Scale Engineer

Sr Manager, Applied Field Engineering - AI/ML

Engineering Manager · Manager · Full Time

US-NY-New YorkUSD 207k – 272k2d ago
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Role

What you'll do.

Lead a high-performing team of Applied Field Engineers at Snowflake specializing in Generative AI, Machine Learning, and Advanced Analytics. In this hands-on management role, you'll drive consumption activation, coach technical sales excellence, and translate field insights into product strategy while managing the technical and professional development of your team in the rapidly evolving AI/ML landscape.

Responsibilities

  • Technical Execution and Consumption Activation Leadership: Drive team performance toward consumption activation metrics, ensuring customers successfully move AI/ML and analytics workloads into production and realize contracted credit value. Establish KPIs for team performance, track progress against activation targets, and implement strategies to accelerate customer time-to-value.
  • Technical Sales Coaching and Mentorship: Coach Applied Field Engineers on technical sales engagement best practices, helping them identify, prioritize, and execute high-impact customer opportunities. Develop playbooks for common customer scenarios, conduct deal reviews, and provide real-time guidance during complex sales engagements focused on AI/ML and advanced analytics capabilities.
  • Customer Architecture Review and Technical Guidance: Review and provide hands-on guidance on customer architectures to prevent technical debt, ensure scalability, and optimize performance across GenAI, machine learning, and data engineering use cases. Serve as a technical escalation point for complex architectural decisions and ensure alignment with Snowflake's best practices.
  • Player-Coach Customer Engagement: Actively participate in customer engagements as a hands-on leader, modeling excellence in technical architecture discussions, executive-level conversations, and solution design. Build trust with VP and Director-level stakeholders while demonstrating technical depth in GenAI/LLM, machine learning, and cloud data architecture.
  • Product Feedback Integration and Cross-Functional Collaboration: Aggregate field insights and communicate recurring product gaps and customer blockers to senior leadership and product teams to inform roadmap discussions. Partner with Sales leadership to align technical resources with regional pipeline and key account priorities, ensuring Applied Field Engineering resources are deployed strategically.
  • Communities of Practice and Knowledge Sharing: Participate in and support Applied Field Engineering Communities of Practice, driving knowledge-sharing initiatives across the team. Facilitate best practice sharing sessions, technical skill development, and industry trend discussions to keep the team current with emerging AI/ML technologies and market demands.
  • Team Recruitment, Onboarding, and Development: Recruit, interview, and onboard a team of high-caliber Applied Field Engineers with strong technical backgrounds in AI/ML, machine learning, or data engineering. Manage team expansion, ensuring new hires have the technical skills and sales acumen to succeed in pre-sales and technical advisory roles.
  • Technical Sales Culture Building: Build and maintain a culture of technical sales excellence where Applied Field Engineers serve as trusted technical advisors to customers throughout the entire sales and post-sales lifecycle. Foster a collaborative, high-performing environment that attracts and retains top technical talent in the competitive AI/ML and data engineering market.
  • Performance Management and Career Development: Conduct regular one-on-one meetings with direct reports, provide constructive feedback on technical and professional development, and create career growth paths. Establish clear performance expectations, conduct performance reviews, and support career advancement for high performers while addressing performance gaps promptly.
  • Executive Communication and Reporting: Prepare regular reports on team performance, consumption activation metrics, and customer insights for senior leadership. Present strategic recommendations based on field intelligence, market trends in AI/ML adoption, and competitive dynamics to inform broader organizational strategy.
  • Technical Skill Development and Training: Design and deliver technical training programs to keep your team current with Snowflake's latest AI/ML capabilities, including Generative AI features, LLM integration patterns, machine learning model deployment, and advanced analytics techniques. Foster a culture of continuous learning and technical excellence.
  • Cross-Functional Alignment: Work closely with Sales, Solutions Engineering, Product Management, and Customer Success teams to ensure seamless handoffs and aligned messaging around Snowflake's AI/ML value proposition. Participate in sales kickoffs, account planning sessions, and strategic initiatives to amplify technical field resources impact.
  • Customer Success and Activation Metrics: Monitor and optimize consumption activation metrics for your team's customers, identifying blockers to production adoption and working with customers to overcome technical or organizational obstacles. Drive post-sales technical engagement that accelerates customer value realization and expands account engagement.
  • Competitive Intelligence and Market Insights: Gather competitive intelligence from customer interactions regarding alternative solutions and market positioning. Provide market feedback to leadership on customer preferences, competitive threats in the AI/ML data platform space, and emerging customer needs.
  • Policy Compliance and Data Security: Ensure your team follows Snowflake's confidentiality and security standards for handling sensitive customer data. Maintain accountability for data security practices within your team and model exemplary stewardship of customer information throughout all technical engagements.
  • Sales Pipeline Support and Deal Acceleration: Support Sales teams in advancing the pipeline by providing technical expertise during high-value customer negotiations, proof-of-concept engagements, and architectural discussions. Identify opportunities to leverage Applied Field Engineering resources to accelerate deal closure and improve customer outcomes.

