# Principal Forward Deployed Engineer I, Frontier Engineering
**Company:** [Snowflake](https://scaleengineer.com/companies/snowflake)
Principal Forward Deployed Engineer I at Snowflake's Frontier Engineering team, working directly with enterprise customers to design, build, and deploy production-grade AI systems at scale. This Principal-level role positions you as the senior technical counterpart to CTOs and VPs of Engineering, driving architectural strategy for complex agentic systems, RAG pipelines, and enterprise AI deployments while mentoring senior engineers and maintaining hands-on technical leadership across high-stakes customer engagements.
**Role:** Principal Engineer
**Seniority:** Principal
**Locations:** US-CA-Menlo Park
**Salary:** 264000–379500 USD
[Apply](https://jobs.ashbyhq.com/snowflake/2c4475a5-c88a-48fc-878a-8b324679745a)
Canonical: https://scaleengineer.com/jobs/snowflake/principal-forward-deployed-engineer-i-frontier-engineering
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## Responsibilities

- Serve as Trusted Technical Partner to Customer Leadership: Build and maintain peer-level relationships with CTOs, VPs of Engineering, chief architects, and heads of data/AI at Snowflake's most strategic enterprise accounts. Lead architectural steering committees, conduct technical reviews, and provide executive strategy alignment to shape customer AI roadmaps. Act as the senior technical counterpart who influences enterprise-scale decision-making.
- Drive End-to-End Technical Architecture & Strategy: Own complete technical execution and outcomes across Snowflake's most complex, high-visibility AI deployments. Design production-grade AI system architectures that balance technical excellence with enterprise requirements. Set technical direction and standards that the Frontier Engineering organization follows for subsequent customer engagements.
- Deliver Production Code & Technical Implementation: Write and ship production-quality code and working prototypes on the most critical components of customer AI programs. Actively code-review designs and implementations to establish and maintain the quality bar for enterprise-scale deployments. Stay hands-on with implementation details across full AI/ML system lifecycles including model optimization and deployment.
- Establish AI Quality Standards & Evaluation Frameworks: Lead the definition and implementation of how the team measures and improves AI system quality across all dimensions. Convert ambiguous customer goals and business requirements into clear, testable, and quantifiable evaluation standards. Build evaluation frameworks and guardrails that ensure production AI systems meet enterprise reliability and performance expectations.
- Mentor & Develop Senior Engineering Talent: Provide technical leadership and mentorship to Senior and Staff engineers within the Frontier Engineering organization. Raise engineering standards across the team through code reviews, architectural guidance, and knowledge sharing. Partner with cross-functional leads to drive complex technical initiatives and develop the next generation of technical leaders.
- Conduct On-Site Customer Engagements: Spend minimum 25% of time working directly at customer sites, embedding with enterprise engineering teams. Understand customer technical environments, business constraints, and organizational structures. Build deep relationships and gather technical insights that inform both customer-specific solutions and platform improvements.

## Requirements

### education

- {"name":"Bachelor's Degree in Computer Science or Related Field","description":"Foundational formal education in computer science, software engineering, mathematics, or related technical discipline. (Alternative: equivalent professional experience demonstrating advanced technical expertise.)"}

### technical

- {"name":"Production AI/ML Systems Architecture","description":"Deep expertise designing and shipping production-scale AI systems including RAG pipelines, multi-step agentic systems, orchestration frameworks, and end-to-end model deployment architectures. Experience with prompt engineering, retrieval strategies, and building systems that reliably operate in complex enterprise environments."}
- {"name":"AI Quality & Evaluation Frameworks","description":"Demonstrated ability to define AI quality metrics, build evaluation frameworks, and implement guardrails for production systems. Experience measuring model performance, detecting degradation, and iterating on AI systems to meet enterprise SLAs and business objectives."}
- {"name":"Large-Scale Enterprise Software Architecture","description":"Systems design expertise for complex, mission-critical applications. Experience with distributed systems, scalability challenges, data infrastructure, and building platforms that operate reliably at enterprise scale with high availability requirements."}
- {"name":"Multi-Language Software Engineering","description":"Strong hands-on coding ability in modern programming languages commonly used in enterprise AI/ML environments. Ability to write, optimize, and review production code while mentoring others on best practices and architectural patterns."}
- {"name":"Data Infrastructure & Platform Knowledge","description":"Working knowledge of modern enterprise data infrastructure, data warehousing patterns, and ETL/ELT architectures. Familiarity with how data platforms integrate with AI/ML systems and the implications for model development and deployment."}

