Senior AI Engineer: Production Systems at Scale

Neura Market; New York, Northern (NY, KY); Hybrid; USD 150,000 - 210,000; Full time

Job summary

Snorkel AI is seeking AI Engineers who combine strong software and distributed systems fundamentals with production experience in AI systems. You will build the infrastructure that enables teams to create, evaluate, and operate LLM and agentic workloads at scale.

Join a fast-moving team focusing on reproducibility, observability, and reliable execution across models, prompts, tools, and environments. You’ll work to make non-deterministic workloads measurable and debuggable while expanding access

Qualifications

• 5+ years building production software systems in AI/ML infrastructure.
• Experience operating non-deterministic AI workloads at scale.
• Strong Python and API development experience.
• Distributed systems and cloud platforms (AWS) expertise.

Responsibilities

• Design and build infrastructure for running large-scale agentic workloads.
• Build scalable synthetic data generation and labeling systems.
• Design evaluation infrastructure for AI systems across models and tools.
• Build orchestration and distributed compute systems across heterogeneous environments.
• Develop infrastructure for agent simulation environments and lifecycle management.
• Build and operate LLM infrastructure for routing, caching, and cost attribution.
• Instrument workloads to capture traces, interactions, latency, and cost.
• Design non-deterministic workloads that are reproducible and measurable.
• Improve developer experience with APIs, SDKs, and tooling for large-scale execution.
• Collaborate with research, product, and engineering to turn experiments into platform capabilities.

Skills

Python

Distributed systems

AWS

AI infrastructure

Observability

Experimentation

Technical leadership

Tools

Prefect

Airflow

Dagster

Ray

Kubernetes

Job description

Snorkel AI is seeking AI Engineers who combine strong software and distributed systems fundamentals with production experience in AI systems. You will build the infrastructure that enables teams to create, evaluate, and operate LLM and agentic workloads at scale.

Join a fast-moving team focusing on reproducibility, observability, and reliable execution across models, prompts, tools, and environments. You’ll work to make non-deterministic workloads measurable and debuggable while expanding access