Qualifications

What we look for.

Technical

  • Generative AI and Large Language Models (LLMs)

    Hands-on experience with GenAI and LLM technologies, including prompt engineering, fine-tuning, RAG architectures, and integration patterns with enterprise data platforms. Ability to discuss LLM capabilities, limitations, and use case applicability with technical and business stakeholders.

  • Machine Learning and Model Development

    Deep understanding of machine learning fundamentals, model training and evaluation, feature engineering, and deployment patterns. Experience with ML frameworks, model monitoring, and optimization techniques relevant to enterprise data science teams.

  • Data Engineering and Cloud Data Architecture

    Strong technical foundation in data engineering principles, ETL/ELT pipelines, data warehouse architecture, and cloud data platform design. Familiarity with Snowflake's architecture, data sharing capabilities, and integration with analytics tools.

  • Snowflake Platform and Ecosystem

    In-depth knowledge of Snowflake's data warehousing capabilities, pricing models, consumption metrics, and AI/ML features. Understanding of Snowflake's ecosystem, partner integrations, and industry-specific solutions.

  • Technical Sales Engagement Methodologies

    Expertise in solution architecture, customer requirements discovery, technical proof-of-concept design, and ROI communication. Ability to translate complex technical concepts into business value propositions for executive stakeholders.

  • Python and SQL for Data Analysis

    Proficiency in Python for data analysis and scripting, combined with strong SQL skills for complex query optimization and data exploration. Ability to validate customer solutions and provide technical guidance on query performance.

  • Cloud Computing Platforms

    Working knowledge of major cloud providers (AWS, Azure, GCP) and how they integrate with Snowflake. Understanding of cloud architecture patterns, security models, and cost optimization strategies.

  • Solutions Architecture and Design Patterns

    Experience designing scalable, production-grade data solutions that balance performance, cost, and maintainability. Knowledge of best practices for high-availability systems, disaster recovery, and data governance.

  • Leadership and Team Management

    Demonstrated ability to build, motivate, and develop high-performing technical teams. Experience with performance management, strategic workforce planning, and creating cultures of technical excellence.

Education

  • Bachelor's Degree in Computer Science, Engineering, or Mathematics

    University degree in computer science, software engineering, electrical engineering, mathematics, or closely related technical field. Provides foundational knowledge in algorithms, data structures, and quantitative analysis essential for technical leadership.

  • Advanced Degree or Equivalent Professional Certification (Preferred)

    Master's degree in Computer Science, Data Science, Machine Learning, or related field, or equivalent professional certifications such as Google Cloud Professional Data Engineer, AWS Certified Machine Learning Specialty, or industry-recognized technical credentials.

Experience

  • 8+ Years of Pre-Sales or Technical Sales Experience

    Extensive background in pre-sales engineering, technical consulting, or solutions architecture roles. Proven track record of leading complex customer engagements, articulating technical value to C-level executives, and supporting large deal closure.

  • 2+ Years of People Management Experience

    Demonstrated leadership experience managing technical teams, preferably leading overlay teams (solutions engineers, applied engineers, or technical specialists). Experience with recruitment, performance management, and developing high-performing technical talent.

  • Consumption-Based Model Expertise

    Track record driving not just deal closures but actual activation and consumption of software services. Experience with usage-based or credit-based pricing models and understanding how to align technical delivery with consumption metrics.

  • Enterprise Customer Success Experience

    Experience supporting Fortune 500 or large enterprise customers through complex implementations and driving adoption of sophisticated data technologies. Understanding of enterprise procurement, stakeholder alignment, and change management.

