Staff Software Engineer - AI

About Outreach Outreach, founded in 2014, is the only complete agentic AI platform for revenue teams. Outreach infuses agentic AI, conversation intelligence, and assistive AI to power hundreds of use cases across revenue motions. From new logo prospecting to expansions, deal acceleration, driving retention, and forecasting, Outreach AI automates workflows and frees sellers to focus on more strategic conversations and actions. Revenue leaders benefit from connected account visibility, performance insights, and higher forecasting accuracy across every GTM team. World leading enterprise organizations use Outreach to power their revenue teams, including Databricks, SAP, Siemens, and Verizon to name a few.

Opportunity Management (OM) Team About The Team

• Our mission is to deliver a resilient, internet-scale microservice platform supporting high-velocity development, real-time analytics, and intelligent automation. The team architects and powers the critical backend services at the core of our opportunity management and forecasting ecosystem. We collaborate closely with cross-functional leaders to drive innovation, enable data-driven decisions, and leverage cutting-edge AI/ML to ensure global customer impact.
• As Outreach invests in AI-native product experiences, this team owns both the backend platform and the clean, well-modeled, high-quality data foundations those experiences depend on. This role is central to that shift.

The Role

• We are seeking a Staff Software Engineer who thrives on tackling complex technical and architectural challenges at scale. You will bring deep expertise in scalable, distributed systems design, lead high-performing teams through ambiguity, and accelerate product innovation in data-driven and AI-rich environments. You will steer system evolution, guarantee performance under high load, and mentor peers while shaping delivery and technical direction.
• Equally important, you will raise the team’s data- and AI-engineering maturity — designing the pipelines, data models, and ML/GenAI integrations that power our next generation of intelligent features.

Your Daily Adventures Will Include

• Leading the architecture, design, and delivery of distributed cloud-native applications capable of high concurrency and demanding real-time data needs.
• Designing and building production-grade data pipelines and ETL/ELT workflows — modeling data for both transactional (OLTP) and analytical (OLAP) use, and orchestrating them with modern workflow tooling.
• Integrating ML and GenAI capabilities into product features — from model serving and evaluation to LLM-powered enrichment, retrieval (RAG), and intelligent automation within our services.
• Championing data quality and correctness — building validation, observability, and testing into every step of the pipeline so downstream analytics and AI features can be trusted.
• Collaborating with data science, product, and engineering partners to ship intelligent, complex product features.
• Setting and promoting engineering standards for code quality, security, and operational excellence; nurturing automation and continuous improvement.
• Diagnosing and eliminating performance bottlenecks and proactively addressing reliability risks.
• Mentoring, reviewing code/architectures, and fostering a culture of rapid learning.
• Decomposing legacy systems into SOA/microservices, resolving tech debt, and evolving the architecture for scale.
• Taking end-to-end ownership of major initiatives from planning through impact.

What Sets This Role Apart: Data & AI Engineering

Role This role blends strong backend engineering with hands-on data and AI capability. You will be expected to grow the team’s fluency in these areas, so we look for depth (or a clear trajectory) across:

• Data modeling & storage: schema design, query optimization, and knowing when to reach for RDBMS, NoSQL, OLAP, or OLTP stores.
• Modern data stack: Spark / Delta Lake, Databricks or Snowflake, dbt for transformation, and Airflow (or similar) for orchestration.
• AI/ML in production: deploying and monitoring models, feature pipelines, and — increasingly — GenAI/LLM application patterns (embeddings, vector search, RAG, prompt/context engineering, evaluation).
• Analytics enablement: building the data models, frameworks, and artifacts that make trustworthy analytics and dashboards easy for others to build on.

Required Qualifications Our Vision of You:…