LLM Engineer

• lead the product marketing strategy for Cisco’s AI infrastructure portfolio
• connect technical capabilities to the business outcomes customers require for AI workloads and AI-native applications
• define the narrative, positioning, messaging, launch strategy, and sales enablement required to power secure, connected, AI-ready organizations
• partner with Product Management, Engineering, Sales, Corporate Marketing, Analyst Relations, and Communications
• translate sophisticated infrastructure innovation into market value
• drive cross-functional alignment
• equip the business to win with enterprise buyers and executives

Director of Product Marketing Strategy – AI Infrastructure

Machine Learning Engineer

• Collaborate closely with ML Scientists to productize and scale ML models, from experimentation to robust production systems
• Design, build, and own large-scale, distributed machine learning systems for training, deployment, inference, and monitoring
• Lead design discussions and architecture reviews; drive high-impact engineering decisions for ML platforms and applications
• Mentor and coach junior engineers and peers on best practices in ML engineering, system design, and code quality
• Develop and maintain reusable components, libraries, and tools to accelerate ML development lifecycle
• Proactively identify areas for improvement in model performance, pipeline efficiency, data quality, or platform capabilities
• Ensure scalability, observability, and fault-tolerance across all components of the ML stack
• Promote engineering excellence by advocating for best practices in testing, CI/CD, infrastructure-as-code, and monitoring
• Partner with stakeholders across Data Engineering, Product, Marketing, and Platform teams to align solutions with business goals
• Stay up to date on advancements in MLOps, ML frameworks, distributed systems, and apply learnings to improve systems and processes

Senior Machine Learning Engineer

• Design, build, and operate end‑to‑end AI/ML solutions (data pipelines, models, services, and tools) for diagnostics, prognostics, and test analytics.
• Implement production‑grade ML pipelines on platforms such as Azure and Databricks, covering data ingestion, feature engineering, training, evaluation, and inference for batch and streaming workloads.
• Develop and maintain robust, observable ML services and internal tools that make complex vehicle and field data easy to use for engineers and technical stakeholders.
• Apply practical ML and statistical methods (e.g., tree‑based models, time‑series and anomaly detection, deep learning where appropriate) with a focus on reliability, explainability, and impact.
• Own model and data observability in production, including metrics, dashboards, alerts, and remediation workflows for drift, data quality, and performance regressions.
• Partner with data engineering to define and use industrialized and vectorized data products that support search, RAG, and analytics at scale.
• Review designs and code, mentor AI/ML practitioners, and help set high standards for testing, logging, deployment, and documentation.
• Collaborate with diagnostics/prognostics SMEs, validation, safety, and program teams to prioritize work, define success metrics, and embed solutions in day‑to‑day engineering workflows.

Staff Artificial Intelligence Machine Learning Engineer

• Work on the AI Platform team focusing on scalable and efficient pipelines for model training, evaluation, and data processing
• Build and evolve core libraries used by AI scientists to develop, launch, and monitor AI products
• Optimize GPU and CPU efficiency and data throughput of large-scale foundation models
• Ensure Artera’s observability infrastructure provides a clear picture of model performance optimization

Machine Learning Engineer – Platform

AI Research Scientist

• Engage in relevant research and development (R&D) of AI systems, models, and advanced machine learning algorithms that augment physics-driven modeling and simulation systems
• Explore and implement AI-powered simulation tooling in support of AI workflows through reinforcement learning, multi-agent systems, and hybrid modeling approaches
• Collaborate with research, engineering, and product teams to build AI-powered solutions that meet mission-critical modeling and decision-support needs
• Engage in and support the drafting and review of conference and journal articles and presentations, sharing advances with both internal stakeholders and the wider research community.
• Contribute content to technical invention disclosures, including associated narrative, graphics, and engagements in support of patent development
• Perform additional responsibilities (no more than 10% of duties) in support of the company’s technology and product development initiatives.

