Apply formal verification to real customer systems, helping formalize specifications, build and check proofs, and improve tooling for Argo to reason about complex architectures and code at scale.
Compensation
The salary for this position is estimated to be $100,000 to $150,000 per year, prorated for the duration of the internship.
Responsibilities
Support forward deployed engineers in deploying and configuring Argo for customers across cloud, on-prem, or hybrid environments.
Help translate customer system data and architecture artifacts into schemas and models that formal methods tools (SMT and SAT solvers, theorem provers) can reason about.
Prototype and evaluate LLM-powered workflows, including prompt design and tool calling, that connect customer data to automated reasoning.
Build and test front-end features and internal tools that make system models, constraints, and verification results understandable and actionable for customers.
Turn what you learn from customer engagements into documentation, product feedback, and contributions to Atalanta’s forward deployment playbook.
Requirements
Bachelor’s program in computer science, software engineering, mathematics, or a related field
Master’s program in computer science, software engineering, mathematics, or a related field
PhD program in computer science, software engineering, mathematics, or a related field
Work alongside forward deployed engineers and applied mathematicians to bring Argo into customer environments and help users apply formal methods to their own systems. This role combines technical skill, user empathy, and clear communication to support missions that matter.
Compensation
The salary for this position is estimated to be $100,000 to $150,000 per year, prorated for the duration of the internship.
Responsibilities
Support forward deployed engineers in deploying and configuring Argo for customers across cloud, on-prem, or hybrid environments
Help translate customer system data and architecture artifacts into schemas and models that formal methods tools (SMT and SAT solvers, theorem provers) can reason about
Prototype and evaluate LLM-powered workflows, including prompt design and tool calling, that connect customer data to automated reasoning
Build and test front-end features and internal tools that make system models, constraints, and verification results understandable and actionable for customers
Turn what you learn from customer engagements into documentation, product feedback, and contributions to Atalanta’s forward deployment playbook
Requirements
Bachelor’s program in computer science, software engineering, mathematics, or a related field
Master’s program in computer science, software engineering, mathematics, or a related field
PhD program in computer science, software engineering, mathematics, or a related field
Full Stack Engineer 4 (Manager IC) - Operational Risk
McLean, VA: $197,300 - $225,100 for Full-stack Engineer 4
Richmond, VA: $179,400 - $204,700 for Full-stack Engineer 4
Responsibilities
Lead a portfolio of diverse technology projects and a team of developers with deep experience in distributed microservices, and full stack systems to create solutions that help meet regulatory needs for the company
Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities, mentoring other members of the engineering community
Collaborate with digital product managers, and deliver robust cloud-based solutions that drive powerful experiences to help millions of Americans achieve financial empowerment
Provide technical leadership and direction across Agile teams, setting standards for design, development, and operational excellence
Lead a portfolio of diverse technology projects and a team of developers with deep experience in distributed microservices, and full stack systems to create solutions that help meet regulatory needs for the company.
Locations (3)
McLean, Virginia, United States
New York, New York, United States
Richmond, Virginia, United States
Compensation
McLean, VA: $229,900 - $262,400 for Full-stack Engineer 5
New York, NY: $250,800 - $286,200 for Full-stack Engineer 5
Richmond, VA: $209,000 - $238,500 for Full-stack Engineer 5
Responsibilities
Own and evolve the Apollo Enterprise Router, Capital One's federated GraphQL runtime written in Rust, covering query execution, query planning, query controls (depth, height, cost estimation), rate limiting, and Persisted Query enforcement
Drive the supergraph schema lifecycle: automated deployment, composition validation, hot reload, rollback, and subgraph integration for both non-prod and production environments
Director, Data Scientist - Global Payment Network. This role involves partnering with a cross-functional team to deliver products using machine learning and AI agents to drive foundational, scalable intelligence capabilities across the payments ecosystem, supporting fraud/risk, merchant acceptance, and merchant intelligence.
Locations (4)
McLean, Virginia, United States
New York, New York, United States
Riverwoods, Illinois, United States
Chicago, Illinois, United States
Compensation
McLean, VA: $269,100 - $307,200 for Dir, Data Science
New York, NY: $293,600 - $335,100 for Dir, Data Science
Riverwoods, IL: $244,700 - $279,200 for Dir, Data Science
Chicago, IL: $244,700 - $279,200 for Dir, Data Science
Data Engineer 4 role focused on building and pioneering in the technology space, solving complex business problems, and driving a major transformation across Capital One by marrying data with emerging technologies.
