Software Engineer AI Team

Backend Engineer · Senior · Full Time

PL-Warsaw-Lixa CUSD 180k – 280k5d ago
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Role

What you'll do.

Join Snowflake's Cortex AI Functions Platform team as a Software Engineer to build scalable infrastructure that enables customers to execute AI inference directly from SQL at data warehouse scale. This role focuses on designing and maintaining high-throughput, reliable distributed systems that process large volumes of inference requests while ensuring predictable performance, cost efficiency, and data residency. You'll collaborate with research teams, product partners, and engineering counterparts to architect multi-modal inference capabilities, quality assurance frameworks, and resilient systems that handle failures gracefully.

Responsibilities

  • Architect High-Throughput Distributed Systems: Design and implement production backend infrastructure for the Cortex AI Functions platform that scales to handle massive volumes of concurrent inference requests while maintaining predictable latency and cost characteristics at data warehouse scale.
  • End-to-End Platform Development: Participate in all stages of building and operating the AI functions platform, from initial architecture and design through implementation, testing, deployment, and production support, ensuring reliability and performance across the entire lifecycle.
  • Multi-Modal Inference Architecture: Lead development of new architecture for multi-modal inference capabilities that extend beyond text-based inputs, enabling customers to process diverse data types through SQL-accessible AI functions.
  • Quality Assurance and Risk Mitigation: Design and implement quality gates and testing frameworks that enable confident deployment of new AI functions and models at batch scale, ensuring production stability and customer trust.
  • Failure Detection and Resilience: Build systems for detecting, diagnosing, and recovering from failures in the inference path, implementing observability and monitoring to ensure graceful degradation when dependencies fail.
  • Performance and Cost Optimization: Continuously optimize throughput, reduce inference costs, and improve reliability as customer usage grows, implementing efficient resource management and cost monitoring across the platform.
  • Cross-Functional Collaboration: Work closely with research teams, product management, and engineering partners to understand customer needs, shape product strategy, and translate research innovations into scalable production systems.
  • Technical Mentorship: Mentor less experienced engineers on best practices in distributed systems design, backend development, and production operations, fostering a culture of technical excellence.
  • Complex Problem-Solving: Apply creative thinking to solve ambiguous, complex technical problems inherent to large-scale AI inference platforms, turning loosely defined requirements into scoped, testable designs.

Qualifications

What we look for.

Technical

  • Production Backend or Distributed Systems Development

    4+ years of professional experience building production-grade backend systems or distributed systems in C++, Java, Go, Python, or comparable systems programming language.

  • High-Throughput Service Design

    Demonstrated expertise designing and operating high-throughput, low-latency services with deep understanding of concurrency patterns, backpressure management, handling partial failures, and comprehensive observability.

  • Service and RPC API Design

    Hands-on experience designing and implementing scalable service APIs including REST, gRPC, or other RPC frameworks, with understanding of protocol design, versioning, and compatibility.

  • Distributed Systems Reliability

    Strong background building distributed systems that guarantee high throughput without compromising reliability, including experience with consensus algorithms, fault tolerance, and failure recovery mechanisms.

  • Software Engineering Best Practices

    Proficiency with modern software engineering practices including test-driven development, comprehensive testing strategies, version control systems, CI/CD pipelines, containerization, and agile methodologies.

Education

  • Bachelor's Degree in Technical Field

    Required: BSc in Computer Science, Electrical Engineering, Physics, Mathematics, or equivalent technical discipline demonstrating foundational computer science principles.

  • Advanced Degree

    Plus: Master's degree or PhD in computer science, mathematics, engineering, or related fields providing advanced theoretical knowledge and research experience.

Experience

  • Large-Scale Data Systems

    Strong asset: Experience with SQL engines, query execution systems, distributed query optimization, or other large-scale data platform systems that process terabytes of data efficiently.

  • MLOps and Model Deployment

    Plus: Hands-on experience in the MLOps field, including deploying machine learning models to production, managing model versioning, inference serving, and monitoring model performance at scale.

  • AI/ML Infrastructure

    Plus: Experience working with AI inference platforms, prompt engineering systems, or large language model integration, understanding latency requirements and inference optimization.

Skills

Required

  • Backend Systems Programming

    Expert-level proficiency in one or more systems programming languages such as C++, Java, Go, or Python for building production systems.

  • Distributed Systems Architecture

    Deep understanding of distributed computing principles, including service orchestration, data consistency models, failure modes, and recovery strategies.

  • Concurrency and Performance

    Mastery of concurrent programming patterns, multi-threading, async I/O, and performance optimization techniques for high-throughput systems.

  • System Design and Scalability

    Ability to design systems that scale horizontally and vertically, considering trade-offs between latency, throughput, consistency, and cost.

  • Problem-Solving and Ambiguity Navigation

    Strong analytical skills and ability to tackle open-ended, loosely defined problems, breaking them into concrete, testable designs and implementations.

  • Technical Communication

    Capability to articulate complex technical concepts, design decisions, and results clearly to both technical teams and business leadership.

  • Independent and Collaborative Development

    Ability to produce well-organized solutions independently while thriving in collaborative team environments with cross-functional partners.

Preferred

  • SQL Engine and Query Optimization

    Nice to have

    Experience with SQL query engines, query optimization, execution planning, or other components of modern data warehouse architectures.

  • MLOps and Model Serving

    Nice to have

    Background in deploying and managing machine learning models in production, including model versioning, A/B testing, and inference serving infrastructure.