AI Research Scientist, Applied AI

• Lead the technical strategy for advancing ads ranking and bidding models using large scale ML and agentic development loops.
• Design, build and launch production models and signals that improve ads quality, relevance and long term value.
• Build and refine training, evaluation and feedback pipelines that continuously learn from online behavior, experimentation results and advertiser outcomes.
• Use AI to accelerate analysis and iteration while applying judgment and verification to ensure correctness and quality.
• Own the technical strategy for advancing ads ranking models, with a focus on AI/agentic development loops and automation.
• Build and refine agentic training and evaluation pipelines that continuously learn from feedback signals, offline and production behavior.
• Partner closely with product, applied science, and infra teams to translate business objectives into modeling roadmaps and success metrics.
• Ensure models meet the bar for reliability, safety, fairness, and compliance with privacy and policy constraints.
• Drive technical design reviews, set modeling best practices, and make high-impact architectural decisions across the ads ML stack.
• Mentor and uplevel other MLEs and scientists in advanced modeling techniques, tooling, and agentic workflows.
• Lead frontier work applying AI- and agentic-driven development loops to one of Pinterest’s highest-leverage systems: ads ranking and auctions.
• Shape product outcomes at massive scale, with direct impact on advertiser value, Pinner experience, and company revenue.
• Partner with senior leaders across product, research, and infra, gaining broad visibility and influence on ads strategy.
• Grow as a technical leader by setting modeling standards, mentoring senior talent, and driving multi-quarter roadmaps across teams.
• Access Pinterest’s rich multimodal data and modern ML/infra stack to experiment quickly and ship ambitious ideas.

Senior Staff Machine Learning Engineer, Agentic Ads

AI Engineer

• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products.
• Design, develop, test, deploy, and support AI software components including foundation model training and large language model inference.
• Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, and PyTorch.
• Invent and introduce state-of-the-art LLM optimization techniques to improve the performance of large scale production AI systems.
• Contribute to the technical vision and the long-term roadmap of foundational AI systems at Capital One.

Senior Lead AI Engineer, Gen AI Platform

• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.
• Oversee the design, development, testing, deployment, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.
• Make high judgment build-vs-buy decisions across a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
• Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
• Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.
• Attract and retain top talent in the AI industry and nurture personal and professional development for your team.
• Foster a culture of learning and staying abreast of the state-of-the-art in AI.

Senior Manager, AI Engineering – Gen AI Platform Services

• You will help design and build end-to-end machine learning solutions.
• You will be working in small and cross-functional product teams, with great opportunities for growth and ownership of projects.
• You will be an active member of an internal community, including economists, data scientists, operations research scientists and machine learning engineers, sharing learnings, best practices and research across many domains.
• You will develop high impact solutions to support Instacart's ambitious growth plans.
• You will work closely with engineers, product managers, other teams, and both internal and external stakeholders, owning a large part of the process from problem understanding to recommending a solution and testing it in controlled experiments.
• You will have the freedom to suggest and drive organization-wide initiatives.

Machine Learning Intern, Economics – Fall

• Build and nurture relationships with AI developers, data scientists, and technical communities worldwide
• Present at conferences, meetups, and industry events to showcase Intel's AI technologies and developer tools
• Develop compelling technical content including blog posts, tutorials, videos, and documentation
• Act as the voice of developers within Intel, influencing product roadmaps and developer experience improvements
• Drive adoption of Intel AI platforms by creating resources, samples, and best practices that accelerate developer success
• Engage with the developer community to position Intel as the platform of choice for AI development

AI Developer Evangelist

• Develop innovative machine learning technology and products that serve millions of Snapchatters
• Work on state of the art GenAI pipelines for image, video, and audio generation
• Deliver generative machine learning experiences on device
• Build cutting-edge augmented reality experiences using generative and diffusion models
• Partner with cross-functional Snap teams to explore and prototype new products

Machine Learning Engineer, Generative ML

• Ready to do the most impactful work of your career at Coinbase
• Build the future of finance alongside people who refuse to settle for 'good enough'

