, extending automated remediation, owning SLOs and error budgets, leading incident response, improving inference performance and efficiency, and partnering across infrastructure, ML platform, research, and product teams.

Build and own Clera’s AI/ML intelligence layer, including recommendation systems, ranking algorithms, and LLM-powered candidate-role matching. Develop production-ready features, data pipelines, and services while collaborating cross-functionally and shipping frequently. The role requires hands-on experience with the listed development tools, TypeScript, React, startup environments, and a willingness to work six days per week in a San Francisco hacker house.

Develops secure, scalable software solutions for JPMorganChase Asset Management. Responsibilities include architecture and application development, production coding, troubleshooting, database optimization, cloud deployment, event processing, infrastructure automation, and responsible use of AI-assisted development tools. The engineer also analyzes data, creates technical artifacts, supports application resiliency and security, and contributes to agile team practices and reusable engineering patterns.

Build and own production-grade AI systems for the People function, including agentic workflows, RAG pipelines, data integrations, evaluation frameworks, and privacy controls. Partner with People, data, governance, and engineering teams to transform operational workflows, ensure reliable use of sensitive workforce data, and promote responsible AI deployment. The role requires independently translating ambiguous business needs into scalable technical solutions and enabling sustainable AI ownership across the organization.

Build and ship end-to-end product features, debug and resolve production issues, partner with Product on UX and direction, use AI heavily for development and prototyping, and iterate quickly across the stack to improve quality and speed.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Develop physics-based robotics simulations, digital twins, software- and hardware-in-the-loop test benches, regression tests, scenario-generation methods, and synthetic data workflows. Build tools that enable engineers to run simulations and parameter sweeps, compare virtual models with real-world data, and evaluate robotics and Physical AI systems. Collaborate across mechanical, electrical, controls, software, and AI engineering teams to improve simulation fidelity and identify failure modes.

Build and maintain AI foundations and production model services, including fine-tuning and deploying open-source models in GPU-backed infrastructure. Develop computer vision, image understanding, OCR, document layout analysis, and multimodal solutions for PDFs, images, and scanned documents. Optimize inference performance, resource utilization, throughput, and latency while debugging production systems and improving engineering standards.

Own end-to-end development of production-scale AI-powered applications that improve internal workflows and customer experiences. Architect agentic systems, RAG applications, tool-using assistants, evaluation and monitoring platforms, shared APIs, guardrails, and infrastructure. Identify high-leverage opportunities, establish technical standards, influence cross-functional direction, mentor engineers, and operate effectively in a greenfield environment.

Architects and deploys secure, scalable AWS cloud, data, and generative AI solutions for enterprise clients. Leads cloud migrations, legacy modernization, Infrastructure as Code, CI/CD, serverless and containerized implementations, and GenAI workloads using Bedrock and SageMaker. Acts as a client-facing technical leader, presents to executives, builds proofs of concept, identifies cloud expansion opportunities, and mentors engineers while developing reusable delivery accelerators and best practices.

Designs and implements secure, scalable AWS cloud architectures, modernizes legacy applications, and deploys generative AI and machine learning solutions using Bedrock, SageMaker, and vector databases. Leads enterprise client engagements, migrations, proofs of concept, and executive technical alignment while identifying cloud adoption opportunities. Provides mentorship, develops reusable infrastructure modules, and promotes AWS Well-Architected delivery practices.

Brings up and optimizes foundation models on SambaNova’s AI platform. Profiles performance across compiler, runtime, and hardware layers; identifies bottlenecks; applies quantization, kernel fusion, scheduling, and memory optimizations; benchmarks inference throughput and latency; and collaborates with machine learning, compiler, runtime, and hardware teams to deliver product improvements.

Designs, develops, deploys, and supports enterprise AI solutions across Northern Trust’s Digital Coworkers and Generative AI Foundation Platform. Responsibilities include full-stack application and API development, Azure cloud engineering, Terraform infrastructure, CI/CD, LLM and RAG integrations, AI agents, workflow automation, monitoring, governance, and production support. The role partners with engineering, architecture, security, product, and business teams to deliver scalable, secure, governed AI capabilities in a regulated financial-services environment.

