Overview
The R&D Engineer is a researcher-practitioner - someone equally comfortable reading papers and shipping prototypes. They are responsible for staying close to the frontier of LLM and AI systems research, identifying ideas with practical value, and building POCs that demonstrate feasibility. These POCs are demoed to the team and, where validated, handed off to the Platform and AI Engineers to productionize. This person does not work in isolation — they collaborate closely with the rest of the team throughout the research-to-product pipeline.

What You'll Do:

• Monitor and synthesize developments in LLM systems, AI infrastructure, and adjacent research areas
• Prototype and evaluate novel ideas with a fast feedback loop — build, demo, iterate
• Extend and improve the existing agent and RAG SDKs with new retrieval strategies, agent patterns, and provider integrations
• Evaluate new embedding models, reranking approaches, and retrieval architectures against the existing pipeline
• Collaborate with the Platform and AI Engineers to hand off validated POCs
• Produce clear documentation and demos that make research accessible to non-researchers
• Contribute to discussions with the tech lead on long-term research direction and where to place technical bets
• Engage with the broader research community — conferences, papers, open-source projects
• Identify opportunities where emerging techniques can address real problems surfaced by the AI Engineer

What You Bring:

• Hands-on experience building or evaluating LLM systems — fine-tuning, inference, retrieval, or agents
• Familiarity with RAG architectures beyond naive vector search — HyDE, MMR, graph-based retrieval, reranking, or hybrid search
• Exposure to agent frameworks and multi-agent orchestration patterns (PydanticAI, LangGraph, AutoGen, CrewAI, or similar)
• Demonstrated ability to move quickly from idea to working prototype
• A portfolio of explored ideas — published work, technical blog posts, side projects, or OSS contributions
• Strong communication skills — can explain complex research to non-researchers clearly
• Collaborative by default; understands that research value is realized when ideas ship
  • Core: Python, agent frameworks (PydanticAI or equivalent), RAG pipeline design (embedding, retrieval, reranking), managed ML platforms (Vertex AI or equivalent)
  • Nice to have: Systems-level programming experience, familiarity with inference infrastructure (vLLM, TensorRT), experience with RLHF or fine-tuning pipelines, MCP protocol, graph databases (Neo4j)

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ZoomInfo is seeking a Senior DevOps Engineer with a software-first mindset to join our Platform Engineering team. We aren't just looking for someone to manage tools; we want an engineer who treats infrastructure as a software product.

This role is for a proactive owner with a big-picture mindset—someone who takes initiative, looks beyond their immediate tasks, and drives complex projects from initial design all the way to production. You will join a highly collaborative, global team where communication is key to building the "paved road" for our developers across multiple cloud providers.

What You'll do:

• Platform Development: Engineer robust, scalable CI/CD platforms using GitHub Actions, building custom actions and reusable workflows that function as a seamless internal product.
• Software Automation: Develop internal tools and CLI utilities in Go or JavaScript to automate complex SDLC tasks and eliminate manual operations.
• Kubernetes Maintenance: Act as a maintainer for our Kubernetes ecosystem, ensuring the platform is optimized for developer workloads, scalability, and high availability.
• End-to-End Ownership: Lead projects from architectural design to implementation and long-term maintenance, identifying system-wide bottlenecks before they become issues.
• Declarative Infrastructure: Lead the implementation of GitOps workflows using ArgoCD, treating application state and infrastructure as version-controlled code.
• Cloud Orchestration: Provision and manage sophisticated GCP and AWS environments using software-defined infrastructure principles.
• Global Collaboration: Work closely with distributed teams of Architects and Engineers, ensuring clear communication and alignment across different time zones.

What you bring:

• Professional Experience: 5+ years of experience in a DevOps/SRE role OR a total of 6+ years of experience consisting of at least 3 years in Software Development and 3 years in DevOps.
• High Degree of Ownership: A proven track record of taking full responsibility for projects, solving ambiguous problems, and driving initiatives end-to-end.
• Software Proficiency: Strong programming skills in Go, JavaScript, or Python, with the ability to write clean, testable code for automation and tooling.
• Kubernetes Expertise: Hands-on experience as a Kubernetes maintainer, including cluster management, resource optimization, and a deep understanding of Kubernetes internals.
• CI/CD Architecture: Proven expertise in designing complex, logic-heavy pipelines (GitHub Actions preferred).

