Software Engineer - Cortex Code Agentic System
Staff Engineer · Staff · Full Time
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Role
What you'll do.
Lead AI engineering and machine learning systems as a Software Engineer on Snowflake's Cortex Code team, building enterprise-grade agentic coding systems for data-centric applications. This role requires 10+ years of AI/ML production experience with Staff-level technical leadership, expertise in evaluation systems and measurement frameworks for LLM agents, and proficiency in Python, TypeScript, and Go. You'll own the complete AI engineering lifecycle—from prompt and tool engineering to evaluation pipelines, deployment, and optimization—while collaborating with elite modeling and infrastructure teams to deliver production-ready intelligence agents at scale.
Responsibilities
- End-to-End Product Ownership: Own Cortex Code features from conception through production, designing and building agentic workflows, sophisticated coding harnesses, and comprehensive evaluation pipelines that measure agent performance across real-world data scenarios and edge cases.
- Enterprise-Grade Context Engineering: Architect and implement advanced context systems including function calling frameworks, dynamic tool schema generation, intelligent guardrails, multi-agent team orchestration, and automated verification and repair mechanisms to ensure reliable agent behavior in production.
- Evaluation & Measurement Systems: Design, build, and maintain production-grade evaluation harnesses and experimentation loops for LLM and agent systems that go beyond one-off benchmarks, establishing continuous measurement and quality gates to track performance degradation and drive systematic improvements.
- Cross-Functional Collaboration & Execution: Partner closely with product, infrastructure, and data teams to translate ambiguous customer problems into validated products and experiments; collaborate with infrastructure engineers to productionize improvements and scale agentic systems to enterprise reliability standards.
- Technical Leadership & Mentoring: Provide Staff-level technical direction, mentor junior and mid-level engineers, establish engineering best practices for AI systems, and influence technical decisions across teams through crisp documentation, constructive debate, and authority without formal power.
- LLM Safety & Observability: Implement comprehensive LLM observability infrastructure, develop safety and guardrail systems for production agents, and establish quality systems that serve as release gates, ensuring compliance, performance, and reliability for enterprise deployments.
- Complex Systems Architecture: Design and maintain complex systems handling substantial state management, intricate branching logic, and demanding operational requirements; take problems to completion with focus on production reliability, clear metrics, and reproducible solutions rather than prototype-stage work.
Qualifications
What we look for.
Technical
Proficiency in Multiple Programming Languages
Expert-level proficiency in at least two of: Python (primary for ML/AI systems), TypeScript/JavaScript (for agentic systems and tooling), or Go (for performance-critical infrastructure). Python expertise is particularly critical for ML frameworks, LLM integration, and data pipeline orchestration.
Large Language Model Engineering
Deep, hands-on experience building and shipping production AI/ML-backed software features at scale, including prompt engineering techniques, few-shot learning, chain-of-thought prompting, and understanding of model strengths, failure modes, and prompting limitations.
Agentic Systems & Coding Agents
Proven expertise with modern agentic coding tools and frameworks (IDE agents, CLI agents, multi-step reasoning systems). Ability to understand agent limitations, design effective prompting strategies, and implement reliability patterns for complex multi-step workflows.
Evaluation Frameworks & Measurement
Strong track record designing, building, and maintaining production-scale evaluation harnesses, benchmarking suites, and experimentation loops. Experience establishing metrics dashboards, continuous quality monitoring, and data-driven iteration cycles for AI systems—not limited to one-off benchmark testing.
Distributed Systems & Data Infrastructure
Experience building and operating complex systems with substantial state, sophisticated branching logic, and rigorous operational requirements. Familiarity with data engineering patterns, orchestration platforms, and scalable system architecture.
Education
Bachelor's Degree
Bachelor's degree in Computer Science, Engineering, Statistics, Mathematics, or a closely related technical field required. Demonstrates foundational knowledge in algorithmic thinking, systems design, and quantitative reasoning essential for AI systems work.
Advanced Degree (Preferred)
Master's degree or higher in Computer Science, Machine Learning, Statistics, Operations Research, or related field strongly preferred but not required. Advanced education signals depth in mathematical foundations, research methodology, and complex systems thinking.
