9 hours ago
Base Salary
$236k - $295k/yr
Responsibilities
- Architect and build the Agentic Harness for complex multi-step AI workflows.
- Design interfaces for tool execution, context construction, state, memory, permissions, retries, fallbacks, and human review.
- Build evaluation harnesses, datasets, automated graders, experiment pipelines, release gates, and regression prevention systems.
- Analyze production agent trajectories and incidents to identify failures and develop durable fixes.
- Develop offline and online measurements for task completion, correctness, groundedness, safety, latency, reliability, and cost.
- Build simulation and replay infrastructure for golden-set tests, adversarial scenarios, model comparisons, and large-scale experiments.
- Improve agent efficiency through model routing, prompt and semantic caching, context compaction, tool-result management, and token optimization.
- Productionize model capabilities as secure, observable, multi-tenant services.
- Establish evaluation standards for sampling, ground-truth quality, grader calibration, leakage prevention, and statistical significance.
- Define technical direction across multiple teams, lead broad-scope projects, mentor engineers, and contribute directly to implementation and debugging.
Requirements
- 9+ years of software engineering experience, including technical leadership of complex production systems.
- Direct experience shipping and operating LLM applications, AI agents, or model-backed workflows in production.
- Strong background in distributed systems, service architecture, high-throughput APIs, concurrency, and failure handling.
- Experience building an agent runtime, workflow engine, developer platform, evaluation system, or similar infrastructure.
- Ability to evaluate nondeterministic systems beyond reliance on a single aggregate score.
- Fluency in Python and strong proficiency in at least one of Java, Go, Rust, or TypeScript.
- Hands-on knowledge of tool calling, structured generation, retrieval, context engineering, prompt management, and model APIs.
- Experience with production observability, structured traces, replay, metrics, logs, and incident diagnosis.
- Ability to balance agent quality with latency, reliability, security, and inference cost.
- Track record of setting technical direction and delivering results across organizational boundaries.
- Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- Strong written and verbal communication skills.
- Preferred experience includes agent evaluation or observability infrastructure, human-evaluation programs, multi-agent orchestration, simulations, adversarial testing, retrieval systems, sensitive-data platforms, model training or fine-tuning, and databases, SQL engines, data platforms, Kubernetes, or cloud-native infrastructure.
Tech Stack
Categories
About Snowflake
Snowflake builds a cloud-native data platform used by enterprises to store, integrate, share, and analyze data across AWS, Azure, and Google Cloud. Its core products span data warehousing, data lakes, data engineering, and governed data sharing, sold via consumption-based subscriptions. Founded in 2012 and publicly traded on the NYSE (SNOW) following a 2020 IPO, Snowflake supports analytics and data application workloads across industries.