🛠 Tech Stack

💼 About This Role

You'll lead the team that powers Decagon's AI agents, owning data and inference systems from streaming pipelines to GPU model-serving. You'll drive architecture for high-throughput data and low-latency LLM inference while staying hands-on with code. This is a player/coach role that combines deep technical leadership with building a high-performing team at a well-funded Series B startup.

🎯 What You'll Do

• Build, lead, and develop a team of data and ML infrastructure engineers
• Own technical strategy for streaming/batch data, realtime databases, and GPU/model-serving stack
• Review designs and PRs, contribute code when needed
• Drive architecture for high-throughput data systems and low-latency inference

📋 Requirements

• 2+ years of engineering management experience leading data/ML/infrastructure teams
• Deep technical depth in streaming/batch processing, analytical databases, or model-serving
• Hands-on experience with large-scale data systems like Kafka, ClickHouse, or Postgres at scale
• Familiarity with cloud platforms (AWS, GCP, or Azure), Kubernetes, and infrastructure-as-code

✨ Nice to Have

• Experience operating LLM inference infrastructure in production
• Experience with realtime analytics engines like ClickHouse or Druid
• Experience delivering data and inference capabilities to enterprise customers

🎁 Benefits & Perks

• 💰 Competitive Salary ($280K-$430K)
• 📈 Equity (included in compensation)
• 🏢 In-office work environment in San Francisco
• 🧠 Backed by top-tier investors (a16z, Accel, etc.)

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Interview· 60 min
3. 3Onsite Interviews· 3 hours