Additional skills: 

• AI Development: Experience or knowledge in AI/ML development, including building or deploying LLM-based applications or managing AI infrastructure.
• Infrastructure as Code: Advanced experience with Terraform or Pulumi.
• Control Planes: Experience with Crossplane for managing cloud infrastructure via Kubernetes.
• Observability: Familiarity with Datadog or OpenTelemetry to monitor platform performance.
• Architecture: Experience with service mesh (Istio/Linkerd) or microservices networking.

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Job Summary: 

We are seeking a highly skilled and self-motivated Senior Embedded DevOps Engineer to support our engineering  teams. This role will focus on driving changes and ensuring adherence to company-established standards for data infrastructure and CI/CD pipelines. The ideal candidate will have strong experience working with AWS and/or GCP,  cloud-based data streaming and processing services, containerized application deployments, infrastructure automation, and Site Reliability Engineering (SRE) best practices for performance and cost optimization.

What You'll do:

• Drive initiatives to implement and enforce best practices for data streaming, processing, analytics and monitoring infrastructure.
• Deploy and manage services on Kubernetes-based platforms such as Amazon EKS and Google Kubernetes Engine (GKE).
• Provision and manage cloud infrastructure using Terraform, ensuring best practices in security, scalability, and cost-efficiency.
• Maintain and optimize CI/CD pipelines using Jenkins, ArgoCD, and GitHub Enterprise Actions to support automated deployments and testing.
• Work with cloud-native data services such as AWS Kinesis, AWS Glue, Google Dataflow, and Google Pub/Sub, BigQuery, BigTable
• Familiarity with  workflow orchestration services such as Apache Airflow and Google Cloud Composer.
• Develop and maintain automation scripts and tooling using Python to support DevOps processes.
• Monitor system performance, troubleshoot issues, and implement proactive solutions to enhance reliability and efficiency.
• Implement SRE practices to improve service reliability, scalability, and cost-effectiveness.
• Analyze and optimize cloud costs, identifying areas for improvement and implementing cost-saving strategies.
• Ensure compliance with security policies and best practices in cloud environments.
• Drive adoption of company standards and influence data teams to align with best DevOps and SRE practices.
• Collaborate with cross-functional teams to improve development workflows and infrastructure.

What you bring:

• 7+ years of experience in a DevOps, Site Reliability Engineering, or Cloud Infrastructure role.
• Strong experience with AWS and GCP data services, including Kinesis, Glue, Pub/Sub, and Dataflow.
• Proficiency in deploying and managing workloads on Kubernetes (EKS/GKE) in production environments.
• Hands-on experience with Infrastructure-as-Code (IaC) using Terraform.
• Expertise in CI/CD pipeline management using Jenkins, ArgoCD, and GitHub Enterprise Actions.
• Programming skills in Python for automation and scripting.
• Experience with observability and monitoring tools (e.g., Prometheus, Grafana, Datadog, or CloudWatch).
• Strong understanding of SRE principles, including performance monitoring, incident response, and reliability engineering.
• Experience with cost optimization strategies for cloud infrastructure.
• Self-motivated and driven, with a strong ability to influence and drive changes across multiple teams.
• Ability to work collaboratively in an agile environment and support multiple teams. 

Preffered Qualifications:

• Experience with data lake architectures and big data processing frameworks (e.g., Apache Spark, Flink, Snowflake, BigQuery).
• Familiarity with event-driven architectures and message queues (e.g., Kafka, RabbitMQ).
• Experience with workflow orchestration tools such as Apache Airflow and Google Cloud Composer.
• Knowledge of service mesh technologies like Istio.
• Experience with GitOps workflows and Kubernetes-native tooling.

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About the Role

We are looking for a highly skilled Senior Data Engineer to become part of our core Data & AI Engineering team. In this pivotal role, you will be responsible for designing and expanding enterprise-level data infrastructure that enables ZoomInfo's internal teams to interact with data comprehensively—extracting, exploring, analyzing, and generating insights—through various platforms using ZI's internal chat agent

The ideal candidate has a strong background in big data processing, pipeline orchestration, and data modeling, with a proven track record of delivering scalable and high-quality data solutions in fast-paced, data-centric product environments. Given the dynamic nature of emerging technologies, this role requires an individual who excels at exploration and embraces continuous learning as core responsibilities. You'll constantly research and implement innovative solutions while integrating vast, diverse data sources into our AI applications, including our industry-leading LLM-powered systems

What you’ll do:

