Top Tech Jobs & Startup Jobs

Own end-to-end customer onboarding and implementation for Stream’s AI Moderation product, including policy design, configuration, testing, integrations, and go-live. Continuously tune moderation systems by analyzing false positives and negatives, investigate escalations, train Trust & Safety teams, and create enablement materials. Partner with Sales, Solutions Engineering, Product, and Engineering to support pilots, improve customer outcomes, and inform the roadmap. The role requires technical customer engagement, project management, analytics, communication, and willingness to review harmful content.

Generate qualified pipeline for Stream's AI Moderation product through targeted outbound prospecting (phone, email, LinkedIn), conduct discovery, book meetings with ICP prospects, collaborate with AEs, maintain Salesforce CRM accuracy, hit activity and pipeline goals, and iterate on outbound messaging and experiments.

Own and evolve Stream’s end-to-end revenue operations data platform. Build Python and dltHub ingestion pipelines, SQLMesh transformation models, dimensional data models, quality and observability systems, and GCP infrastructure centered on BigQuery. Integrate GTM systems, enable trusted analytics and reverse ETL, improve reliability and cost efficiency, and establish engineering standards. The role requires hands-on platform ownership in a fast-moving hybrid Boulder startup environment.

Own infrastructure for high-scale real-time systems, including Kubernetes architecture, AWS-to-GCP migration, PostgreSQL scaling, cloud cost optimization, production tooling in Go and Python, capacity planning, reliability improvements, and incident response. Collaborate across engineering teams on system design and independently lead infrastructure initiatives in a small, senior environment.

Own and evolve the company data platform: build ingestion pipelines into BigQuery, create SQLMesh transformations and dimensional models, improve data quality and observability, manage infrastructure with Terraform and GCP, enable BI and reverse ETL, and set technical direction while mentoring engineers and leading incident response.

Owns AI model development end to end, including dataset design, supervised fine-tuning, post-training, evaluation, data pipelines, production deployment, monitoring, and iteration. The role optimizes models for latency, cost, reliability, and scale while collaborating with Go API and infrastructure teams. It also involves setting technical direction in an ambiguous environment, contributing to open source, mentoring engineers, and maintaining high engineering standards.

Design, build, and operate high-scale infrastructure for real-time systems. Lead Kubernetes architecture and migrations, contribute to AWS-to-GCP migration, optimize cloud costs, write production Go and Python tooling, tune capacity, and support reliability through on-call, incident response, and root cause analysis.