Locations (4)
McLean, Virginia, United States
New York, New York, United States
Richmond, Virginia, United States
Wilmington, Delaware, United States
Compensation
McLean, VA: $197,300 - $225,100 for Data Engineer 4
New York, NY: $215,200 - $245,600 for Data Engineer 4
Richmond, VA: $179,400 - $204,700 for Data Engineer 4
Wilmington, DE: $179,400 - $204,700 for Data Engineer 4
Responsibilities
Collaborate with and across Agile teams to design, develop, test, implement, and support technical solutions in full-stack development tools and technologies
The Azure Platform Engineer builds and supports the Azure Kubernetes Service (AKS) environments, CI/CD pipelines, infrastructure as code, and automation that form the internal platform our engineering teams use to deliver software reliably and securely.
Locations (2)
Jacksonville, Florida, United States
Nashville, Tennessee, United States
Responsibilities
Deploy, operate, and troubleshoot workloads on Azure Kubernetes Service (AKS), including cluster configuration, scaling, upgrades, and ingress
Maintain platform tooling and processes across AKS, Azure, Azure DevOps, and Azure Bicep
Develop and maintain infrastructure as code (IaC) using Azure Bicep to provision and manage cloud resources in Azure
Build, maintain, and improve Azure DevOps pipelines for building, testing, and deploying applications
Apply secure DevOps practices, including secrets management, least-privilege access, and vulnerability scanning in pipelines and infrastructure, in support of security and HIPAA compliance requirements
Senior Full Stack Security Engineer (Application & Product)
Join the engineering team that builds the data products, integrations, and AI-enabled tools the rest of Amplitude runs on. This role will specifically support Marketing and Go-to-Market. You’ll partner with Marketing Operations, Revenue Operations, Sales, and Analytics to build reliable data pipelines, trusted business models, and reusable semantic and context layers.
The Staff Data Engineer is a critical member of Hershey's Enterprise Data & Analytics organization, responsible for delivering a seamless, governed, and scalable AI platform experience across the enterprise. This role bridges AI engineering, platform engineering, and product thinking to ensure AI and data tools are easy to discover, adopt, and operationalize.
Responsibilities
Own and evolve the AI platform roadmap across Databricks, Azure ML, and related technologies
Pilot, evaluate, and operationalize new AI platform capabilities
Build and maintain governed, scalable AI infrastructure including compute environments, deployment pipelines, monitoring solutions, feature stores, and AI services
Design and implement "paved road" solutions that provide secure, compliant, and repeatable workflows for AI and machine learning development
Embed AI Trust, Risk, and Security Management (AI TRiSM) practices into platform processes
Partner with architects to define service boundaries, integration patterns, and reusable frameworks
Requirements
Bachelor's Degree or higher in Computer Science or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
Architect, build, and maintain GQL Router plugins using Rust native plugins, Rhai scripting, and external coprocessors to extend router capabilities for Capital One's specific governance and security needs
Lead SRE and operational excellence for the federated platform, including monitoring, alerting, off-hours escalations, vulnerability remediation, and vendor product maintenance for all Enterprise GraphQL-owned components
Collaborate with digital product managers, and deliver robust cloud-based solutions that drive powerful experiences to help millions of Americans achieve financial empowerment
Requirements
Bachelor's Degree or higher in Computer Science or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data
Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
Translate the complexity of work into tangible business goals
Requirements
Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 9 years of experience performing data analytics
Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 7 years of experience performing data analytics
PHD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 4 years of experience performing data analytics
Influence a team of developers, data analysts and data scientists with deep experience in machine learning, distributed microservices, lakehouse architecture, and full-stack systems
Utilize programming languages such as Python and Spark, along with open-source relational and NoSQL databases, and cloud-based data warehousing platforms including Databricks and Snowflake
Design, build and deliver world-class cloud data solutions and applications with little or no support from supervisors or managers
Architect and enforce common data engineering design patterns to ensure code quality, maintainability, and reusability across data platforms and pipelines
Requirements
Bachelor's Degree or higher in Computer Science or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
We offer competitive pay and benefits packages that reflect our commitment to the health and well-being of our Ampliteers.
Responsibilities
Build and operate production data pipelines using Fivetran, dbt, and Snowflake, integrating CRM, marketing automation, advertising, product usage, and other business data.
Create trusted Marketing and GTM data products for campaign performance, funnel conversion, attribution, account engagement, intent scoring, and pipeline analysis.
Build and maintain semantic layers that define shared business entities, relationships, dimensions, and metrics so dashboards, analysts, and AI tools use consistent definitions.
Develop context layers for AI-enabled analytics, capturing business terminology, metric descriptions, synonyms, approved joins, business rules, and verified queries that help tools interpret data accurately.
Partner directly with business stakeholders to understand workflows, clarify ambiguous requirements, validate assumptions, and translate business problems into practical technical solutions.
Requirements
Nice to have: Marketing attribution
Nice to have: Campaign influence
Nice to have: Lifecycle marketing
Nice to have: Account scoring
Nice to have: Warehouse-to-business-system activation
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