Top AI Engineer Jobs

Build and evolve a cloud-native developer platform focusing on self-service "Golden Paths", GitOps/CI-CD tooling, observability, and AI enablement. Partner with Security and Infrastructure, mentor engineers, drive platform strategy and adoption, define reliability (SLO/SLI) and incident response, and treat the platform as a product with strong developer empathy.

Own and improve analytics datasets and ETL pipelines powering an internal product analytics AI agent. Build retrieval, knowledge, evaluation, and backend agent workflows. Improve retrieval and answer quality, develop evaluation systems, and create user-facing features. Partner with data scientists to turn analytics needs into scalable capabilities and help set roadmap and technical direction for the data and quality side of the platform.

Design and implement runtime protocols, formats, and systems to capture, serialize, stream, and replay interactive 3D content across engines. Own C++ runtime and C# APIs across editor, build, simulation, streaming, and playback. Optimize for low-latency, determinism, memory and bandwidth constraints, and build AI interfaces for inspecting and invoking engine state.

Lead design and delivery of scalable cloud services and full-stack features for a next-generation game creation platform. Build performant front-end and backend systems, optimize cloud scalability and performance, maintain CI/CD, monitoring and databases, apply agentic AI tools in development, drive technical direction, and collaborate with AI, infrastructure, and data teams.

Design and productionize agentic multimodal AI applications and multi-agent systems; architect orchestration, routing, state/memory, and human-in-the-loop workflows; build evaluation and benchmarking frameworks; optimize inference on accelerated hardware (quantization, batching, runtime tuning); profile and solve AI acceleration and memory bottlenecks; collaborate with researchers and developers to deploy and operate large-scale LLM/vision systems.

Design, evaluate, and productionize generative AI/ML solutions (RAG, agents, embeddings, retrieval). Build evaluation frameworks for hallucination detection, benchmark LLMs, optimize prompts/models, create datasets, fine-tune models, and collaborate to deploy and monitor enterprise-scale GenAI systems.

Lead development and execution of AI-specific test plans to evaluate LLM and AI system quality, robustness, security, and compliance with OWASP AI Testing Guide and related standards. Document defects and remediation, capture evidence, validate model behavior (prompt-response, hallucinations), implement AI-driven QE approaches (autonomous test generation, self-healing automation, defect prediction), and report results to stakeholders.

Define and lead an enterprise-level test automation strategy across various layers, focusing on scaling automation frameworks, improving quality, and mentoring engineers. Collaborate on testing AI systems, enhancing CI/CD processes, and reducing flakiness in automation.

Lead on-device ML inference deployment and performance optimization for outdoor camera/doorbell workloads. Optimize latency, throughput, memory, power and stability across CPU/DSP/NPU/GPU. Perform kernel/operator-level tuning, runtime integration in C/C++, quantization validation, and build profiling/benchmarking tooling. Drive cross-functional work with ML, firmware, and hardware teams and provide staff-level technical leadership and mentoring.

Build end-to-end full-stack features and scalable systems that leverage LLMs for an AI-native CRM. Collaborate with product, implement observability and metrics, participate in code reviews and mentorship, and contribute to technical vision and recruiting.

Build and operate revenue systems and GTM workflows, integrating CRM and automation tools. Translate sales and marketing needs into technical designs, create dashboards and alerts, ensure data integrity, and lead process-improvement initiatives to scale GTM operations.

Build and own AI platform systems that power Harveys agentic products: context and session management, model integration and routing, evaluation tooling, SDKs, and developer-facing abstractions. Partner with product teams to prototype and integrate new model capabilities and improve AI quality across the company.

Build and own AI platform systems including model integration, routing, context and session management, and evaluation infrastructure. Create SDKs and shared abstractions, partner with product teams, and integrate new foundation models to improve agent quality and developer experience.

Hands-on senior engineer to ramp an existing codebase and build backend services, agent tools, APIs, and data integrations for an AI-enabled data platform. Implement agent endpoints, retrieval and RAG flows, semantic and vector search, access controls, audit logging, testing, and observability within a three-month contract with potential extension.

Integrate AI capabilities into production systems by connecting models, inference endpoints, and orchestration components. Implement reliable, secure inference flows, handle latency/token limits and batching, write integration tests, monitor and refine AI integrations, and collaborate with cross-functional teams to deliver scalable AI functionality.

Build and operate Afresh's AI platform: knowledge graph and retrieval systems, LLM-powered agents and serving infrastructure, evaluation/observability, and Databricks-based pipelines to turn grocery data into production-quality model context.

Lead end-to-end development of AI-powered features and shared AI services, own product loop from discovery to production, design data and system architecture, create reusable tooling and guardrails, mentor engineers, and ensure secure handling of sensitive school and family data.

Systems Engineer with an AI focus to support the Data and Systems Integration Division in McLean, VA. Role requires active TS/SCI with polygraph, ~5+ years post-degree AI experience, support for national security customers, system integration for R&D prototyping, and project management within government research programs. Frontier AI lab experience preferred.

Design, optimize, and deploy machine learning models for resource-constrained edge devices. Focus on model compression, quantization, hardware-aware optimization, performance profiling, and production deployment to mobile/embedded platforms while addressing on-device privacy, security, and cross-functional collaboration.

Design and build AI-enabled robotics proofs of concept, then deploy and integrate them in customer sites. Responsibilities include camera placement and calibration, model deployment, I/O configuration, on-site troubleshooting, and customer training. Collaborate with sales and engineering teams, produce documentation and integration guides, and feed deployment learnings back into product development.

Design, develop, test, and productize AI/ML algorithms: select and process data, run statistical analysis and ML models, perform test & evaluation and V&V, integrate systems, retrain models, verify model quality, and convert prototypes into scalable solutions.

Build and own core parts of an AI-native automated testing platform. Integrate and tune LLMs for production, implement LLM evaluation and observability, perform SFT/RLFT, engineer evaluation datasets and prompts, and ship full-stack features using Python/TypeScript in an in-person San Francisco team.

Early-career role to build, operate, and improve cloud data/analytics platforms (Fabric, Snowflake, Databricks). Support platform operations, CI/CD for analytics assets, onboarding for BI tools (Power BI/Tableau), runbooks, monitoring, incident response, and basic security/governance. Work under senior mentorship and contribute to automation, deployment templates, and performance triage.

Design and build hands-on curriculum and lab environments teaching technical learners to build, configure, and deploy agentic AI on Veeva's platform. Create working AI agent demos and reference implementations, maintain advanced Java and REST API coding courses, partner with Product and Engineering, and apply instructional design to complex technical content.

Design and build hands-on technical curriculum and lab environments teaching how to build, configure, deploy, and troubleshoot AI agents. Create working agent demos, Java/REST-based exercises, partner with Product and Engineering, and apply instructional design to advanced coding-intensive courses for customers, partners, and internal teams.