Top Remote AI Engineer Jobs

Own product areas within AI Investing and other financial services, defining user problems, product scope, success metrics, roadmaps, and launch plans. Partner with engineering, design, data, operations, compliance, and business teams to simplify complex financial products, launch features, monitor performance, and iterate using data, feedback, research, and production insights. The role requires strong product judgment, execution, prioritization, communication, and ownership in ambiguous environments.

Own AI Finance product areas across insurance, payments, savings, investing, travel, and related financial services. Define user problems, product scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams. Simplify complex financial products, use research and performance data to guide decisions, launch features, monitor results, and drive continuous iteration while balancing speed, customer experience, business impact, compliance, and operational complexity.

Own reliability, monitoring, and incident response for AI infrastructure; build automation and CI/CD tooling; manage Kubernetes/Docker production workloads; partner with infrastructure, security, and compliance; improve observability and documentation; develop internal full‑stack tooling in Go or Python.

Senior SRE on the IT Operations team owning reliability, monitoring, and incident response for AI infrastructure. Build automation, CI/CD and Kubernetes tooling, improve observability and documentation, and develop internal full-stack tools using Go or Python. Partner with Infrastructure, Security, and Compliance to scale secure, resilient AI deployment pipelines.

Own the design and implementation of WISEcode’s automated quality engineering ecosystem. Build AI-augmented UI, API, integration, regression, database, performance, and reliability testing frameworks for ASP.NET and Blazor applications. Integrate testing into CI/CD, improve observability and release confidence, investigate failures and race conditions, validate AI/ML outputs, and establish scalable quality standards and metrics across engineering teams.

Build and prototype state-of-the-art large language models and AI assistants, take services from ideation to production, fine-tune open-source models, use LLM platforms, analyze model statistics and projections, and collaborate cross-functionally in a fast-paced hybrid Palo Alto environment.

Own the end-to-end product direction and quality of an AI email application. Define AI and system requirements, translate model capabilities into product decisions, balance quality, cost, latency, reliability, safety, and user experience, and partner with ML, backend, and client engineers. Establish evaluation frameworks, quality standards, feedback loops, and failure recovery strategies while driving prioritization, execution, experimentation, and user trust in a zero-to-one environment.

Build production-grade infrastructure for GTM AI agents: harden prototypes, write evals and prompts, implement observability, own deployment pipelines on GCP, manage safe database access, monitor and improve agent fleet, and partner with GTM builders to scale reliability.

Lead application engineering for an AI-powered email product across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build APIs connecting AI capabilities to product experiences, make architectural trade-offs, establish observability and operational excellence, and lead a small team of senior engineers through hands-on technical mentorship and code development.

Leads application engineering for an AI-powered email product, owning backend, mobile, and desktop architecture. Designs reliable AI workflows with orchestration, state management, retries, fallbacks, and failure handling; builds APIs connecting AI capabilities to product experiences; and leads a senior engineering team through architectural decisions, reviews, mentorship, and production excellence. Partners with ML leadership to ship scalable, observable, secure, and reliable systems while balancing latency, cost, user experience, and maintainability.

Lead the architecture and development of reliable AI-powered email applications across backend, mobile, and desktop platforms. Design APIs and workflow systems supporting orchestration, state, tool use, retries, fallbacks, and failure handling. Lead a small senior engineering team, make architectural trade-offs, mentor engineers, and establish strong production practices including observability, monitoring, testing, scalability, security, and operational excellence. Partner with ML leadership to deliver robust, high-quality product experiences.

Own product areas for an AI-focused neobank across financial services such as insurance, payments, savings, investing, and travel. Define user problems, product direction, roadmaps, metrics, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify financial workflows; launch features; monitor performance; and iterate using data, feedback, research, and production insights.

Own product areas for AI stockbroking and broader financial services, defining user problems, product direction, roadmaps, success metrics, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify financial products, ship features quickly, monitor performance, and iterate using data, research, feedback, and production issues.

