Top AI Engineer Jobs

Own AssemblyAI’s go-to-market technology stack, including Salesforce, enrichment, outbound automation, routing, scoring, and AI agents. Build production automations, integrations, internal tools, and AI workflows for prospecting, research, personalization, and lead scoring. Convert product usage data into actionable sales signals, automate account targeting, maintain clean funnel data, and partner with Sales and Marketing leadership to improve pipeline and revenue operations.

Design, build, and scale AI engines, conversational assistants, context-aware agents, and workflow automations for go-to-market teams. Own the GTM AI roadmap, trusted data and knowledge architecture, AI governance, production monitoring, adoption, and value realization. Partner with Sales, Marketing, Revenue Operations, Product, and Systems leaders to translate business needs into scalable AI solutions and measure their impact.

Lead and develop a team of Applied AI Engineers while remaining hands-on in production code. Own technical outcomes, architecture decisions, engineering standards, customer technical relationships, hiring, and delivery across ambiguous enterprise environments. Partner with engagement and growth leaders, provide deliberate coaching and feedback, support customer deployments, and translate field insights into improvements for Mosaic, the company’s AI deployment toolkit.

Develop AI-enabled software and production applications that improve cyber operations. Partner with mission operators to identify challenges, rapidly prototype solutions, and apply Python, software engineering, AI, data analytics, cybersecurity, LLMs, Splunk, Kubernetes, Rancher, Airflow, and DevSecOps practices to mission objectives.

Develop and productionize GenAI applications for federal-sector customers, including RAG, multi-agent systems, Text2SQL, and fine-tuning solutions. Advise customers, lead production rollouts, present technical concepts, collaborate with product and engineering teams, and influence the AI product roadmap. The role requires production machine learning deployment experience, strong data science expertise, customer-facing communication, U.S. citizenship, Secret clearance eligibility, and periodic customer travel.

Design, build, and deploy cloud-native, agentic AI applications on AWS/GCP. Apply AI and automation to improve developer productivity, design and consume APIs, ensure security and compliance, support generative/agentic AI in production, collaborate in Agile teams, and contribute to architecture and engineering best practices.

As a Software Engineer, you will build AI systems to enhance developer workflows, implementing agents and servers, and ensuring code reliability through evaluation pipelines.

As a Staff Engineer, you'll lead the orchestration platform strategy, mentor engineers, and ensure reliable workloads across GPU clusters, directly impacting AI innovation.

Design and build Robinhood’s agent platform, including orchestration, tool integrations, context and memory management, trajectory-level evaluations, simulation environments, and action guardrails. Ship production agentic applications, establish safety and evaluation standards, create reusable SDKs and CI gates, and provide technical leadership through architecture reviews, mentorship, and production-readiness decisions.

Own Arize’s product documentation and build automation to keep content accurate, detect code-documentation drift, test examples, and draft updates. Create tutorials, cookbooks, onboarding materials, and open-source demonstrations for developers and coding agents. Coordinate documentation with partners, frameworks, and course authors; gather community feedback; support marketing content; engage developers at events and online; and prototype product features with the Product team.

Own quality control, regulatory alignment, and performance reporting for AI-powered clinical extractors. Analyze accuracy, consistency, data quality, provenance, and performance drift; guide extraction logic and clinical interpretation; support FHIR and CSV data exports; and contribute to governance, de-identification, validation, and client reporting. Collaborate with Data Science, Engineering, and Clinical Informatics while serving as a clinical subject-matter expert, particularly in oncology and real-world evidence.

Build and deploy scalable AI-powered web applications and Generative AI workflows. Develop responsive frontend experiences, backend services, AI data pipelines, LLM integrations, and cloud infrastructure. Collaborate with product managers, designers, and AI/ML engineers to translate user needs into product features, optimize application and model performance, and manage cloud deployments. The role requires ownership of production-grade solutions using modern frontend frameworks, backend languages, RAG architectures, vector databases, CI/CD, and AWS.

Lead the design and development of mission-relevant software, build backend services and APIs, mentor engineers, and support technical demos.

Design, build, and operate AWS-based microservices and asynchronous workflows powering AI-driven laboratory protocol generation. Develop NLP pipelines, classifiers, embedding and RAG infrastructure, evaluation frameworks, guardrails, cost monitoring, and observability. Integrate AI systems into production SaaS architecture, ensure reliability and safe scaling, and support post-launch operations through debugging, incident response, and continuous improvement.