  • Cloud Platform Experience

    Nice to have

    Hands-on experience with cloud computing platforms like AWS, Azure, or GCP, including containerization, orchestration, and cloud-native architecture patterns.

  • Observability and Monitoring

    Nice to have

    Experience designing comprehensive observability systems using distributed tracing, metrics collection, and logging frameworks for production debugging.

  • Low-Latency Systems

    Nice to have

    Background building ultra-low-latency systems with deep understanding of performance profiling, hardware considerations, and optimization techniques.

Tech stack

Languages

C++JavaGoPython

Frameworks

gRPCREST APIsAsync/Await Frameworks

Databases

SQL DatabasesDistributed CachesMessage Queues

Tools

KubernetesDockerCI/CD PipelinesMonitoring and ObservabilityGit and Version Control

Other

AI Inference PlatformsData Warehouse ArchitectureML Model DeploymentCost Optimization

Compensation

Pay and benefits.

Base·USD 180,000 – 280,000

Equity·Stock options

Benefits

  • Equity Compensation

    Stock options provided as part of the comprehensive compensation package, allowing you to participate in Snowflake's growth as a public company.

  • Innovation-Driven Culture

    Work in a 'get it done' culture that empowers every engineer to innovate on new ideas, create lasting impact, and accelerate their professional growth through meaningful projects.

  • Collaborative Environment

    Join a team that values cross-functional collaboration with research scientists, product managers, and engineering partners, fostering knowledge sharing and technical excellence.

  • Cutting-Edge Technology

    Work on the forefront of the data revolution and AI transformation, building platform capabilities that define the future of enterprise AI and data analytics.

  • Technical Leadership Opportunities

    Opportunities to mentor less experienced engineers and contribute to architectural decisions that shape the long-term product strategy.

  • Scale and Impact

    Impact millions of users by building infrastructure that powers large-scale AI inference at enterprise data warehouse scale.

Process

Interview steps.

  1. 01

    Initial Screening Call

    Brief conversation with a recruiter to understand your background, experience with distributed systems, and alignment with the role's technical requirements.

  2. 02

    Technical Phone Screen

    Discussion of your experience building production backend systems, architectural decisions you've made, and your approach to solving complex distributed systems problems.

  3. 03

    System Design Interview

    In-depth conversation on designing high-throughput, reliable systems. You'll discuss architectural trade-offs, scalability concerns, failure handling, and cost optimization strategies for a distributed AI inference platform.

  4. 04

    Implementation Interview

    Technical coding exercise focused on demonstrating proficiency in your preferred language and ability to implement concurrent, efficient algorithms relevant to backend systems development.

  5. 05

    Behavioral and Values Alignment

    Discussion of your experience working in collaborative environments, mentoring others, navigating ambiguous problems, and how you handle complex technical communication with leadership.

  6. 06

    Leadership Team Meeting

    Conversation with engineering leadership or team members to assess fit with Snowflake's culture, understand the team's vision for the Cortex AI platform, and discuss long-term growth opportunities.

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.

We’re at the forefront of the data revolution, committed to building the world’s greatest data and applications platform. Our ‘get it done’ culture allows everyone at Snowflake to have an equal opportunity to innovate on new ideas, create work with a lasting impact, and excel in a culture of collaboration.

We're looking for a talented and motivated Software Engineer to join the Cortex AI Functions Platform team. AI functions are how Snowflake customers call AI directly from SQL, running models over their own tables without moving data out of the platform. Our team builds the platform that makes this work at data warehouse scale, where a single statement can expand into a large volume of inference requests that must complete predictably, within expected cost, and without compromising the query when a dependency degrades or fails.

Current projects include a new architecture for multi-modal inference, quality gates that let us ship new functions and models with confidence at batch scale, and support for input data beyond plain text. Alongside these, we work continuously on throughput, cost and reliability as customer usage grows, and on making failures in the inference path easier to detect, diagnose and recover from.

For this role, you don’t need to have a research background, including pre-training or fine-tuning models.

 

Your journey as a Software Engineer

Working in our team you will:

  • Think creatively to find optimal solutions to our complex, often ambiguous problems

  • Participate in all stages of building and running the platform behind Snowflake’s AI functions, from design and implementation through rollout and production support

  • Work closely with research teams, as well as product and engineering partners, to understand and shape the short and long-term product development strategy

  • Serve as a mentor to less experienced engineers

Ideal candidate

  • 4+ years of experience building production backend or distributed systems in C++, Java, Go, Python, or a comparable language

  • BSc in a technical field (CS, EE, Physics, Math, etc), MSc/PhD is a plus

  • Experience designing and operating high-throughput services, including handling concurrency, backpressure, partial failures, and observability

  • Experience designing service or RPC APIs, such as REST or gRPC

  • Experience with software engineering best practices (programming, testing, version control, CI/CD, containers, agile development, etc)

  • Experience with building distributed systems that guarantee high throughput without compromising on reliability

  • High levels of curiosity and eager enthusiasm for open-ended problems, and the ability to turn a loosely defined problem into a scoped, testable design

  • Ability to articulate results and complex concepts to leadership

  • Must be able to produce solutions independently in an organized manner, as well as work in a team

  • Experience with SQL engines, query execution, or other large-scale data systems is a strong asset

  • Hands-on experience in the MLOps field (e.g., deploying ML models) is a plus

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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