### experience

- {"name":"15+ Years Enterprise Software Engineering","description":"Extensive track record delivering complex software systems in enterprise environments. Progressive growth through mid-level, senior, and staff-level engineering roles with demonstrated technical leadership and architectural influence."}
- {"name":"3+ Years Production AI/ML Systems at Enterprise Scale","description":"Proven experience building, shipping, and operating production AI and ML systems or agentic applications in complex enterprise settings. Experience iterating on models, managing technical debt, and scaling systems to meet production reliability requirements."}
- {"name":"Technical Advisor to Executive Engineering Leadership","description":"Documented history of serving as trusted technical advisor to CTOs, VPs of Engineering, and chief architects at large enterprises. Experience influencing technical strategy at the C-suite level and navigating complex organizational dynamics."}
- {"name":"Field Engineering or Customer-Facing Technical Delivery","description":"Track record converting field delivery patterns, customer requirements, and domain knowledge into reusable platforms, tools, frameworks, and libraries. Experience translating one-off customer solutions into scalable products or best practices."}
- {"name":"Technical Leadership & Mentorship","description":"Demonstrated ability to mentor and develop senior engineers, raise engineering standards, and lead technical initiatives across organizations. Experience influencing team culture and establishing best practices through example and advocacy."}

## Skills

### required

- {"name":"AI/ML Systems Architecture","description":"Designing production-grade AI architectures including RAG systems, prompt engineering strategies, vector databases, and agentic systems with complex reasoning and decision-making capabilities."}
- {"name":"Production Engineering & DevOps Mindset","description":"Deep understanding of reliability, scalability, monitoring, and deployment strategies for production systems. Experience with SLAs, incident management, and building systems that fail gracefully under stress."}
- {"name":"Technical Communication Across Audiences","description":"Ability to communicate complex technical concepts clearly to both deeply technical engineers and non-technical executives. Skill in writing design documents, architectural proposals, and executive summaries that drive decision-making."}
- {"name":"Systems Thinking & Problem Decomposition","description":"Strong ability to break down complex problems into manageable components, identify critical path items, and understand second and third-order consequences of architectural decisions."}
- {"name":"Enterprise Software Development","description":"Experience building enterprise-grade software with emphasis on security, compliance, maintainability, and operational excellence. Understanding of enterprise customer requirements, procurement processes, and vendor evaluation criteria."}

### preferred

- {"name":"Experience with Snowflake Platform","description":"Hands-on experience with Snowflake's data warehouse, familiarity with Cortex AI capabilities, or knowledge of Snowpark for data applications. Understanding of how Snowflake integrates with modern AI/ML infrastructure."}
- {"name":"Commercial Data/AI Platform Development","description":"Track record shipping features into commercial data or AI platforms based on customer requirements. Experience balancing product vision with customer needs and navigating the constraints of platform-based delivery models."}
- {"name":"Open Source Contributions & Community Engagement","description":"Active contributions to open-source AI frameworks, technical publications, conference speaking engagements, or thought leadership. Demonstrated commitment to advancing the broader AI/ML engineering community."}
- {"name":"Advanced ML Operations","description":"Experience with MLOps practices, model versioning, experiment tracking, feature stores, and operationalizing ML systems at scale. Knowledge of tools and practices for maintaining model quality in production."}
- {"name":"Enterprise Data Architecture","description":"Deep understanding of modern enterprise data infrastructure, data governance, data mesh patterns, and the integration between data platforms and AI/ML systems."}

