Troveo AI
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.
Role Overview 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.
Key Responsibilities 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 with
GitHub Actions ,
ArgoCD , or
Tekton , 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.
Qualifications & Experience
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.
Nice to Have
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 & Compensation Location:
Strong preference for candidates based in the
San Francisco Bay Area . Compensation:
$200,000 – $300,000 base salary + meaningful equity participation.
Why Join Troveo?
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.
#J-18808-Ljbffr
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.
Role Overview 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.
Key Responsibilities 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 with
GitHub Actions ,
ArgoCD , or
Tekton , 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.
Qualifications & Experience
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.
Nice to Have
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 & Compensation Location:
Strong preference for candidates based in the
San Francisco Bay Area . Compensation:
$200,000 – $300,000 base salary + meaningful equity participation.
Why Join Troveo?
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.
#J-18808-Ljbffr