Staff Software Engineer — Search Platform, API & Infrastructure

About the role

The Advanced Content Engineering (ACE) team is seeking a Staff Software Engineer to lead the design and delivery of the search platform's control-plane API and cloud infrastructure. The platform's core promise is self-service: internal client teams must be able to create a search system, configure an ingestion topology, promote a new index to production, and monitor system health — entirely through APIs — without requiring direct involvement from the platform team.

Responsibilities

• Plan, design, develop, and own the platform’s management API — the self-service interface through which client teams create and configure search systems, manage ingestion topologies, register reusable components, promote index versions, and monitor system health.
• Establish API strategy and cross-system integration patterns — designing versioned, backward-compatible interfaces with clear contracts, comprehensive documentation, and developer-experience patterns drawn from best-in-class search platform providers — and set governance standards that the team follows for all future API surface.
• Design and expose the API surface required to support the platform’s evaluation and experimentation workflows — including endpoints that enable the search grading tool to consume experiment run outputs, query/result pairs, and relevance judgments, and that allow client teams to configure and trigger A/B search experiments through self-service interfaces.
• Define, build, test, deploy, scale, and operate what they ship — full-stack ownership is the baseline, not a bonus.
• Own infrastructure cost management: monitor AWS spend across platform components, evaluate architectural trade-offs at the system level, and implement an enterprise performance and optimization framework that keeps the platform’s economics sustainable as it scales — including compute cost governance for inference workloads as custom model serving is introduced.
• Break down complex business requirements into functional and technical requirements with consideration for security, ethical AI implementation, and operational efficiency; contribute to recommendations where technology transformation can spark business growth.
• Drive adoption of AI-assisted development practices across the team’s infrastructure and API work — establishing the tooling, patterns, and norms that enable engineers to leverage AI to move faster while maintaining the quality and reliability bar the platform demands.
• Implement and operate customer-controlled encryption key (CMK) support — applying security strategy, risk assessment frameworks, and security governance to give enterprise clients control over their encryption keys while preserving multi-tenant reliability.
• Define and own platform-level SLOs covering API availability, query latency, ingestion throughput, and end-to-end document freshness — and build the monitoring infrastructure (CloudWatch, distributed tracing, alerting) that makes SLO compliance continuously visible to the team and to client teams.
• Design the observability infrastructure for agentic retrieval paths — where standard request/response logging is insufficient: implement trace-level instrumentation that captures tool invocation sequences, per-hop latency, and retrieval inputs, enabling reliable diagnosis of failures and quality regressions in non-deterministic agent workflows.
• Take full operational responsibility for platform API and infrastructure — you built it, you own it, you run it: triage and resolve incidents, write thorough post-mortems, and drive systematic improvements that prevent recurrence.
• Embed security architecture throughout the platform’s infrastructure: least-privilege IAM, secrets management, encryption at rest and in transit, audit logging, and compliance implementation aligned with TR’s enterprise security requirements.
• Lead significant projects and business initiatives that span multiple engineers and interact with partner teams; determine work priorities and make adjustments to short-term priorities while maintaining strategic focus; provide specialist advice to senior management on complex infrastructure and security issues.
• Mentor and develop Senior and mid-level engineers — providing coaching, technical direction, and educational opportunities in cloud infrastructure, platform API design, reliability engineering, and AI-assisted development practices.
• Engage with client teams as a technical partner — understanding their integration experience and pain points, feeding structured requirements back into the platform API roadmap, and proactively reducing time-to-value for new platform adopters.

Qualifications

You’re an ideal fit if you have:

• A 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 cloud-native platform and infrastructure engineering on AWS: VPC architecture, IAM, ECS, Lambda, DynamoDB, MSK, and related managed services — with hands-on infrastructure-as-code experience (Terraform and/or AWS CDK) and the ability to establish infrastructure governance frameworks
• Production experience with OpenSearch, Vespa, or Elasticsearch at an operational level — cluster sizing, backup and restore, index lifecycle management, and multi-tenant access controls
• Mastery of Python with strategic awareness of language selection and migration; strong software engineering fundamentals including testing architecture, security architecture, and system design
• Experience operating Kafka (MSK) or other distributed messaging infrastructure in production, including partition management, consumer group monitoring, and schema registry governance
• Experience building developer-facing internal platforms where API quality and documentation are treated as first-class product concerns
• Familiarity with distributed tracing infrastructure for non-deterministic or agentic workflows — where trace design must capture tool call sequences and per-hop context, not just request/response pairs
• Familiarity with AI service architecture: evaluating AI vendors, cost-benefit analysis, and integrating AI API services with fallback strategies into production platform infrastructure.