## Tech stack

### tools

- {"name":"Git & Version Control","description":"Proficiency with Git, GitHub, or similar distributed version control systems. Experience managing large codebases, code reviews, and collaborative development in enterprise environments."}
- {"name":"MLOps & Model Management","description":"Experience with MLOps tools such as MLflow, Weights & Biases, DVC, or similar for model versioning, experiment tracking, and lifecycle management of production AI systems."}
- {"name":"Containerization & Orchestration","description":"Hands-on experience with Docker, Kubernetes, and container orchestration for deploying and managing AI applications in enterprise environments. Understanding of deployment patterns for production systems."}
- {"name":"Monitoring & Observability","description":"Experience with observability platforms, APM tools, and logging frameworks (e.g., ELK stack, Datadog, New Relic). Ability to instrument AI systems for production monitoring and alerting."}
- {"name":"Evaluation & Testing Frameworks","description":"Experience building and using evaluation frameworks for AI systems. Tools and practices for testing model performance, building CI/CD pipelines for ML, and automating quality checks."}

### others

- {"name":"Large Language Models & Generative AI","description":"Deep working knowledge of modern LLMs (GPT-4, Claude, specialized open-source models), their capabilities and limitations, prompt engineering strategies, and fine-tuning approaches for enterprise applications."}
- {"name":"Agentic Systems & Multi-Step Reasoning","description":"Understanding of agentic AI architectures, orchestration patterns for multi-step reasoning, tool use and function calling, and building systems where AI makes decisions and takes actions across multiple iterations."}
- {"name":"Enterprise AI Quality & Guardrails","description":"Expertise in ensuring AI system safety, implementing guardrails, handling hallucinations and model errors, and building trust and explainability into production AI systems for regulated enterprises."}
- {"name":"Distributed Systems & Scale","description":"Understanding of distributed systems concepts, eventual consistency, fault tolerance, and the challenges of scaling systems to handle enterprise workloads with high availability requirements."}

### databases

- {"name":"Snowflake","description":"Primary data warehouse and AI platform used by Snowflake customers. Deep understanding of Snowflake's architecture, Cortex AI capabilities, and integration patterns with modern AI/ML applications."}
- {"name":"Vector Databases","description":"Experience with vector databases like Pinecone, Weaviate, Milvus, or built-in vector search capabilities in Snowflake. Essential for RAG system implementations and semantic similarity search in AI applications."}
- {"name":"Enterprise Data Warehousing","description":"Knowledge of modern cloud data platforms (BigQuery, Redshift, Azure Synapse). Understanding of data warehouse architecture patterns and how data accessibility impacts AI/ML system design."}

### languages

- {"name":"Python","description":"Primary language for AI/ML development, data science, and building machine learning systems. Essential for model development, data preprocessing, and building AI applications."}
- {"name":"SQL","description":"Core language for enterprise data access, query optimization, and data manipulation within Snowflake and modern data warehouses. Critical for data engineering aspects of AI systems."}
- {"name":"Java or Scala","description":"Used in enterprise backend systems and distributed data processing frameworks. May be required for understanding enterprise software architecture and large-scale data processing pipelines."}

### frameworks

- {"name":"LLM/Agentic Frameworks","description":"Experience with frameworks for building agentic systems, such as LangChain, LlamaIndex, AutoGen, or similar tools for orchestrating complex AI workflows with multiple reasoning steps."}
- {"name":"RAG Frameworks","description":"Knowledge of retrieval-augmented generation frameworks and patterns, including vector database integration, semantic search, and hybrid retrieval strategies for grounding LLMs in enterprise data."}
- {"name":"ML/DL Frameworks","description":"Proficiency with production ML frameworks like PyTorch, TensorFlow, or scikit-learn for model training, evaluation, and optimization. Understanding of model serving frameworks like FastAPI, Seldon, or similar."}
- {"name":"Snowpark & Snowflake APIs","description":"Preferred experience with Snowpark for data application development and Snowflake's API ecosystem. Understanding of how to build AI applications that leverage Snowflake's data infrastructure."}