• Design, develop, and maintain high-performance, product-centric data pipelines using Airflow, DBT, and Python.
• Architect and optimize the massive-scale data warehouse and lakehouse that serves as our single source of truth for all customer data, primarily using Snowflake.
• Lead the integration of diverse structured and unstructured data sources (e.g., web data, third-party APIs) into our data ecosystem, ensuring high-quality and reliable ingestion.
• Implement and enforce Model Context Protocol (MCP) or similar architectures to feed accurate and contextual data into our LLM-powered products for applications like Retrieval Augmented Generation (RAG) and advanced search.
• Collaborate with ML engineers, data scientists, and product managers to translate business needs into scalable data solutions that directly enhance customer value.
• Define, monitor, and enforce data quality SLAs across all pipelines and products, ensuring data accuracy and lineage are a top priority.
• Mentor and coach junior engineers, promoting best practices in code quality, data architecture, and operational excellence.
• Participate in architectural decisions and long-term strategy planning for our enterprise-wide data infrastructure, with a focus on cost, performance, and reliability.

What you bring:

• Expert-level SQL for building performant, scalable queries and transformations on massive datasets.
• Strong Python programming skills with a focus on distributed computing, data manipulation, and building robust APIs.
• Production-level experience for large-scale batch and streaming data processing.
• Hands-on experience with DBT (Data Build Tool) for advanced data modeling and transformations in a modern data stack.
• Deep knowledge of Snowflake data warehouse design, optimization, and cost modeling.
• Experience implementing Model Context Protocol (MCP) or similar architectures to feed structured and unstructured data into LLM-powered systems.
• Strong understanding of data architecture concepts, including data lakes, event-driven architectures (e.g., Kafka), ETL/ELT, and data mesh.
• Proficiency with cloud platforms (GCP and/or AWS) and infrastructure as code (e.g., Terraform).

Nice to Have

• Familiarity with LLMOps, LangChain, or RAG (Retrieval Augmented Generation) pipelines.
• Experience with building embedding models or pipelines for Named Entity Recognition (NER).
• Knowledge of data cataloging tools (e.g., OpenLIneage, etc.) and lineage tracking.
• Familiarity with other distributed systems and databases (e.g., DynamoDB, Flink).

Required Non-Technical Skills

• Excellent communication skills – ability to explain complex technical concepts to both engineering teams and non-technical stakeholders.
• Strategic & Product-Oriented Thinking – can translate business objectives and customer needs into scalable, high-impact data solutions.
• Leadership & Mentorship – experience guiding and uplifting engineering teams to achieve their full potential.
• Stakeholder Management – able to collaborate effectively across departments (Product, Engineering, Sales, Compliance).
• Agility & Adaptability – thrives in ambiguous, evolving environments and can rapidly prototype and iterate on solutions.
• Strong documentation habits and ability to evangelize best practices across the organization.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 8+ years of progressive experience in data engineering, with a track record of leadership and impact.

Demonstrated experience in implementing or scaling data infrastructure for a data-centric product company.

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At ZoomInfo, we encourage creativity, value innovation, demand teamwork, expect accountability, and cherish results. If you are in charge, take initiative, and get stuff done individually, we want to talk to you! We have high aspirations for the company and are looking for the right people to help fulfill the dream. We strive to continually improve every aspect of the company and use cutting-edge technologies and processes to delight our customers and rapidly increase revenues.

We’re looking for a Software Engineer with an emphasis on backend to join the Chorus Platform team. Chorus.ai is a Conversation Intelligence platform for Sales Teams that processes and analyzes a tremendously large set of sales calls to provide insights into how any salespeople from any organization can perform at the highest level and match the best sales performers in the industry. We generate insights and notifications to sales managers on how deals are going, risks, and the next steps necessary to close the deal. Chorus is an indispensable platform in a modern and data-driven sales team.  

As part of the Platform team, you will constantly be working on challenging and innovative projects aimed at empowering different user personas with the capabilities of our core technology. You will own aspects of the customer-facing product, you will be working with different technologies, and finding technical solutions that are capable of handling millions of customer requests per day.

You’ll see that collaboration is second nature, you’ll be greeted by a team of incredibly smart, talented, and motivated individuals who will help you define your new best, and most of all, you are hungry to be part of an ambitious team with colossal goals.

What You’ll Do:

• Design, build, and deploy complex distributed systems
• Build 3rd party integrations with major conference providers using SDKs

Qualifications:

• 4+ years of software development experience.
• Experience with cloud-based infrastructure.( AWS/GCP)
• Experience with SQL and NoSQL Databases/ Datastores
• Good knowledge of RESTful API Standards
• Knowledge of industry best practices for package management, and their release cycles ( CI/CD)
• Proven experience in distributed microservices development.
• Proven experience with distributed systems software design.
• High personal and leadership skills.
• Strong communication skills

Education:

• BSc in Computer Science, Mathematics, Software Engineering, or equivalent experience

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