Top AI Engineer Jobs

Develop and deploy machine learning models, datasets, algorithms, and training recipes to improve AI safety for multimodal LLMs and agentic systems. Work across LLM security, frontier risks, agentic safety, multi-turn evaluation, content safety, hallucinations, and fairness. Apply post-training methods including SFT and RLHF/RLAIF, conduct safety evaluations and research, and collaborate cross-functionally with engineering, data science, and research teams.

Build, test, maintain, and support enterprise AI platform capabilities, including APIs, backend services, workflows, automation, integrations, and governed generative AI solutions. Contribute to prompt management, retrieval-augmented generation, model routing, evaluation, observability, secure data access, platform onboarding, testing, documentation, monitoring, incident resolution, and responsible AI practices. Collaborate with engineers, architects, product owners, security teams, data teams, and business stakeholders to deliver reliable production services.

Design and optimize high-performance AI compute kernels for GEMM, attention, normalization, convolution, and related primitives. Improve throughput, latency, memory behavior, and precision across heterogeneous hardware. Integrate kernels with Triton and PyTorch, profile workloads using performance tools, evaluate mixed-precision formats, and collaborate with compiler, runtime, and hardware teams on ISA and memory architecture co-design.

Build and operate reliable, scalable cloud infrastructure for AI workflows, advertising operations, and data systems. Responsibilities include designing asynchronous and event-driven systems, maintaining CI/CD and infrastructure automation, improving observability, security, performance, and cost efficiency, and leading incident response and disaster recovery. The role partners across engineering and product teams on capacity planning, reliability, troubleshooting, and post-incident improvements.

Build and test production-grade AI agents, copilots, and automated workflows for Roku engineering teams. Interns will connect agents to data, services, and tools; evaluate reliability and failure modes; write tested and documented code; participate in reviews; and collaborate with engineers and mentors. The 12-week paid internship includes ownership of a scoped project from problem definition through delivery.

Build and support enterprise AI-orchestrated workflows and API integrations across ITSM, HR, identity, access, and internal business systems. Develop Python and JSON automation logic, troubleshoot APIs and authentication, and implement logging, monitoring, retries, correlation IDs, and error handling. Review AI-generated code, support production workflows, collaborate with stakeholders, and create reusable automation templates and documentation while following security, governance, testing, and release standards.

Designs and develops production-ready AI applications powered by large language models. Responsibilities include building backend orchestration services, user interfaces, LLM API integrations, prompt engineering workflows, RAG architectures, enterprise data connectors, and scalable microservices. The role also evaluates AI performance, supports responsible AI practices, prototypes conversational tools, and collaborates with product, data science, and business teams to deliver AI-enabled solutions for a global manufacturing company.

Leads enterprise AI innovation through hypothesis-driven pilots, evaluation, and experimentation with agentic workflows, RAG, text-to-SQL, and emerging LLM techniques. Designs secure, observable, cost-aware AWS architectures, develops reference implementations, and transitions successful prototypes into scalable enterprise patterns. Communicates technical findings, business impact, tradeoffs, and recommendations while evaluating AI tools, frameworks, vendors, and research.

Researches, develops, trains, and evaluates reinforcement learning algorithms for defense modeling, simulation, and decision-support applications. Builds Python and MATLAB software, custom simulation environments, reward and action/observation designs, reproducible experimentation pipelines, and integrations with C++ simulation systems. Uses Ray RLlib, GPU and distributed computing, and analyzes algorithm performance. Collaborates with software engineers, simulation developers, and subject matter experts to transition research concepts into operationally relevant prototypes.

Designs and delivers customer-facing API integrations, ETL pipelines, webhook automations, and AI-assisted workflows for nonprofit organizations. Owns discovery, architecture, development, testing, deployment, documentation, monitoring, and iteration. Productizes repeatable solutions into scalable service offerings, collaborates with Sales, Customer Success, and Product, and contributes to scoping, pricing, revenue reporting, and product feedback.

Designs and deploys security controls for AI applications, agents, copilots, model platforms, and enterprise AI infrastructure. Builds APIs, services, policy checks, automation, and integrations governing identity, authorization, secrets, data access, tool use, logging, and egress. Performs AI threat modeling, security reviews, adversarial testing, and remediation for prompt injection and unsafe tool execution. Integrates security telemetry and response workflows while mentoring engineers and influencing enterprise security practices.

Leads the design, development, and operation of production AI applications using LLM agents, RAG pipelines, AWS cloud services, Kubernetes, and modern full-stack technologies. Owns retrieval quality, evaluation, observability, infrastructure as code, CI/CD, production troubleshooting, and technical mentorship. The role requires shipping reliable AI systems, influencing architecture and business priorities, and collaborating across engineering and business teams.