## Benefits

### benefits

- {"name":"Comprehensive Health Coverage","description":"Medical, dental, and vision insurance with company-subsidized premiums. Coverage extends to family members and dependents with flexible plan options."}
- {"name":"401(k) Retirement Plan with Company Match","description":"Tax-advantaged retirement savings with competitive company matching contribution. Financial planning resources and guidance available to all employees."}
- {"name":"Equity & Stock Options","description":"Participation in Snowflake's equity program as a core component of total compensation. Stock options with favorable vesting schedules aligned with long-term value creation."}
- {"name":"Flexible Time Off","description":"Unlimited paid time off policy (PTO) with manager support to ensure meaningful time away. Separate paid leave for holidays and major life events."}
- {"name":"Professional Development & Learning","description":"Annual learning budget for conferences, courses, certifications, and technical development. Tuition reimbursement for continuing education and advanced degrees. Access to online learning platforms."}
- {"name":"Remote Work & Flexibility","description":"Flexible work arrangements with ability to work from home or Snowflake offices. Collaboration tools and infrastructure support for distributed teams. Travel support for on-site customer engagements."}
- {"name":"Parental & Caregiver Leave","description":"Paid parental leave for all parents regardless of gender. Fertility benefits, adoption assistance, and caregiver support programs. Backup childcare and eldercare resources."}
- {"name":"Mental Health & Wellness","description":"Comprehensive mental health support including counseling services, therapy sessions, and behavioral health programs. Fitness benefits, wellness programs, and on-site health resources."}
- {"name":"Commuter & Relocation Benefits","description":"Pre-tax commuter benefits for public transportation. Relocation assistance package for candidates relocating for the role. Home office equipment stipend."}
- {"name":"Life Insurance & Disability Coverage","description":"Life insurance coverage provided at no cost to employees. Short-term and long-term disability insurance for income protection. Accidental death and dismemberment coverage."}

## Compensation

- **max:** 350000
- **min:** 250000
- **currency:** USD
- **stockOptions:** true

## Interview process

### steps

## Full description
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.

We are hiring a Principal Forward Deployed Engineer for our Frontier Engineering team. This team embeds directly with Snowflake's most strategic customers to design, build, and deploy production-grade AI systems —. At the Principal level, you are the senior technical counterpart to CTOs, VPs of Engineering, and chief architects at our largest enterprise customers; you set the technical direction that the Frontier Engineering organization follows.

## AS A PRINCIPAL FORWARD DEPLOYED ENGINEER YOU WILL:

* **Be the trusted technical partner to customer engineering leadership:** Build durable, peer-level relationships with CTOs, VPs of Engineering, chief architects, and heads of data/AI at Snowflake’s most strategic accounts: lead architectural steering, technical reviews, and executive strategy alignment to help shape their AI roadmaps.
* **Drive Architecture & Technical Strategy Across High-Stakes Deployments:** Own end-to-end technical execution and outcomes across Snowflake’s most complex, high-visibility AI engagements.
* **Stay hands-on:** Write production code and working prototypes on the most critical parts of customer programs, and review designs and code to set the bar for how the team builds.
* **Own the AI quality bar:** Lead how the team measures and improves AI quality, turning ambiguous customer goals into clear, testable evaluation standards.
* **Provide Technical Leadership & Mentorship:** Mentor Senior and Staff engineers, raise engineering standards across the organization, and partner with cross-functional leads to drive complex technical initiatives.
* **Travel to Customer Sites:** Spend at least 25% of your time onsite working with customers.

## OUR IDEAL CANDIDATE WILL HAVE:

* 15+ years of software engineering experience, including at least 3 years building and shipping production AI/ML or agentic systems at enterprise scale.
* Proven track record as a trusted technical advisor to CTOs and executive engineering leaders at large enterprises.
* Demonstrated success converting client/field delivery patterns into reusable platforms, tools, frameworks, or open-source libraries.
* Deep expertise in production AI architectures—including RAG pipelines, multi-step agentic systems, evaluation frameworks, and guardrails.
* Hands-on builder who writes and reviews production code and stays close to implementation.
* Strong systems mindset with exceptional technical communication skills across both deeply technical and executive audiences.

## BONUS POINTS FOR THE FOLLOWING:

* Experience shipping features into a commercial data or AI platform, based on customer requirements.
* Active contributions to open-source AI frameworks, technical publications, or industry speaking engagements.
* Familiarity with Cortex AI, Snowpark, or modern enterprise data infrastructure platforms.

## WHY JOIN FRONTIER ENGINEERING AT SNOWFLAKE?

Frontier Engineering is where Snowflake’s AI platform meets reality — you’re not building demos, you’re shipping AI applications that run in some of the world’s most complex enterprise environments. You will work on hard problems with real stakes, alongside engineers who are deeply technical and customer-obsessed.

How do you want to make your impact?

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](http://careers.snowflake.com)