Top AI Engineer Jobs

Build end-to-end AI integrations in a cybersecurity platform: gather contextual security data, call and optimize AI services, map responses to actionable UI (React), implement error/fallback handling, monitoring, caching, and user feedback loops while working directly with customers.

Own reliability, performance, and evolution of workplace technology across Microsoft 365, identity, and endpoints. Lead incident response, problem management, automation, and ITSM; design secure scalable services; drive strategy and Copilot/GenAI adoption; mentor engineers and influence cross-functional stakeholders to improve reliability and operational excellence.

As a System Software Engineer, you will design communication primitives for AI models, optimize performance for training workloads, and benchmark performance on clusters.

Design, build, and maintain FedRAMP-compliant platform services for cloud traffic, ingress/egress, identity, and cryptographic controls. Lead migrations, implement IAM (Okta, SAML/OIDC, RBAC), enforce FIPS-validated cryptography, harden Kubernetes and secrets management, automate compliance (NIST SP 800-53, ConMon), and mentor engineers while partnering with security teams for assessment readiness.

Design, build, and productionize LLM-based conversational and AI systems for enterprise coaching. Integrate structured and unstructured data, prototype end-to-end services, improve model behavior and observability, and collaborate with product and engineering to deploy scalable, reliable AI-driven coaching workflows.

The role involves building and improving AI voice features, debugging issues, and collaborating with product teams to enhance user experience.

Design, build, and operate large-scale ML training and evaluation infrastructure for RL/AI research. Enable distributed, cost-aware GPU training, benchmarking, data curation, and continuous evaluation; collaborate with research and platform teams to productionize models.

Design and implement high-performance C++ backend systems for defense autonomy products. Build scalable, reliable infrastructure for contested environments, collaborate with researchers and customers, own architecture through deployment, and support Ann Arbor site growth while interfacing with DoD programs.

Build and productionize scalable backend services, APIs, and AI workflows to connect requirements, modeling, validation, and test data for autonomous vehicle programs. Implement traceability, versioning, dashboards, and KPIs, refactor prototypes into certifiable systems, and collaborate with systems engineering, safety, and autonomy teams to support compliance and validation at scale.

Build and ship shared AI infrastructure, agent frameworks, and tooling; partner with product teams to productionize LLM and agentic features; own GenAI best practices for architecture, safety, evaluation, and deployment; develop agentic systems and AI SRE tooling; engage with customers to shape roadmap and demos.

Engineer large-scale scientific data services to enable AI for science: design data ingestion, indexing, and search for terabyte-to-petabyte datasets; expose data via high-performance APIs and usable web apps; deploy and optimize services in Kubernetes and HPC environments; collaborate with scientists and partners on data structuring, performance profiling, and secure multi-user access.

Develop, train, fine-tune, and deploy AI/ML models (including LLMs and generative AI). Build user-facing AI applications (chatbots, virtual assistants), optimize model performance, run experiments (prompt engineering, RAGs, fine-tuning), integrate solutions with systems, ensure data privacy/security, and brief stakeholders. Support big-data approaches and collaborate across teams.

Build and maintain production AI/ML models and pipelines for an NDR platform; develop scalable, secure backend services; optimize detection algorithms and reduce false positives; integrate ML features into product architecture; mentor engineers; participate in code reviews, technical strategy, and documentation; operate cloud-native, containerized services and manage infrastructure as code.

The role involves optimizing analog-aware software for neural networks, designing algorithms, improving software quality, and collaborating on hardware validation. Requires experience with complex algorithms and communication skills.

Build and maintain integrations across AI frameworks, MCPs, SDKs, and developer platforms. Ship plugins, connectors, templates, and agent tooling that let developers access Nimble. Contribute to open-source, publish examples, engage developer communities, and support partnerships and events to drive adoption.

Design, build, and maintain scalable data pipelines and architectures. Perform data extraction, cleansing, transformation, and ensure data quality and accessibility. Collaborate with data scientists, analysts, and product teams to deliver data solutions and troubleshoot data issues.

Design, train, evaluate, and deploy multimodal and generative AI models and multi-agent systems for covalent small-molecule drug discovery. Build molecular representations (GNNs, equivariant nets), RAG pipelines, property-prediction and virtual-screening models, and production serving infrastructure to support medicinal chemistry and cross-functional discovery teams.

Design, prototype, and productionize AI systems and infrastructure—recommendations, natural language interfaces, agentic workflows—partnering with product and engineering to scale ML across the full lifecycle (data curation, training, deployment, monitoring).

Build and productionize AI systems and infrastructure that power customer-facing features and internal tooling. Partner with product, design, and engineering to identify high-impact ML opportunities, prototype solutions (recommendations, NL interfaces, agents), and manage the full ML lifecycle from data curation to deployment and monitoring.

Design, prototype, and productionize AI/ML systems (recommendations, NL interfaces, agentic workflows). Build and scale ML infrastructure, partner with product/design/engineering, and solve UX problems at the intersection of AI and BI.

Design, prototype, and productionize AI systems (recommendations, natural language interfaces, agents). Partner with product and engineering to identify AI opportunities, build scalable ML infrastructure, and manage the full ML lifecycle from data curation through training, deployment, and monitoring.

Design and implement end-to-end ML/NLP workflows (semantic search, classification, entity resolution, retrieval/RAG). Build production AI services, integrate with data/backend systems, support deployment/monitoring/versioning, evaluate performance, and contribute to architecture and mission-aligned solutions.

Sola seeks a Software Engineer for Desktop Automation to own and enhance their execution platform, focusing on reliability and integrating with AI pipelines.

Design, prototype, and deploy production-ready AI features using LLMs and orchestration tools. Build scalable APIs, implement monitoring/safety, integrate Vertex AI/OpenAI, and lead AI integration projects with product and engineering.

Design, implement, and maintain cloud-based AI infrastructure; build CI/CD pipelines and containerized deployments; manage AI model lifecycle, monitoring, scalability, and security; collaborate with AI/ML teams to optimize deployment and resource usage.