Top AI Engineer Jobs

The Software Engineer, AI will develop intelligent systems for financial workflows, utilizing LLMs and other technologies to enhance bookkeeping and data processing for agriculture businesses.

Develop and optimize end-to-end data pipelines for autonomous driving, ensure data quality, and support model training through efficient data management processes.

Design, build, and operate scalable data and AI platform capabilities. Create reusable abstractions, templates, and services for data pipelines and products. Integrate Generative AI for discovery and automation, ensure data quality, lineage, observability, security, and support cross-functional analytics and applied-AI teams.

The role involves developing AI and machine learning solutions to improve organizational efficiency, ensuring user-focused system modernization and compliance with cybersecurity standards.

Design and implement end-to-end AI/ML software solutions to modernize client systems. Build developer-focused tools and prototypes, apply best practices for software workflows, and collaborate to refine experimental tooling while considering ecosystem and future enhancements.

Design, build, and operate large-scale production systems and integrate diverse technologies. Develop AI/ML-enabled end-to-end solutions on AWS, manage Kubernetes deployments, and implement observability. Lead technical initiatives, support policy/governance, and work within TS/SCI polygraph security requirements.

Partner with Sales to design and demo AI-powered, data-driven solutions for life sciences clients using Komodo's Healthcare Map. Run live no-code analyses, build reusable demo assets, navigate AI governance and procurement, quantify ROI, mentor junior SEs, and feed product/AI roadmap input.

The Lead Software Engineer will mentor a frontend team while actively contributing to development, focusing on quality and collaboration with product and design partners.

Build and maintain secure RESTful and event-driven APIs and MCP server implementations to allow AI agents to interact with enterprise platforms (ServiceNow, Okta, ERP). Implement authentication (OAuth2/OIDC), API gateway policies, audit logging, and monitoring. Collaborate with AI and governance teams, document integrations, and ensure secure, reliable, auditable agentic workflows.

The Data/AI Cloud Engineer will implement AWS data services, build ETL pipelines, optimize databases, and support data-driven solutions collaborating with technical teams.

As a Senior Engineer 2, you'll lead complex AI system designs, oversee team quality, integrate LLM APIs, mentor junior engineers, and collaborate across teams to enhance the AI Agentic Platform.

Lead R&D on LLM training and inference optimization, train and evaluate large-scale models on Tenstorrent hardware, improve performance via techniques like speculative decoding, quantization, kernel fusion, flash attention and distributed training, diagnose system bottlenecks, and translate research into scalable production solutions through cross-functional collaboration.

The AI Automation Engineer will apply AI in finance operations to automate processes like AP, AR, and reconciliation, ensuring compliance and performance measurement.

As a Missions Software Engineer, you'll collaborate with defense customers to integrate and operationalize vehicle intelligence products, tackling software engineering, hardware integration, and project execution challenges.

As a C++ Software Engineer, you will design high-performance backend systems for autonomy products, tackle complex engineering challenges, and collaborate with teams to deliver robust solutions for defense applications.

The Lead Software Engineer will design and implement backend systems in C++, tackle autonomy challenges, and ensure robust software solutions meet real-world operational demands.

Deploy and integrate AI-enabled SOC solutions across customer environments: design cloud-native ingestion pipelines, integrate SIEM/XDR/SOAR and tooling, implement automation and AI workflows, improve analyst experience, troubleshoot production issues, and provide technical leadership and field-facing delivery.

You will build and maintain AI-native application features, working with a team of AI and backend engineers to ensure quality and innovation.

The role involves architecting scalable backend systems for real-time evaluations, integrating with streaming data, and building intervention layers for AI applications. You'll design frameworks and operationalize vector databases while ensuring low-latency service performance.

Build and improve production AI/data pipelines, design model evaluation workflows and Jupyter analyses, add observability to AI systems, experiment with models/retrieval/prompt architectures, integrate and abstract AI APIs, and help define inference serving, fine-tuning, and dataset-generation infrastructure.

Lead design and delivery of cloud-native, event-driven platforms and applications. Drive full lifecycle development, champion engineering best practices, implement production-ready solutions, and build AI/LLM capabilities like RAG, embeddings, and model CI/CD across multi-cloud environments.

Build and ship production AI/LLM-powered workflows and platform services that operate over sensitive enterprise and government data. Design context/grounding, permissioning, orchestration, tracing, evals, and runtime visibility. Work across backend services, APIs, async workers, data pipelines, and product surfaces, owning reliability, auditability, and performance.

Develop and implement compiler optimization algorithms for deep learning workloads, analyze networks, debug and profile GPU performance (CUDA), define public APIs, and deliver ahead-of-time and just-in-time compilation techniques for NVIDIA GPUs to improve inference performance, build times, and memory efficiency.

Contract SRE to build, operate, and automate colo, on‑prem GPU clusters, and cloud infrastructure. Own provisioning, IaC (Terraform/Ansible), observability (Prometheus/Grafana or DataDog), incident response, runbooks, and customer-facing platform reliability.

Build and productionize backend-first AI capabilities for a FinOps intelligence platform: design backend services, implement AI features (anomaly detection, recommendations, agents), create evaluation/rollback/monitoring systems, and collaborate with product to ship reliable, cost-efficient, production-ready features.