Top AI Engineer Jobs

Develop and operate a system for argumentation related to AI safety, utilizing ontologies and knowledge graphs while collaborating with various stakeholders.

The Research Engineer will develop AI platforms focusing on alignment challenges, multi-agent systems, and optimization algorithms while collaborating on technical publications and prototypes.

Develop and maintain LLM pipelines, design RAG architectures, implement agentic systems, and integrate AI features into data pipelines.

The Software Engineer - AI Systems will design and implement systems to coordinate LLMs, build testable software stacks, conduct research, and collaborate with team members to create AI-powered security solutions.

Design and implement cybersecurity controls, assess vulnerabilities and mitigation strategies, integrate security across the SDLC, run security assessments and audits, review security tool outputs to identify threats, support deployment and administration of IAM/PAM/endpoint/DLP/encryption technologies, and document processes.

The Staff Software Engineer will lead the development of cloud-native AI platforms, optimizing AI workload deployment in Kubernetes environments and collaborating with cross-functional teams.

Design, build, and deliver an AI and machine learning platform, enhancing developer velocity while collaborating with cross-functional teams.

Develop and optimize AI models and training systems for robotics applications, focusing on design, implementation, and performance optimization of ML models.

Support DoD Emerging Technology Mission Assurance by researching, evaluating, prototyping, and integrating AI/ML/Generative AI solutions. Design and run technology evaluations, PoCs, and lab tests; develop assessment reports, architecture, CONOPS, and migration roadmaps; evaluate model performance, scalability, security, and cloud integrations; brief stakeholders and support transitioning capabilities to operational environments.

Build and deploy AI/LLM-powered automations and API integrations to streamline back-office processes. Partner with cross-functional teams to gather requirements, design workflows with orchestration tools, write tested production code, measure automation impact, and stay current on AI tooling.

Embed with business teams to discover high-value AI opportunities, design and build agentic AI-enabled workflows and integrations, validate prototypes with users, iterate for production readiness, document risks and controls, and convert solutions into reusable patterns while collaborating with governance, security, and data partners.

The job involves building responsive web dashboards, advanced data visualizations, and analytics tools for unmanned aircraft systems, requiring a deep understanding of UI/UX design and data presentation.

As an AI Product Engineer, you will develop and deploy LLM-powered language learning products, enhance existing features, and collaborate cross-functionally to optimize performance and user experience.

Lead end-to-end AI-first transformation for operations: define problems, design architecture, build agentic systems and automation pipelines, integrate with CRMs and operational tooling, ship production solutions and drive company-wide AI adoption.

Own and ship backend systems and agentic AI features for telemetry analysis: design agent tool interfaces, build distributed job execution on Kubernetes, implement APIs and MCP server, integrate frontier models, and build evaluation and observability for production agent workloads.

Lead design, build, and operate backend agentic systems that analyze large-scale time-series telemetry. Own product from customer discovery through shipping. Implement APIs, distributed job execution on Kubernetes, sandboxed code execution, context/memory management, evaluation suites, and observability while integrating frontier models and scaling agent workloads.

Build and operate backend systems for agentic AI that analyze large-scale time-series telemetry. Own product features from customer discovery through deployment: APIs, distributed job execution on Kubernetes, sandboxed code execution, context management, observability, model integration, and evaluation suites.

Build and operate agentic backend systems that reason over large-scale time-series telemetry. Design APIs, tool interfaces, sandboxed code execution, distributed job scheduling on Kubernetes, observability, and evaluation suites. Partner with customers and product to turn real workflows into dependable AI agent capabilities across cloud and on-prem deployments.

As a Research Engineer, you will optimize AI performance, focusing on kernel development for large-scale ML workloads, profiling bottlenecks, and collaborating with teams to enhance model training and inference.

Develop cloud-native web, mobile, data marketplace and API features; migrate products to public cloud; build and optimize CI/CD pipelines and test automation; ensure production reliability and performance; participate in Agile/Scrum, peer reviews, and continuous improvement.

Automate and support assembly, integration, and test of satellite production systems. Develop and maintain Python/Bash automation and data-collection scripts, troubleshoot hardware/software interfaces, commission test equipment, execute software updates, maintain documentation, and ensure aerospace quality and cleanroom compliance to improve production throughput.

Lead design and delivery of ML, generative AI, and agentic AI solutions for finance use cases. Architect secure, scalable AWS-based systems, implement MLOps, CI/CD, observability, data/model pipelines, APIs, and production monitoring. Mentor engineers, collaborate with cross-functional stakeholders (risk, compliance, business), and ensure compliance, auditability, and cost controls for production AI systems.

Design and build Renterra's data and AI foundation: event architecture, data pipelines, analytical systems, and ClickHouse-based queries. Create AI agents and intelligent workflows that use company data to solve customer problems. Ensure production reliability, observability, security, and measurement via evaluations and feedback loops. Partner across product, engineering, leadership, and customers; report to the CTO.

Build and own full-stack features for core user journeys, shipping backend APIs, async workflows, and frontend experiences. Contribute to architecture, write well-tested maintainable code, partner with product and design, and proactively address technical debt and performance issues.

Build high-performance, scalable React+TypeScript web applications for payment experiences. Design reusable UI components and optimize frontend performance while collaborating with cross-functional teams on architecture, testing, accessibility, and product delivery.