Designs, implements, and optimizes Microsoft 365 security, compliance, identity, and governance solutions for clients. Configures Microsoft Purview, Entra ID, Defender, DLP, information protection, access controls, and AI-agent security capabilities. Assesses environments, identifies gaps, develops remediation plans, automates administration with PowerShell, and delivers documentation, workshops, testing, and knowledge transfer. The role requires strong consulting and customer-facing skills while supporting technical project delivery across enterprise environments.

Develop and support cloud-native Python backend services, RESTful APIs, integrations, and data solutions. Help move AI/ML models and pipelines into production for governance, risk, and compliance applications. Write automated tests, troubleshoot development and production issues, and collaborate with product, architecture, DevOps, and infrastructure teams in an Agile environment.

Lead complex customer engagements from discovery through production delivery by architecting, building, and deploying AI solutions. Write and review production code, guide senior engineers, make architecture and deployment decisions, and translate customer-specific learnings into scalable CoCounsel product capabilities. The role requires hands-on expertise in generative AI, LLM systems, enterprise integrations, distributed systems, cloud architecture, security, reliability, and production operations, along with strong technical leadership and customer communication.

Build and deploy secure, production AI applications for tax and audit professionals, including RAG pipelines, custom agents, and agentic workflows. Develop scalable Python backend services, integrate with client and Thomson Reuters systems, and establish evaluation, observability, monitoring, and testing practices. Lead client delivery, translate ambiguous business needs into technical solutions, support deployments and incidents, and collaborate with AI engineers and senior technical leaders across multiple engagements.

Build and deliver enterprise integrations connecting AI solutions with tax and audit products, ERP systems, document platforms, and data warehouses. Develop document-processing, retrieval, embedding, and vector-search pipelines; enable AI agents through APIs and MCP; and establish data validation and quality controls. Implement secure authentication, testing, monitoring, deployment patterns, and operational runbooks. Partner with clients and cross-functional engineering, platform, consulting, and reliability teams to deliver reusable, production-ready solutions.

Build and maintain ERP and business-system integrations, automation, data transformations, and AI-enabled features. Translate requirements into tested, secure solutions; deploy, monitor, troubleshoot, document, and support implementations. Collaborate with engagement leaders, client IT teams, and business stakeholders while applying version control, testing, access controls, and human review for AI functionality. The role requires 3–5 years of relevant engineering experience, programming and SQL proficiency, and may involve up to 30% travel.

Architect and build Traba’s AI agent platform, including orchestration, model strategy, retrieval, memory, tools, evaluation, observability, and integrations with WMS, TMS, ERP, and customer systems. Lead foundational technical decisions, spend time with customers to shape deployments, establish quality and reliability standards, hire and mentor engineers, and partner with company leadership on the platform roadmap.

Build and scale Traba’s AI-powered staffing marketplace by integrating existing AI agents and frontier models into product workflows. Develop frontend and backend systems for worker vetting, matching, fulfillment, payments, real-time matching, distributed services, and APIs. Own product surfaces end to end, collaborate with product and design, mentor engineers, and partner with the CTO on architecture and roadmap.

Build and lead Traba’s AI-powered staffing platform, integrating frontier models and agents into production workflows. Architect scalable backend systems for real-time matching, autonomous vetting, data processing, and platform infrastructure. Own technical architecture, performance, reliability, and scale while partnering with the CTO and contributing to roadmap decisions. The role requires strong full-stack experience, distributed systems expertise, and proficiency with TypeScript/Node.js or Python, PostgreSQL, and messaging systems.

Build production AI agent systems for industrial supply chain workflows, including agent harnesses, tool use, sub-agents, retrieval, orchestration, evaluation frameworks, APIs, and integrations with WMS, TMS, and ERP systems. Partner with customers, operators, product, and engineering to understand operational needs, deploy reliable agents, improve model performance, and create repeatable deployment patterns. The role requires strong software engineering, LLM production experience, and comfort working across backend services, data systems, and customer-facing solutions.

Develop, deploy, and analyze AI/ML solutions for wired and wireless signaling data. Build software using neural-network libraries and Python, C, or C++, apply RF and digital signal processing knowledge, benchmark and optimize trained models, and solve complex problems collaboratively. The role may guide junior employees, support Agile modernization and automated testing, requires a STEM bachelor’s degree, eligibility for TS/SCI clearance with polygraph, and travel up to 25%.

Research Engineer improving AI security agents through agent reasoning, evaluation, benchmarking, model selection, security hardening, and reliability engineering. The role develops production-quality systems that detect vulnerabilities, resist prompt injection and data exfiltration, validate findings, and learn from customer engagements. Responsibilities span research and engineering, including experiments, graders, benchmarks, multi-step planning, model tuning, and security research for LLM-based applications.

Conduct advanced AI/ML research and develop novel methods, models, and mission-focused technologies. Build, train, validate, and integrate deep learning systems using modern frameworks and GPU hardware. Research areas include adversarial, trustworthy, safe, agentic, deep, and generative AI. Communicate findings through publications, technical reports, presentations, and study plans while collaborating with interdisciplinary teams and leading technical tasks.