  • Generative AI and Machine Learning Project Leadership

    Hands-on experience leading or directly supporting customer projects involving GenAI implementation, machine learning model deployment, or advanced analytics solutions. Familiarity with common pitfalls and best practices in AI/ML adoption.

  • Cross-Functional Collaboration in Product and Sales Organizations

    Experience working across Sales, Product Management, Customer Success, and Engineering teams. Ability to translate field insights into product requirements and communicate effectively with stakeholders across functional boundaries.

  • Technical Debt and Scalability Assessment

    Ability to evaluate customer architectures, identify technical debt, and recommend refactoring strategies that balance short-term business needs with long-term scalability and maintainability.

Skills

Required

  • Team Leadership and People Management

    Ability to recruit, onboard, develop, and retain high-performing technical professionals. Strong coaching skills with demonstrated ability to provide constructive feedback, identify growth opportunities, and build team culture.

  • Technical Architecture and Solution Design

    Expertise in designing end-to-end data solutions leveraging Generative AI, machine learning, and advanced analytics. Ability to assess scalability, performance, and cost implications of architectural decisions.

  • Executive Communication and Stakeholder Management

    Ability to engage confidently with VP, Director, and C-level stakeholders, translating complex technical concepts into business value and ROI. Strong presentation and negotiation skills.

  • Consumption Activation and Customer Success

    Deep understanding of consumption-based software models and proven ability to drive not just sales closure but actual customer activation, adoption, and expansion of technology solutions.

  • Generative AI and LLM Knowledge

    Practical understanding of Generative AI technologies, large language models, RAG systems, and enterprise AI implementation patterns. Ability to advise customers on AI use case viability and implementation strategies.

  • Machine Learning and Advanced Analytics

    Technical foundation in machine learning, statistical analysis, and advanced analytics techniques. Ability to evaluate ML projects for feasibility and discuss model governance, monitoring, and optimization with data science teams.

  • Sales Enablement and Coaching

    Expertise in developing sales playbooks, coaching technical teams through complex engagements, and providing real-time guidance on customer interactions to maximize deal success and customer outcomes.

  • Feedback Synthesis and Product Collaboration

    Skill in aggregating customer feedback, identifying patterns and themes, and communicating insights to product and leadership teams in a structured and actionable way.

  • Cloud Data Platform Expertise

    Deep knowledge of Snowflake's capabilities, pricing models, and ecosystem. Understanding of how Snowflake competes in the market and how to position its AI/ML capabilities against alternative platforms.

  • Problem-Solving and Technical Analysis

    Strong analytical and problem-solving abilities with comfort diving deep into technical issues, debugging customer problems, and recommending solutions that balance performance, cost, and complexity.

  • Adaptability and AI-Native Thinking

    Comfort with rapidly evolving AI/ML technology landscape and willingness to continuously learn emerging capabilities. Ability to experiment with new tools and methodologies and integrate them into team practices and customer solutions.

  • Strategic Thinking and Business Acumen

    Ability to understand business objectives beyond technical requirements, identify strategic opportunities, and align technical delivery with broader organizational and customer business goals.

  • Data Security and Compliance Awareness

    Understanding of data security best practices, compliance requirements (GDPR, SOC 2, HIPAA, etc.), and ability to guide customers on secure implementation of data and AI solutions.

Preferred

  • Data Warehousing and ETL/ELT Experience

    Nice to have

    Hands-on experience with data warehousing architectures, ETL/ELT pipeline design and optimization, and data lake implementation patterns. Familiarity with tools like dbt, Airflow, or similar data orchestration platforms.

  • Marketing and Go-to-Market Collaboration

    Nice to have

    Experience collaborating with marketing teams to develop customer case studies, technical content, and messaging. Ability to translate technical achievements into compelling customer narratives and marketing assets.

  • Competitive Market Analysis

    Nice to have

    Experience researching competitive data platforms, analytics solutions, and AI/ML offerings. Ability to articulate Snowflake's competitive advantages in the data cloud market.

  • Customer Reference and Advocacy Program Management

    Nice to have

    Experience building and managing customer reference programs, coordinating case studies, and leveraging satisfied customers for sales enablement and brand advocacy.

