Top AI Engineer Jobs

As an AI Engineer at Campfire, you'll design and implement AI solutions for accounting software, working on automating workflows and building intelligent features for real-time insights.

Design, build, and operate customer-facing AI systems through discovery, prototyping, deployment, monitoring, and scaling. Translate ambiguous problems into technical specs and prototypes, own system architecture and tradeoffs, collaborate with product teams, and improve tooling using frontier models and cloud platforms.

The Applied AI Engineer will develop AI-powered systems to streamline internal processes, collaborating with various departments to enhance efficiency and automation. This role involves problem ownership, system design, and deploying solutions that leverage AI technology while ensuring user adoption and long-term value.

As a Senior AI Engineer at BioRender, you'll develop AI-driven features, collaborate with cross-functional teams, and prototype innovative solutions to enhance scientific communication.

As a Machine Learning Engineer, you will optimize backend services, integrate ML models, collaborate with cross-functional teams, and support continuous improvement initiatives.

Build and own end-to-end AI features across the stack: prompt engineering, evals, vector search, backend/data models, and frontend interactions. Ensure accuracy, safety, speed, and on-brand AI outputs. Collaborate with Product and Design to iterate on generative AI features used daily (coaching, candidate summaries, natural-language assistant).

Design and implement onboard perception systems (computer vision, localization, visual-motor policies, and data annotation), own end-to-end product lifecycle, deliver production-grade software, and collaborate cross-functionally to optimize the autonomy stack for humanoid robots.

The role involves architecting and implementing LLM-powered tools to enhance developer productivity, automate mundane tasks, and establish best practices for AI integration in development processes.

Design and implement AI-driven tools to improve workflows across teams. Collaborate to identify internal problems AI can solve, and prototype solutions quickly.

Build and deploy AI-powered tools and workflows across performance marketing, creative production, CRM, landing pages, growth operations, and product. Develop LLM integrations, personalization systems, automation pipelines, dashboards, Slack bots, and internal tools using marketing platforms and data infrastructure. Partner with Growth, Product, and Engineering teams, evaluate AI tooling, mentor nontechnical users, and translate technical solutions into measurable business impact.

Own the end-to-end tooling ecosystem for embodied AI, including robotic and simulation data acquisition, lifecycle pipelines, multimodal sensor synchronization, labeling, teleoperation, remote orchestration, and real-time monitoring. Architect scalable simulation, hardware-in-the-loop, and cloud infrastructure; establish data and communication standards; optimize pipelines; and lead collaboration across AI research, hardware, and robotics teams to deliver production-ready systems.

Own strategic enterprise deployments of production agentic AI systems, including multi-turn voice and digital agents, retrieval pipelines, integrations, guardrails, monitoring, evaluation, and ROI measurement. Support pre-sales demonstrations and proof-of-concept builds while translating customer feedback into product and engineering roadmap improvements. The role requires hands-on production coding, customer-facing technical leadership, and comfort operating in an emerging forward-deployed engineering function.

Build, test, tune, and scale SIEM and data-lake detection rules mapped to MITRE ATT&CK. Manage detection-as-code through Git, pull requests, and CI/CD pipelines; reduce false positives and improve alert fidelity. Collaborate with threat intelligence, threat hunting, SOC, and data engineering teams. Use AI agents, LLM-assisted workflows, and AI/ML techniques to accelerate validation, coverage analysis, anomaly detection, and behavioral analytics. Monitor emerging threats and document detection logic and tuning decisions.

Lead the reliability, stability, observability, and scalability of Seekr’s software platform. Design and implement systems, networks, and services that meet SLAs; develop monitoring, automation, and failure-detection solutions; conduct load testing; manage observability tools; troubleshoot production and deployment issues; and support incident response. Collaborate closely with software engineering teams across hybrid cloud and on-premises environments.

Leads enterprise AI architecture and technical vision across multiple products. Designs RAG and agent workflows, integrates and optimizes LLMs, and builds secure, observable, high-availability, multi-tenant systems. Establishes AI security, monitoring, and cost-management standards while partnering with product, data science, and engineering leaders. Mentors technical teams, drives cross-functional initiatives, and guides technology roadmaps. The role requires advanced cloud, containerization, vector database, embedding optimization, and production-scale AI application experience.

Own operations for AI application infrastructure, including system monitoring, incident response, SLA/SLO governance, capacity management, help desk support, and production readiness. Lead root cause reviews, operational strategy, process improvements, and reliability initiatives across deployment and support workflows. Partner with development, operations, leadership, and external providers; oversee L1/L2/L3 support, mentor team members, and apply AI Ops, MLOps, DevSecOps, observability, and cloud practices.

Build and maintain Trustly’s AI platform, including LLM infrastructure, prompt management, evaluation pipelines, and agentic workflow tooling. Partner with teams to embed AI into products, establish testing standards, evaluate models and architectures, and drive internal adoption through reusable libraries, documentation, and enablement resources. The role requires staff-level technical leadership, cross-functional influence, and taking AI systems from prototype to reliable production at scale.

Build and configure AI agents and ML models for healthcare finance customers using existing platform infrastructure. Partner with customers and product teams to scope solutions, create visualizations, present AI capabilities, validate model reliability, and establish testing rigor. Generalize customer-specific solutions into reusable platform features while shipping changes through code review and automated CI/CD.

Lead design and implementation of end-to-end AI/ML solutions with emphasis on LLM fine-tuning/RAG, MLOps, cloud platform evaluation and setup (Azure/Oracle), model deployment and monitoring, and integrating model outputs into business reports (PowerBI/Oracle Analytics). Document processes and collaborate with cross-functional teams.

Build, test, tune, and scale SIEM and data-lake detection rules using threat intelligence, MITRE ATT&CK, and detection-as-code workflows. Investigate false positives, improve alert fidelity, collaborate with SOC, threat hunting, threat intelligence, and data engineering teams, and develop coverage for emerging threats. Use AI agents, LLM-assisted tooling, MCP, and machine-learning techniques to accelerate rule development, validation, telemetry analysis, and anomaly detection.

Build and scale an AI-powered audit workbench that transforms raw evidence into reviewable workpapers. Responsibilities include designing ingestion and extraction pipelines, deterministic recomputation and exception detection, workpaper generation, evaluation infrastructure, and frontend review tools. The role emphasizes production LLM reliability, golden-dataset evaluations, schema design, traceability, deterministic regulatory logic, structured human review, and end-to-end system ownership in a regulated domain.

Lead frontend development for Sift Agents, building high-performance, agentic AI user experiences. Own product area from customer discovery through shipping, design interaction patterns for streaming and long-running agent tasks, render agent findings alongside large telemetry datasets, optimize browser performance (Web Workers, OPFS, WebAssembly), contribute to component library, and collaborate with backend engineers on API design.

Build and own frontend surfaces for agentic AI features: design streaming and long-running agent interactions, render agent findings alongside large telemetry datasets, optimize browser performance (Web Workers, OPFS, WebAssembly), contribute to component library, and collaborate with backend and product teams while engaging customers to shape the product.

Own and build the frontend for agentic AI features: design agentic UX, streaming responses, long-running tasks, and render agent findings alongside large telemetry datasets. Optimize browser performance (Web Workers, OPFS, WebAssembly), contribute to component libraries, collaborate on APIs, and work directly with customers to translate workflows into product features.

Build and own frontend surfaces for agentic AI used by hardware engineers: design streaming/long-running interactions, render agent outputs alongside large telemetry, optimize browser performance (Web Workers, OPFS, WebAssembly), contribute to component library, and collaborate with customers and backend engineers from MVP through iteration.