Experience
10+ Years of AI/ML Production Experience
Minimum 10 years of professional software engineering experience specifically shipping AI and ML-backed features to production at scale. Demonstrated ability to move from conception to operational maturity with production-grade reliability.
Staff-Level Technical Leadership
Proven track record of Staff-level ownership including setting technical direction, leading complex cross-team initiatives, making architectural decisions that scale, and mentoring junior and mid-level engineers on advanced topics.
Production ML/LLM Systems
Deep experience building, deploying, and maintaining machine learning pipelines and LLM-based systems in production environments. Understanding of model deployment strategies, A/B testing for AI features, and managing technical debt in ML systems.
Data Engineering Pipeline Experience
Hands-on experience with data engineering tools and methodologies (dbt, Airflow), data modeling for analytics, and building retrieval systems or semantic layers. Knowledge of ETL/ELT patterns and data quality considerations for ML features is highly valuable.
Complex Problem Resolution
Proven ability to own ambiguous, open-ended problems end-to-end and drive them to completion. Experience taking vague quality or performance concerns, establishing clear metrics, and implementing sustained, reproducible improvements.
Skills
Required
Python
Advanced proficiency with Python for ML systems, including expertise with NumPy, Pandas, scikit-learn, PyTorch, or TensorFlow. Strong understanding of Python for building production data pipelines, API integrations, and LLM applications.
TypeScript/JavaScript
Strong TypeScript and JavaScript skills for building robust, typed agentic systems and tooling. Experience with modern frameworks and async/concurrent programming patterns for agent orchestration and execution.
Machine Learning & AI Systems
Comprehensive understanding of ML workflows including feature engineering, model training, hyperparameter tuning, evaluation metrics, and validation strategies. Expertise specifically in LLM fine-tuning, prompt optimization, and agentic reasoning architectures.
Prompt Engineering & Tool Design
Mastery of prompt engineering techniques for complex reasoning tasks including chain-of-thought, multi-turn interactions, and few-shot learning. Ability to design effective tool/function schemas and manage context windows for reliable agent behavior.
System Design & Architecture
Demonstrated ability to design scalable, maintainable systems handling complex state, operational requirements, and high-reliability needs. Knowledge of distributed computing patterns, failure modes, and resilience strategies.
Communication & Technical Leadership
Exceptional written and verbal communication skills including ability to author crisp technical specifications, lead constructive technical debates, and influence decisions across organizational boundaries. Proven ability to mentor and elevate team capabilities.
Preferred
Go Programming
Nice to haveProficiency in Go for building high-performance systems, CLI tools, and microservices. Valuable for optimizing critical infrastructure components and understanding systems-level programming.
LLM Observability & Monitoring
Nice to haveExperience implementing observability infrastructure for language models including logging, tracing, and monitoring systems. Familiarity with tools and patterns for detecting model degradation and tracking prompt/completion quality.
Safety, Guardrails & Alignment
Nice to haveBackground in implementing safety mechanisms, content filtering, and alignment techniques for AI systems. Experience building production systems with guardrails and understanding adversarial prompting or jailbreak prevention.
Data Engineering & Orchestration
Nice to haveHands-on experience with dbt (data build tool), Apache Airflow, Dagster, or similar orchestration platforms. Knowledge of data quality frameworks, incremental processing, and building reliable data pipelines that feed ML systems.
Retrieval Augmented Generation (RAG)
Nice to haveExperience building and optimizing RAG systems, including vector database integration, semantic search, and context ranking. Understanding of embedding models and relevance scoring for information retrieval in agentic systems.
Semantic Layers & Analytics
Nice to haveFamiliarity with semantic layer concepts and modern semantic SQL tools that abstract database complexity for analytics. Understanding of how semantic layers enhance AI agent capabilities for data-centric reasoning.
Coding Agent Tools
Nice to haveDeep familiarity with contemporary coding agents and IDE intelligence tools (GitHub Copilot, Cursor, Codeium, etc.). Ability to evaluate agent UX, identify failure patterns, and translate user feedback into systematic improvements.
Tech stack
Languages
Frameworks
Databases
Tools
Other
Compensation
Pay and benefits.