  • Startup or High-Growth Environment Experience

    Nice to have

    Background scaling technical teams or departments within fast-growing companies, with comfort navigating rapid organizational change, unclear requirements, and dynamic market conditions.

  • Open Source and Developer Community Engagement

    Nice to have

    Active participation in technical communities, open source contributions, or developer advocacy. Ability to engage with developer audiences and build community around technical initiatives.

  • Vertical-Specific Domain Expertise

    Nice to have

    Deep domain knowledge in financial services, healthcare, retail, manufacturing, or other industries heavily investing in AI/ML and advanced analytics. Understanding of industry-specific use cases and compliance requirements.

  • Certifications in Cloud or AI/ML

    Nice to have

    Professional certifications such as AWS Certified Machine Learning Specialty, Google Cloud Professional Data Engineer, Azure Data Scientist Associate, or similar cloud and AI/ML credentials.

  • Prior Snowflake Experience

    Nice to have

    Previous experience working with Snowflake as a customer, partner, or employee. Familiarity with Snowflake's product roadmap, ecosystem, and market positioning.

Compensation

Pay and benefits.

Base·USD 207,000 – 271,688

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.

At Snowflake, we empower both enterprises and individuals to reach their full potential. Our culture prioritizes impact, innovation, and collaboration, making Snowflake the ideal place to build ambitious projects, execute quickly, and advance technology — and your career — to the next level.

The Role

We are seeking a Manager, Applied Field Engineering - AI/ML Product Specialists to lead a high-performing team of Applied Field Engineers within the Applied Field Engineering organization.

In this hands-on leadership role, you will manage a team of Applied Field Engineers who specialize in Generative AI, Machine Learning, and Advanced Analytics. You will be responsible for coaching your team through technical sales engagements, driving execution excellence, and ensuring customers successfully activate and consume Snowflake's AI/ML capabilities. You will translate team-level insights into feedback that shapes broader strategy, working closely with your manager and cross-functional partners to align execution with organizational priorities.

Responsibilities & Focus Areas:

Technical Execution & Consumption Activation:

  • Drive team performance toward Consumption Activation — ensuring customers successfully move workloads into production and realize contracted credit value

  • Coach AFEs on technical sales engagement best practices, helping them identify and prioritize high-impact opportunities

  • Review and provide hands-on guidance on customer architectures to prevent technical debt and ensure scalability

  • Actively participate in customer engagements as a player/coach, modeling excellence in architectural discussions and executive-level conversations

Product Feedback & Cross-Functional Collaboration:

  • Aggregate and communicate field insights to senior leadership; surface recurring product gaps and customer blockers to inform roadmap discussions

  • Partner with Sales leadership to ensure technical resources are aligned to regional pipeline and key account priorities

  • Participate in Communities of Practice and support knowledge-sharing initiatives across the team

Team Leadership & Development:

  • Recruit, onboard, and develop a team of Applied Field Engineers, with a focus on technical growth and performance management

  • Build a culture of Technical Sales excellence where AFEs serve as trusted advisors to customers throughout the sales and post-sales lifecycle

  • Conduct regular 1:1s, provide ongoing feedback, and support career development for direct reports

On day one we will expect you to have:

  • 8+ years of industry experience in a pre-sales, technical sales, or technical consulting capacity

  • 2+ years of people management experience, preferably leading technical overlay or specialist teams

  • Experience with Consumption-based models: Ability to drive not just deal closures, but actual activation and usage of software services

  • Technical credibility: Hands-on depth in at least one of the following: GenAI/LLMs, Machine Learning, Data Engineering, or Cloud Data Architecture

  • Communication skills: Ability to engage confidently with VP and Director-level stakeholders and translate technical value into business outcomes

  • Education: University degree in computer science, engineering, mathematics, or related fields (or equivalent experience)

About Our Team

Our Applied Field Engineering team consists of highly skilled and experienced technical specialists. We are passionate about client and internal stakeholder success, ensuring data is accessible, usable, and valuable to everyone.

Why Snowflake

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.

Every Snowflake employee is expected to follow the company’s confidentiality and security standards for handling sensitive data. Snowflake employees must abide by the company’s data security plan as an essential part of their duties. It is every employee's duty to keep customer information secure and confidential.

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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