Lead the architecture and development of scalable LLM-powered applications, AI infrastructure, and full-stack services. Build reliable platforms, automation, monitoring, and deployment systems for enterprise-scale AI workloads. Conduct capacity planning, performance tuning, incident response, and post-mortems. Collaborate with product teams, improve development processes, and mentor engineers while supporting rapid and reliable delivery of AI features.

Lead technical direction and people management for an AI/ML engineering squad. Design, build, and deploy production-grade ML and Generative AI solutions (LLMs, RAG, agentic systems), drive MLOps/CI-CD, ensure Responsible AI and HIPAA-compliant practices, conduct architecture/code reviews, and mentor engineers to deliver measurable business outcomes.

Lead hands-on engineer designing and modernizing cloud-native, reliable, and automated software solutions. Drive adoption of enterprise AI-assisted engineering practices, build automation frameworks, support performance and resiliency testing, lead code reviews, and communicate technical strategy and reliability standards across teams.

Design and build scalable AI platform capabilities to enable multi-step GenAI workflows, model execution, and integrations. Implement backend services, execution infrastructure, APIs, and observability to support production GenAI experiences across sales and product teams. Collaborate with AI engineers, data scientists, and product partners to turn AI use cases into reliable, extensible platform features.

Lead technical vision and architecture for ML-driven trajectory generation in autonomous vehicles. Build and deploy scalable training pipelines, integrate models into real-time safety-critical onboard systems, mentor senior engineers, drive cross-functional initiatives, and move solutions from research to production using simulation and large-scale datasets.

Own and optimize deep learning training performance across GPUs, cloud infrastructure, and distributed environments. Profile workloads, develop custom GPU kernels, tune PyTorch and training systems, improve memory and compute efficiency, and design scalable distributed training strategies. Collaborate with researchers and infrastructure teams to preserve numerical correctness while improving speed, cost, reliability, and scalability. Build benchmarking, checkpointing, recovery, and regression-testing workflows for long-running AI/ML experiments.

Lead enterprise AI initiatives as a principal engineer and solution architect. Design, prototype, and deploy scalable generative AI, RAG, agentic AI, and predictive solutions. Translate business needs into technical requirements, evaluate technologies and vendors, conduct architecture reviews, and establish AI engineering, trust, safety, monitoring, and compliance standards. Mentor engineers and guide responsible AI practices across customer-facing products and internal operations.

Leads enterprise AI strategy, architecture, and production implementation across corporate functions, contact centers, and GTM products. Designs generative and agentic AI solutions, conversational and voice agents, RAG systems, intelligent workflows, and multi-agent orchestration on Azure and Google Cloud. Establishes AI governance, observability, SecOps, escalation, and operational models while influencing executives, product teams, and engineering organizations. Mentors technical staff and drives measurable AI adoption and business outcomes.

Build and improve AI agents that automate engineering, fleet operations, and business workflows. Responsibilities include integrating LLMs with internal systems, implementing planning and tool-using agent workflows, developing backend services and APIs, evaluating models and frameworks, and contributing to internal AI platforms. The role requires strong programming fundamentals, Python or comparable language skills, and familiarity with machine learning or LLMs. Preferred experience includes agent frameworks, RAG, vector databases, cloud platforms, and containerization.

Build and own Lightspark’s internal AI and data platform, including agent infrastructure, MCP servers, tool integrations, governed data, sandboxed execution, and production pipelines. Partner with Finance, Sales Ops, Compliance, and other teams to replace manual workflows with reliable systems. Extend dbt and cloud data warehouse infrastructure, support monitoring and data reliability, and improve engineering tooling. The role requires production experience with AI agents, LLM APIs, internal platforms, and TypeScript/Node.js or Python.

Designs, builds, and deploys Python-based NLP and machine learning workflows, knowledge graphs, analytical tools, APIs, and collaborative TypeScript applications. Integrates data feeds, supports secure end-to-end pipelines, prototypes solutions in ambiguous environments, and partners with Joint Staff customers in classified SIPRNet and JWICS environments. The role requires adapting technologies to mission-specific analytical and operational needs while providing documentation and technical support.