Evaluate AI coding agents such as Codex, Claude Code, and Cursor for reasoning quality, engineering judgment, explanations, usefulness, and user trust. Provide rigorous, opinionated feedback distinguishing response-quality levels and help define standards for excellent AI-assisted development. This contract role requires staff- or principal-level engineering experience and familiarity with modern AI development workflows.

Design, develop, test, and deploy AI/ML services and agentic workflows for rugged tactical networking and computing systems. Translate business needs into technical requirements, collaborate with technical owners, and build scalable applications using Python, Linux, cloud-native infrastructure, AI frameworks, databases, observability tools, and CI/CD pipelines. The role supports mission-critical government, defense, and intelligence applications.

Build and deploy agentic AI applications for business and scientific stakeholders across R&D, clinical, manufacturing, and commercial functions. The role involves identifying high-value problems, prototyping solutions, productionizing AI workflows using AWS, Azure, Databricks, and Microsoft technologies, and establishing engineering standards, deployment practices, monitoring, and documentation for a new internal AI function.

Designs, implements, operates, and improves shared university AI services. Consults with academic and administrative units, evaluates AI platforms and vendors, prototypes integrations, and moves pilots into sustainable enterprise solutions. The role develops governance guardrails, service processes, business cases, documentation, and adoption metrics while coordinating with security, legal, procurement, architecture, and data governance teams. It also supports AI communities of practice and communicates technical capabilities, limitations, risks, and recommendations to technical and non-technical stakeholders.

Architect and develop bare-metal C firmware for RISC-V management cores controlling AI/HPC networking acceleration engines. Responsibilities include secure firmware loading, image authentication, boot and reset sequencing, health validation, multi-die firmware distribution, updates, hardware/software interface definition, simulator-based testing, architecture standards, code reviews, and technical mentorship.

Lead the AI Platform team in building reliable, secure, observable infrastructure, shared services, deployment patterns, and operational practices for AI capabilities. Set technical priorities, coordinate cross-team dependencies, guide architecture and production operations, improve developer experience, and coach engineers while contributing to complex technical work.

Leads research, development, productionization, and deployment of advanced AI models powering context-engine and agentic platforms. Builds multimodal ingestion, entity-resolution, knowledge-graph, and agentic-search capabilities; designs experiments; collaborates with product and infrastructure teams; mentors engineers; contributes to research and technical documentation; and supports customer-focused product delivery.

Design, build, and own a multi-tenant enterprise SaaS backend that feeds ML data and BI. Collaborate with product, data science, UX, and users to deliver scalable, reliable customer-facing AI solutions. Drive systems design, database schemas, development lifecycle standards, technical docs, and code reviews while using AI/agentic tools to accelerate development.

Build shared APIs, SDKs, evaluation infrastructure, tracing, observability, and model-routing systems for Suno’s AI agent platform. Support engineering teams adopting the platform, improve reliability and scalability with Infrastructure, and manage provider fallbacks, rate limits, and cost controls. The role requires production backend or platform experience, distributed systems expertise, and experience supporting developer teams and deploying LLM or agent systems.

Build and scale production AI innovations, including agentic systems, real-time voice, and multimodal experiences. Research models and frameworks, prototype rapidly, run safe experiments, and measure quality, latency, cost, and impact. Extend agent-platform orchestration, tool use, and evaluation capabilities. Make cross-team technical decisions, establish engineering practices, mentor others, communicate with technical and executive audiences, and partner with product, design, and engineering to turn validated concepts into secure, reliable platform products.

Design and implement production agentic AI systems, RAG pipelines, intelligent document processing services, embedding and vector retrieval workflows, and multi-agent extraction systems. Build FastAPI and Java/Spring Boot services, databases, evaluation harnesses, and governance controls. Own major AI platform components, collaborate with architecture and business teams, and mentor mid-level engineers while driving technical excellence and production quality.