Base·USD 236,000 – 309,750
Equity·Stock options
Full posting
Original listing.
At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.
Snowflake is about empowering enterprises to achieve their full potential, and people too. With a culture that’s all in on impact, innovation, and collaboration, Snowflake is the sweet spot for building big, moving fast, and taking technology, and careers, to the next level.
About the Role
The Cortex Code team is building the future of coding agents for working with data. See our flagship product in action: Cortex Code in Action: Live Demos + AMA.
Your work will directly impact how developers and businesses build with data. You'll own the full AI engineering lifecycle: design, prompt/tool engineering, evals, deployment, measurement, and optimization. You'll work with a small, high-powered modeling and infrastructure team. What you will do in this role:
Own features end-to-end for Snowflake Cortex Code products. Build agentic workflows, coding harnesses, evaluation pipelines.
Build enterprise-grade context engineering: function calling, tool schemas, guardrails, agent teams, and verification/repair.
Partner with product and infra: translate customer problems into products and experiments. Collaborate with infrastructure teams to productionize improvements.
Work with an elite team of engineers towards building great products
Requirements:
Bachelor’s degree in Computer Science, Engineering, Statistics or a related field. Master’s or higher degree preferred but not a requirement.
5+ years of experience shipping AI features in production.
Proficiency in programming languages such as Python, Typescript, Go
Strong communication skills and ability to collaborate effectively in a team environment.
(Optional) Experience working with data engineering pipelines (dbt, airflow), data modeling, data analysis, retrieval systems, and semantic layers is a plus.
Nice to have
Deep experience with agentic coding tools (e.g. IDE agents, CLI agents) and intuition for model strengths, failure modes, and prompting limits.
Background in data engineering (dbt, Airflow), data modeling, analytics, retrieval / RAG, or semantic layers — highly relevant for data-centric coding agents.
Prior work on eval harnesses, LLM observability, or safety / guardrails in production.
You may be a particularly good fit if you
Have built and owned complex systems — pipelines, orchestration, or software with substantial state, branching logic, and operational requirements.
Thrive in high-intensity environments with short feedback loops and high standards for rigor.
Take problems to completion independently: you don’t stop at a prototype; you care about production reliability and clear metrics.
Are a power user of modern coding agents and care about turning that intuition into systematic measurement and improvement.
About Snowflake
Snowflake is the AI Data Cloud trusted by the world's most innovative companies. We're shipping production-ready AI applications at scale and want you to join us in building the future of how businesses interact with their data through Cortex Code, Cortex agents, Cortex analyst, Cortex search.
Requirements:
Bachelor’s degree in Computer Science, Engineering, Statistics, or a related field. Master’s or higher preferred but not a requirement.
10+ years of experience shipping AI/ML-backed software in production, including Staff-level ownership of technical direction, cross-team delivery, and mentoring.
Strong track record building and operating eval harnesses, measurement, and/or experimentation loops for LLM/agent systems—not only one-off benchmarks.
Proficiency in programming languages such as Python, TypeScript, Go (strong in at least two).
Exceptional communication skills: crisp write-ups, constructive debate, and ability to influence without authority across engineering and product.
(Optional) Experience with data engineering pipelines (dbt, Airflow), data modeling, data analysis, retrieval systems, and semantic layers is a plus.
Nice to have
Deep experience with agentic coding tools (IDE agents, CLI agents) and intuition for model strengths, failure modes, and prompting limits.
Background in data engineering (dbt, Airflow), analytics, retrieval / RAG, or semantic layers—highly relevant for data-centric coding agents.
Prior work on LLM observability, safety/guardrails, or quality systems used as release gates in production.
You may be a particularly good fit if you
Have built and owned complex quality + data pipelines—substantial state, branching logic, and operational requirements.
Thrive in high-intensity environments with short feedback loops and high standards for rigor.
Take ambiguous “quality is slipping” problems to completion: you care about clear metrics, reproducibility, and sustained improvement—not one-off score bumps.
Are a power user of modern coding agents and care about turning intuition into systematic measurement and team-wide practice.
Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.
How do you want to make your impact?
For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com
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