Staff Software Engineer Search Platform, Ingestion & Indexing

Staff Software Engineer

Advanced Content Engineering (ACE) is seeking a Staff Software Engineer to serve as the technical anchor for the search platform's ingestion and indexing systems. The platform processes millions of documents across TR's legal, tax, and professional content corpora — parsing, chunking, enriching, embedding, and indexing them into a hybrid search engine that powers both human-facing search interfaces and autonomous AI agents. Getting this pipeline right, at scale, with zero-downtime operations and increasingly agentic retrieval patterns, is one of the platform's most consequential engineering challenges.

This role owns the design, implementation, and operational health of the document ingestion pipeline and search index management systems — from the Kafka-based streaming infrastructure that moves documents through processing stages, to the Vespa application architecture that stores and serves them. Staff Engineers on this team define, build, test, deploy, scale, and operate what they ship — full-stack ownership is not a principle we aspire to, it is the daily reality. AI-assisted development is the team norm, not the exception, and constant delivery to production is the expectation. This is a role for someone who sets architectural boundaries, not just executes within them

In this position, you will focus on:

• Ingestion Pipeline Architecture & Engineering
• Custom Model Operationalization
• Search Engine & Index Management
• Agentic Search Infrastructure
• Evaluation & Search Quality
• Reliability & Operational Ownership
• Technical Leadership

About You

You're an ideal fit if you have:

Required Experience —

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field

• 8+ years of software engineering experience, with demonstrated progression to staff-level or equivalent technical leadership — including ownership of a functional area and leadership of significant cross-functional projects

• Deep expertise in distributed stream processing: designing, building, and operating high-throughput, fault-tolerant event-driven pipelines using Kafka or equivalent technologies at production scale

• Production experience with Vespa, OpenSearch, or Elasticsearch — including schema design, ranking profile configuration, and end-to-end application lifecycle management

• Mastery of Python with strategic awareness of language and framework selection; strong software engineering fundamentals including test strategy, performance architecture, and system design

• Proficiency with AWS cloud services used in data pipeline and search infrastructure (MSK, ECS, Lambda, DynamoDB, Step Functions, CloudWatch), with infrastructure-as-code experience (Terraform or AWS CDK)

• Demonstrated ability to take full operational responsibility end-to-end — defining SLOs, building observability, running on-call, and driving systematic improvements from incident retrospectives — with a track record of shipping to production frequently and removing delivery friction proactively

• Comfort and fluency with AI-assisted development tools; you use them to move faster and produce higher-quality work, not as a novelty

• Track record of establishing architectural principles, cross-system design patterns, and documentation standards that improve the broader team's engineering quality

Preferred Experience —

• Experience operationalizing ML models in production: inference serving, model promotion pipelines, canary rollouts, and production observability for model quality signals

• Familiarity with agentic retrieval patterns — multi-hop retrieval, latency budget management across retrieval hops, context window optimization, and stateful session design

• Experience with online search analytics: instrumenting systems for query performance monitoring, A/B or interleaved ranking experiments, and query log analysis to surface relevance gaps

• Experience with embedding pipelines, vector indexing, and hybrid (dense + sparse) retrieval architectures in a production context

• Familiarity with Protobuf schema design and schema registry governance patterns (Confluent Schema Registry or equivalent)

• Experience building self-service or multi-tenant platform infrastructure where reliability and correctness directly affect multiple downstream teams

• Background in AI ethics frameworks and responsible deployment of machine learning components in production pipelines

What Success Looks Like

In the first 90 days:

• Develop a thorough understanding of the platform's current ingestion and indexing architecture, active technical debt, known reliability gaps, and the roadmap for Vespa adoption

• Establish strong working relationships with the search platform team, TR Labs, and key client teams consuming the ingestion pipeline

• Take on-call ownership for your functional area and deliver at least one meaningful improvement to pipeline reliability, observability, or delivery automation

In the first year:

• Lead the architectural design and delivery of a major phase of the Vespa migration — including ingestion pipeline changes, schema migration, and zero-downtime index promotion — resolving novel technical challenges with minimal precedent

• Establish robust SLO coverage and observability across ingestion components, with on-call playbooks, documented architectural decision records, and demonstrated improvement in incident response quality

• Deliver a production-ready custom model operationalization framework: inference serving, promotion pipeline, and observability for at least one custom model integrated into the ingestion or query stack

• Become the recognized technical authority for ingestion and indexing — the person the team and partner organizations turn to for architectural direction in this domain — with demonstrated influence on platform strategy.