Lead Software Engineer

About Troveo

Troveo is building the next-generation data platform to train AI video models. We offer the world’s largest library of AI video training data—featuring millions of hours of licensed video content. Our end-to-end data pipeline connects creators, rights holders, and AI research labs, enabling scalable, compliant, and innovative uses of video for AI applications and model development.

We are an early-stage, high-growth venture backed by forward-thinking investors, and we’re seeking a Lead Software Engineer to help build the distributed systems powering Troveo’s data and AI infrastructure.

The Lead Software Engineer will design and scale the foundational backend systems that enable Troveo’s massive data delivery, compute, and model-training operations. You’ll collaborate closely with product, DevOps, and frontend teams to architect resilient, performant microservices that support large-scale AI video data processing.

This is a hands-on, high-impact role for an engineer who thrives on technical depth - balancing reliability, scalability, and efficiency in distributed environments. You’ll shape how data moves, transforms, and powers Troveo’s AI ecosystem.

You will lead the architecture of Troveo’s data pipelines, systems, and applications, working at the intersection of large-scale data, cloud infrastructure, and applied AI. The ideal candidate combines deep systems experience with strong communication, precision, and a startup mindset.

Architecture & Systems Design

• Lead the architecting of Troveo’s data pipelines, systems, and applications for scalability and reliability.
  

• Partner with product, frontend, and DevOps teams to co-design scalable backend architectures.
  

• Architect and deploy microservices in production environments, ensuring orchestration, auto-scaling, and fault tolerance across hybrid or multi-cloud setups.
  

• Build resilient distributed systems addressing challenges like eventual consistency, service mesh (Istio), and event-driven architectures with Kafka or NATS.
  

• Collaborate across teams as a player-coach, mentoring other engineers while delivering hands-on code and system design.
  

Data Infrastructure & Optimization

• Design and optimize data pipelines that process massive video datasets for AI workloads.
  

• Dive deep into database internals—execution and storage engines, sharding, replication, and vector search techniques—to ensure efficiency at scale.
  

• Extensive experience with AWS, especially S3, for large-scale data processing and storage.
  

• Strong knowledge of SQL (PostgreSQL preferred); Snowflake SQL experience is a plus.
  

• Collaborate with ML and data engineering teams to embed AI/ML models directly into backend services, maintaining contextual awareness of video AI tradeoffs.
  

Reliability, Observability & Operations

• Implement comprehensive monitoring, logging, and tracing frameworks (Prometheus, Grafana, Jaeger) to maintain 99.99% uptime.
  

• Build and maintain CI/CD pipelines with GitHub Actions, ArgoCD, or Tekton, integrating security scans and automated testing for zero-downtime deployments.
  

• Profile and optimize backend services for low latency, cost efficiency, and high throughput under load.
  

• Ensure operational excellence under pressure—especially during tight delivery windows—while maintaining clear communication with leadership.
  

Security & Compliance

• Enforce zero-trust security principles, encryption at rest and in transit, and compliance with GDPR/CCPA.
  

• Work with the platform team to ensure all deployments meet Troveo’s data protection and reliability standards.
  

Cross-Functional Collaboration & Soft Skills

• Exhibit meticulous attention to detail, ensuring deliverables adhere precisely to contract terms and customer expectations.
  

• Communicate effectively under pressure, providing updates and clarity during time-sensitive project deliveries.
  

• Demonstrate strong lateral and technical communication, sharing customer delivery learnings across the engineering org to strengthen platforms and systems company-wide.
  

• Partner directly with Product to translate requirements into scalable, reliable backend solutions.
  

• 8+ years of backend software engineering experience, including system architecture and distributed systems design.
  

• Deep expertise in Go, Python, or Node.js, with production microservices experience.
  

• Strong understanding of Kubernetes, container orchestration, and cloud-native architectures.
  

• Hands-on experience with Kafka, NATS, or similar event-driven platforms.
  

• Proven experience operating at scale with a startup mentality - fast-moving, adaptable, and pragmatic.
  

• Familiar with video AI/ML systems - not leading their development, but understanding the tradeoffs that impact system design and performance.
  

• Experience implementing observability and CI/CD pipelines in production.
  

• Excellent communicator and mentor; capable of leading by example and elevating team technical standards.
  

• Prior experience in AI/ML infrastructure or large-scale data processing.
  

• Exposure to vector databases, Elasticsearch, or real-time analytics systems.
  

• Contributions to open-source backend or infrastructure projects.
  

• Experience in multi-cloud or hybrid environments.
  

Location: Strong preference for candidates based in the San Francisco Bay Area.
Compensation: $200,000 – $250,000 base salary + meaningful equity participation.

• Build the distributed backbone that powers the world’s largest AI video dataset.
  

• Tackle complex systems challenges at the intersection of data, AI, and infrastructure.
  

• Collaborate with a world-class engineering and research team shaping the future of AI video.
  

• High autonomy, high impact—your code will define the reliability and scale of Troveo’s platform.
  

• Competitive compensation with significant equity upside.