Kuku FM

About the role

We are seeking an AI Engineer to build and implement AI systems for content production at scale. You'll work at the intersection of engineering and content designing prompt pipelines, integrating generative models, and building the tooling that turns source material into finished creative output. The ideal candidate is technically strong but also has taste: someone who understands story and craft, and can tell the difference between output that's technically correct and output that's actually good.

Responsibilities

• Build and iterate on prompt pipelines and multi-agent workflow components
• Design and integrate agentic workflows orchestrate multi-step, tool-using agents that plan, call models, and hand off between stages in production
• Deploy and serve open-source models set up inference endpoints, manage GPU compute, and optimize for latency and cost
• Write evals compare outputs against references, quantify quality, and feed results back into the pipeline
• Work on data pipelines: structured extraction from messy source text, localization, similarity/dedup
• Debug and maintain pipeline stages in production

What you bring:  

• (1+/3+) years of engineering experience, or a strong portfolio of shipped projects
• Solid Python fundamentals clean, working, readable code
• Hands-on experience with LLM APIs and prompt engineering (personal projects count)
• Comfort with Git, REST APIs, and working in a Linux environment
• A feel for content and narrative you can judge whether generated output is actually good, not just valid
• Curiosity and clear communication you ask good questions and don't stay stuck silently

 Preferred

• Exposure to agent/orchestration frameworks (LangGraph, LangChain, CrewAI)
• Familiarity with vector databases, embeddings, or RAG (Qdrant, pgvector)
• Hands-on work with open-source generative media models Flux, LTX, Wan, or similar
• Experience deploying open-source models for inference (vLLM, ComfyUI, Replicate/Cog, Docker + GPU)
• Experience writing evals or LLM-as-judge scoring
• Node.js and Fastapi familiarity, or experience deploying on AWS

About the role

We are seeking an AI Engineer to build and implement AI systems for content production at scale. You'll work at the intersection of engineering and content designing prompt pipelines, integrating generative models, and building the tooling that turns source material into finished creative output. The ideal candidate is technically strong but also has taste: someone who understands story and craft, and can tell the difference between output that's technically correct and output that's actually good.

Responsibilities

• Build and iterate on prompt pipelines and multi-agent workflow components
• Design and integrate agentic workflows orchestrate multi-step, tool-using agents that plan, call models, and hand off between stages in production
• Deploy and serve open-source models set up inference endpoints, manage GPU compute, and optimize for latency and cost
• Write evals compare outputs against references, quantify quality, and feed results back into the pipeline
• Work on data pipelines: structured extraction from messy source text, localization, similarity/dedup
• Debug and maintain pipeline stages in production

What you bring:  

• (1+/3+) years of engineering experience, or a strong portfolio of shipped projects
• Solid Python fundamentals clean, working, readable code
• Hands-on experience with LLM APIs and prompt engineering (personal projects count)
• Comfort with Git, REST APIs, and working in a Linux environment
• A feel for content and narrative you can judge whether generated output is actually good, not just valid
• Curiosity and clear communication you ask good questions and don't stay stuck silently

 Preferred

• Exposure to agent/orchestration frameworks (LangGraph, LangChain, CrewAI)
• Familiarity with vector databases, embeddings, or RAG (Qdrant, pgvector)
• Hands-on work with open-source generative media models Flux, LTX, Wan, or similar
• Experience deploying open-source models for inference (vLLM, ComfyUI, Replicate/Cog, Docker + GPU)
• Experience writing evals or LLM-as-judge scoring
• Node.js and Fastapi familiarity, or experience deploying on AWS

We're seeking an experienced Engineer to join our engineering team, handling massive-scale data processing and analytics infrastructure that supports over 1B daily events, 3M+ DAU, and 50k+ hours of content. The ideal candidate will bridge the gap between raw data collection and actionable insights, while supporting our ML initiatives.

Key Responsibilities

• Lead and scale the Infrastructure Pod, setting technical direction for data, platform, and DevOps initiatives.
• Architect and evolve our cloud infrastructure to support 1B+ daily events — ensuring reliability, scalability, and cost efficiency.
• Collaborate with Data Engineering and ML pods to build high-performance pipelines and real-time analytics systems.
• Define and implement SLOs, observability standards, and best practices for uptime, latency, and data reliability.
• Mentor and grow engineers, fostering a culture of technical excellence, ownership, and continuous learning.
• Partner with leadership on long-term architecture and scaling strategy — from infrastructure cost optimization to multi-region availability.
• Lead initiatives on infrastructure automation, deployment pipelines, and platform abstractions to improve developer velocity.
• Own security, compliance, and governance across infrastructure and data systems.

Who You Are

• Previously a Tech Co-founder / Founding Engineer / First Infra Hire who scaled a product from early MVP to significant user or data scale.
• 5–12 years of total experience, with at least 2+ years in leadership or team-building roles.
• Deep experience with cloud infrastructure (AWS/GCP), 
• Experience with containers (Docker, Kubernetes), and IaC tools (Terraform, Pulumi, or CDK).
• Hands-on expertise in data-intensive systems, streaming (Kafka, RabbitMQ, Spark Streaming), and distributed architecture design.
• Proven experience building scalable CI/CD pipelines, observability stacks (Prometheus, Grafana, ELK), and infrastructure for data and ML workloads.
• Comfortable being hands-on when needed — reviewing design docs, debugging issues, or optimizing infrastructure.
• Strong system design and problem-solving skills; understands trade-offs between speed, cost, and scalability.
• Passionate about building teams, not just systems — can recruit, mentor, and inspire engineers.

Preferred Skills

• Experience managing infra-heavy or data-focused teams.
• Familiarity with real-time streaming architectures.
• Exposure to ML infrastructure, data governance, or feature stores.
• Prior experience in the OTT / streaming / consumer platform domain is a plus.
• Contributions to open-source infra/data tools or strong engineering community presence.

What We Offer

• Opportunity to build and scale infrastructure from the ground up, with full ownership and autonomy.
• High-impact leadership role shaping our data and platform backbone.
• Competitive compensation + ESOPs.
• Continuous learning budget and certification support.
• A team that values velocity, clarity, and craftsmanship.

Success Metrics

• Reduction in infra cost per active user and event processed.
• Increase in developer velocity (faster pipeline deployments, reduced MTTR).
• High system availability and data reliability SLAs met.
• Successful rollout of infra automation and observability frameworks.
• Team growth, retention, and technical quality.