Own product areas across AI investing and financial services, defining user problems, product direction, scope, metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to ship customer-focused features. Use research, feedback, metrics, experiments, and production insights to improve products, balancing speed, usability, business impact, compliance, and operational complexity.

Own AI Finance product areas across insurance, payments, savings, investing, travel, and other financial services. Define user problems, roadmaps, metrics, scopes, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial journeys; launch features; monitor performance; and drive iterative improvements using data, research, feedback, and production insights.

Own Aleph’s shared AI foundation, including model selection and routing, proxy infrastructure, context management, tool design, and evaluation systems. Ship production agentic features using prompt engineering, caching, parallel tool calls, and subagents. Build observability for agent performance, collaborate with customers and finance experts to define quality standards, and drive adoption of reliable AI systems across product teams.

Designs and operates production-grade AI agent infrastructure across Azure, Oracle Cloud, GCP, and AWS. Owns Kubernetes, Terraform, CI/CD, GitOps deployments, observability, security, reliability, networking, and reusable automation frameworks. Automates provisioning, secret rotation, compliance checks, releases, and operational runbooks while partnering with product and platform engineers to support secure, portable enterprise environments.

Build and maintain AWS-based AI infrastructure and services for ML workflows and real-time inference. Implement CI/CD pipelines, integrate LLM/GenAI components, ensure observability, write high-quality code, collaborate cross-functionally, and prototype ML algorithms to turn forensic data into investigation leads.

Build and own customer-facing search features across Vue.js frontends and Python/Django backends. Develop AI-powered experiences, search and retrieval systems, and data workflows using Elasticsearch, Kafka, Celery, and relational databases. Collaborate with Product and Design, troubleshoot production issues, improve reliability and performance, participate in on-call rotations, and make pragmatic technical trade-offs. The role is fully remote with up to 10% travel and requires overlap with U.S. time zones.

Research and optimize AI and machine-learning systems across desktop and mobile platforms. Evaluate model performance, develop and deploy on-device applications and agents, and improve browser ML products. Apply systems and network expertise, collaborate with privacy and security researchers, and investigate AI risks. The role involves open-source development, performance benchmarking, and use of modern ML frameworks to deliver privacy-focused solutions.

Deploys and validates complete AI infrastructure software stacks on multi-node GPU systems, then converts implementations into technical documentation, automation, demos, training, and reference architectures. Tests prerelease software, evaluates interoperability and operational resilience, supports partners and field teams, collaborates with engineering and open-source communities, and recommends product improvements based on customer feedback. The role presents solutions through briefings, workshops, webinars, events, and internal training, with some travel required.

Lead the data strategy and own end-to-end data pipelines for AI training, simulation, and deployment in safety-critical autonomous systems. Build data flywheels, integrate with large-scale GPU/TPU training infrastructure, ensure simulator fidelity, partner with safety and certification teams, mentor engineers, and track KPIs for pipeline health and model readiness.

Deploys, integrates, and scales enterprise generative AI solutions within customer engineering teams. Builds intelligent agents and multi-agent systems using Vertex AI, ADK, and MCP; connects AI systems to enterprise databases; develops real-time data pipelines; and operationalizes LLMs across cloud environments. The role also includes model training, tuning, evaluation, deployment, vector-based grounding, cloud infrastructure integration, stakeholder communication, and translating business goals into technical architectures.

Deploys, integrates, and scales enterprise generative AI solutions in customer cloud environments. Responsibilities include architecting GCP and Vertex AI infrastructure, deploying and optimizing LLMs, building RAG data and vector search pipelines, managing GPU/TPU workloads on GKE, automating infrastructure with Terraform, and advising clients on AI safety, prompt engineering, hallucinations, and inference costs. The role also collaborates with research and platform teams and requires travel to client sites.

Develop HPC and AI software workflows for experimental data processing, imaging, plasma, high-energy physics, and materials simulations. Profile applications, benchmark performance, identify bottlenecks, and advise collaborators and product teams. Build tutorial examples, translate research into software tools, and represent NVIDIA at scientific conferences and industry events.