• Design AI solutions for customer experience use cases, including self-service, agent assistance, journey management, and back-office automation.
• Support solution designs that combine LLMs, deterministic workflows, tools, orchestration layers, fallback strategies, and human handoff patterns.
• Support retrieval-augmented generation, knowledge, and content strategies that help ground AI responses in trusted enterprise data.
• Assist with approaches for content structure, chunking, retrieval, relevance, and freshness while balancing performance, latency, and governance considerations.
• Help define AI evaluation approaches and quality measurement strategies for customer-facing AI experiences.
• Support the creation of test sets, feedback loops, and success metrics tied to accuracy, containment, customer satisfaction, business impact, and adoption.
• Assist in designing contextual AI experiences that use real-time data, conversation state, customer information, interaction history, and external signals to create more relevant and personalized customer and employee experiences.
• Support solution design decisions related to scalability, latency, reliability, and cost efficiency in enterprise environments.
• Help evaluate trade-offs across model selection, caching, orchestration, architecture patterns, and production-readiness considerations.
• Leverage strong working knowledge of Genesys AI capabilities to articulate and demonstrate product value to customers and prospects.
• Support presales activities including technical discovery, solution design, product demonstrations, sandbox and trial engagements, AI integration guidance, and value assessments.
• Design and deploy AI prototypes in sandbox and/or customer development environments to validate use cases, integrations, latency, success criteria, and differentiated Genesys AI value.
• Partner with account teams and Professional Services to help transition successful prototypes into production pilots, including technical handoff, solution hardening, KPI alignment, and business outcome validation.
• Build and support integrations to third-party systems via RESTful APIs and emerging interoperability patterns, showcasing what is possible with Genesys Cloud AI solutions.
• Develop reusable technical assets and help enable Solution Consultants, partners, and account teams through workshops, coaching, and scalable technical content.
• Provide field feedback and customer insights to Product Management and Engineering related to AI product design, implementation considerations, and customer-driven enhancements.
• Engage with technical and business stakeholders to position Genesys AI as part of a customer’s broader customer experience, IT, and transformation strategy.

AI Architect

• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.
• Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.
• Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
• Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
• Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.

Lead AI Engineer – MLX, Agentic AI, Gen AI platform Services

Lead AI Engineer – Vision Model Customization, VLM

Senior Lead AI Engineer – AI Foundations, LLM Core, Agentic AI

Lead AI Engineer – AI Foundations, LLM Core, Agentic AI

• Spearhead the development, deployment, and optimization of our Machine Learning models and Generative AI workflows
• Act as the bridge between technical execution and executive vision
• Lead the end-to-end lifecycle of ML and Generative AI models, including data structuring and implementation
• Deploy and maintain sophisticated AI applications
• Prepare decks and reports for leadership to translate complex technical milestones into clear business updates
• Maintain and scale a Python-based backend, ensuring seamless integration with our Next.js frontend

AI Engineering Lead

• Define on-vehicle architecture for producing the core tracking results from the Perception stack. (Senior)
• Assist with the implementation of core tracking results from the Perception stack (Junior)
• Work with both the model and data teams to develop a high-performance, efficient model that can run on a vehicle.
• Work with Perception data, both on the input and output of machine learning models

Senior Machine Learning Engineer – Perception Detection and Tracking

• drive the design and development of our core Document Intelligence Platform as a Service
• support the design and implementation of LLM-based technologies for document parsing, entity extraction, and classification tasks
• apply traditional ML and deep learning techniques to enhance the accuracy, efficiency, and scalability of document intelligence models
• build scalable ML pipelines and services for data preprocessing and training
• perform exploratory data analysis on diverse document datasets

See all jobs on JobTailor

Director of Product Marketing Strategy – AI Infrastructure

Senior Machine Learning Engineer

Staff Artificial Intelligence Machine Learning Engineer

Machine Learning Engineer – Platform

AI Research Scientist, Applied AI

Senior Staff Machine Learning Engineer, Agentic Ads

Senior Lead AI Engineer, Gen AI Platform

Senior Manager, AI Engineering – Gen AI Platform Services

Machine Learning Intern, Economics – Fall

AI Developer Evangelist

Machine Learning Engineer, Generative ML

Senior Machine Learning Engineer

AI Architect

Lead AI Engineer – MLX, Agentic AI, Gen AI platform Services

Lead AI Engineer – Vision Model Customization, VLM

Senior Lead AI Engineer – AI Foundations, LLM Core, Agentic AI

Lead AI Engineer – AI Foundations, LLM Core, Agentic AI

AI Engineering Lead

Senior Machine Learning Engineer – Perception Detection and Tracking

Machine Learning Engineer – Document Intelligence