Top AI Engineer Jobs

Design and implement full-stack applications and AI agents to enable rapid development and deployment of AI solutions in critical industries. Mentor engineers and take ownership of projects while applying secure SDLC practices and collaborating with cross-functional teams.

As a Senior Software Engineer, you will enhance AI application interfaces for enterprise clients, ensuring high product quality and a seamless user experience through direct customer collaboration and rapid iteration.

The AI Enablement Engineer will build and deploy AI agents, design intelligent workflows, and collaborate with teams to improve processes using AI technologies.

As a Senior Software Engineer for AI products, you will own features across the stack, develop the AI SRE product, create integrations, and collaborate on user experience while ensuring technical quality.

The Senior Staff Engineer will lead the development of an underwriting platform, enhance AI integration, mentor junior engineers, and collaborate with teams to transform GEICO's underwriting processes through tech innovations.

Develop and support Rescale's HPC platform, integrate AI into CAE workflows, solve customer HPC/AI problems, build AI software, collaborate with engineering teams, and engage directly with customers.

Lead design, validation, and optimization of utility-scale BESS for data centers and grid applications. Responsibilities include battery sizing, lifecycle modeling, vendor model review, inverter/PCS and controls validation, EMS/Plant Controller and SCADA integration, and operational analytics using Python and engineering tools.

Design, deploy, secure, and maintain enterprise Windows endpoints (Windows 10/11) supporting AI-enabled desktop capabilities. Administer Intune/Endpoint Manager, implement STIG-compliant baselines, configure Conditional Access and Zero Trust controls, automate provisioning with Autopilot and PowerShell, integrate endpoint telemetry with security tools, and support audits and compliance in government cloud environments.

Design, build, deploy, and operate AI/ML capabilities in AWS GovCloud using FedRAMP-authorized services. Responsibilities include data preparation, model development, evaluation, deployment, monitoring, RAG implementations, embedding/vector search integration, Python development of production services, containerized deployments, CI/CD, and collaboration with engineering and cybersecurity teams to meet mission and compliance requirements.

Design, build, and run Claude-powered agentic workflows that automate partner operations (application triage, onboarding, deal registration, communications, reporting). Integrate automations with partner portal, CRM, enablement, finance, and databases, own production reliability, add guardrails and human checkpoints, and create reusable patterns so partner teams can self-serve.

The Senior Staff Machine Learning Engineer will lead innovative AI engineering projects and optimize algorithms for e-commerce applications, collaborating across teams to drive significant business impact.

Support climate and Earth system researchers by developing, deploying, and optimizing AI/ML workflows on HPC and GPU-accelerated systems. Assist with compiling, profiling, tuning, and porting scientific applications; optimize system utilization (CPU/GPU, memory, storage, I/O); maintain scientific software environments and datasets; provide consulting, training, documentation, and grant support to advance AI-enabled climate research.

Design, build, and deploy AI-powered applications, automations, dashboards, and integrations to improve operational efficiency. Evaluate LLMs and automation platforms, create data-driven BI tools, integrate systems via APIs and cloud services, train staff on AI tools, and establish governance and strategy for responsible AI use.

Lead design, build, and maintain scalable AI agents and backend systems for real-time, high-volume security operations. Integrate LLMs/GenAI, design data flows, APIs, databases, and enterprise authentication, collaborate cross-functionally, monitor and improve system reliability and compliance.

Design and optimize AI communication middleware for distributed multi-node, multi-GPU training. Implement C/C++ performance-critical features, improve GPU communication paths, profile and tune frameworks (PyTorch, TensorFlow, JAX, DeepSpeed), develop benchmarks, contribute upstream, and collaborate with kernel, driver, switch, and systems teams to validate and deploy high-performance networking for AI/HPC.

Build and ship production AI systems and agentic workflows in client codebases, produce public educational content, lead workshops/hackathons, and manage end-to-end client features and deployments.

Define and build Fuse's inference-serving architecture and stack, including request routing, batching, scheduling, and autoscaling for latency-sensitive workloads. Own model-level optimizations (quantisation, distillation, speculative decoding), translate performance SLAs into capacity plans, and integrate low-level GPU/CUDA performance work with serving infrastructure. Set benchmarks, tooling, and act as technical owner of inference performance and reliability.

Embed with business teams to identify workflows, rapidly prototype, and deliver production AI applications using LLMs, agents, RAG, and enterprise integrations. Build full-stack solutions, integrate with platforms (Microsoft 365, Salesforce, Oracle), deploy to cloud, and create reusable AI components and standards to drive enterprise adoption and measurable business value.

Lead operations for large-scale AI infrastructure: manage NVIDIA GPU and high-performance networking environments, troubleshoot Linux/Kubernetes/storage/hardware issues, drive incident response and root cause analysis, improve observability and automation, mentor engineers, and collaborate with engineering and datacenter teams to ensure platform reliability and scalability.

Lead the AI engineering org for a YC-experienced startup: drive 0->1 product development, design full-stack architectures, partner with customers and product, mentor engineers, and establish scalable production AI systems and technical strategy.

Embed with client teams to design, build, and ship AI-driven integrations and automations. Responsibilities include prompt and retrieval design, prototyping with no-code tools and custom Python/JavaScript, hardening prototypes for production (reliability, logging, monitoring, access control), handling sensitive data, producing runbooks/specs, and collaborating with platform and engagement leads to drive adoption and reusable patterns.

Lead enterprise POCs and pre-sales technical engagements to turn prototypes into production deployments. Troubleshoot integrations across identity, security, networking, and observability; design custom configurations; run onboarding and enablement; and act as the technical lead during sales cycles to ensure successful rollout and adoption.

Lead technical POCs and pre-sales engagements to turn proofs-of-concept into production deployments. Troubleshoot complex integrations (identity, security, networking, observability), design custom solutions, run onboarding and enablement, and act as the technical lead during sales to ensure successful rollout and adoption.

Design and build backend systems and internal tooling for AI-native products: secure data access for agents, agent orchestration, evaluation/observability for non-deterministic outputs, performance and latency optimization, and end-to-end feature ownership from concept to production.

Embed with client teams to design, build, and productionize AI-driven workflows and automations. Own workstreams from prototype to reliable production: prompt design, retrieval, model selection, integrations, error handling, monitoring, and documentation to drive measurable operational impact and reusable platform patterns.