Top AI Engineer Jobs

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 quota-carrying technical sales for Ramp’s AI applications, agents, and infrastructure. Lead discovery, demonstrations, workshops, proofs of concept, ROI and total cost of ownership analyses, technical validation, objection handling, and deal strategy through close. Partner with account executives and internal teams while engaging engineering, IT, security, finance, and executive stakeholders. Help establish repeatable messaging, sales plays, qualification criteria, and go-to-market processes for Ramp’s emerging AI business.

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.

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.

Develop data-driven solutions using Python and R; perform statistical analysis, data wrangling, and machine learning model development with TensorFlow and Scikit-Learn. Collaborate with cross-functional teams to deliver actionable insights, predictive analytics, and automation. The role also requires software engineering experience, familiarity with agentic AI frameworks or coding agents, and an active TS/SCI clearance with polygraph.

Develop data-driven solutions using Python and R; conduct statistical analyses; design and train machine learning models with TensorFlow and Scikit-Learn; wrangle and prepare raw data; and collaborate with cross-functional teams to deliver actionable insights. The role requires extensive software engineering experience, agentic AI or AI coding-agent expertise, active TS/SCI clearance with polygraph, and a relevant bachelor's degree or equivalent experience.

Design and own end-to-end computer vision and machine learning pipelines for deployed robotic fleets, covering data collection, training, sensor fusion, evaluation, edge deployment, and telemetry. Develop data engines with auto-labeling, synthetic data, and observability loops; integrate perception with navigation and planning through ROS 2. Evaluate emerging AI methods against latency, cost, determinism, and safety constraints. Mentor engineers, collaborate across hardware and software teams, and occasionally advise clients.

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.

Lead frontend architecture and technical direction for Observe by Snowflake’s AI Products team. Build interactive interfaces for streaming, multi-step AI agents; develop Slack integrations, CLI tooling, evaluation pipelines, MCP integrations, and extensible platform capabilities. Partner with product and design on roadmap decisions, mentor engineers, author architecture guidance, and deliver scalable customer-facing web applications for AI-powered observability.

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.

Own end-to-end design, implementation, testing, deployment, and production support of AI-friendly, scalable full-stack retail commerce software. Define contract-first APIs, leverage AI-assisted development tools, ensure testability, observability, security, and build reusable engineering assets and developer tooling to accelerate delivery without increasing technical debt.

Two-year international rotational program for early-career engineers working across R&D teams on software and AI solutions. Responsibilities include developing innovative network and communications products, collaborating with Agile teams, communicating design and project progress, resolving issues, learning new technologies, and adapting to changing priorities. Participants may complete temporary assignments in other countries or locations before continuing their careers with VIAVI.

A two-year international rotational program for early-career engineers working across R&D teams. Participants develop software and hardware solutions for communication networks, collaborate with Agile teams, solve technical problems, and learn emerging technologies. The role includes software engineering and AI-focused work involving programming, Linux, networking protocols, RESTful APIs, large language models, AI-assisted coding, and agentic workflows. Graduates continue their careers with a designated VIAVI team.

Designs, develops, deploys, and maintains AI-enabled automation for Finance and FP&A workflows. Responsibilities include coding reporting and forecasting automation, building finance data pipelines and API integrations, embedding AI for variance explanations and anomaly detection, validating data quality, debugging production solutions, and managing the full solution lifecycle from concept through ongoing maintenance.

Assist the AI engineering team with NLP research, language model development, data annotation, experiments, and model evaluation. Support multilingual translation, content moderation, analytics, and other language-related features for a football-focused social and messaging platform.

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.

Lead the technical direction, architecture, and delivery of production agentic AI systems. Build AI platform abstractions, agent workflows, evaluation and guardrail infrastructure, cloud-native services, and operational tooling using TypeScript/Node.js and Python. Own deployment, monitoring, troubleshooting, and measurable outcomes while partnering with product, clinical operations, and business leaders. Mentor engineers, establish reusable patterns, communicate architectural trade-offs, and ensure privacy, security, compliance, reliability, safety, and cost controls for sensitive healthcare data.

Join the Fury Team as an AI Engineer to develop AI models for autonomous systems, designing architectures and integrating ML for real-time applications.

The Applied AI Engineer will develop AI systems to enhance cyber capabilities, manage datasets, fine-tune models, and integrate AI into applications, focusing on national security.