# Software Engineer Intern AI/ML - Warsaw (2027)
**Company:** [Snowflake](https://scaleengineer.com/companies/snowflake)
Join Snowflake's AI-focused R&D team as a Software Engineering Intern in Warsaw to contribute to cutting-edge AI-powered products like Snowflake Intelligence. You'll design and implement ML pipelines, optimize model serving infrastructure, and collaborate with world-class AI researchers while developing production-grade systems that power large-scale data processing and inference. This full-time, 4-12 month paid internship requires strong Python, Go, or Java programming skills, ML framework experience, and a deep understanding of distributed systems and model deployment.
**Role:** ML Engineer
**Seniority:** Intern
**Locations:** PL-Warsaw-Lixa C
**Salary:** 6200–8500 PLN
[Apply](https://jobs.ashbyhq.com/snowflake/418a8f32-1658-47d9-9450-ed3a6e947215)
Canonical: https://scaleengineer.com/jobs/snowflake/software-engineer-intern-ai-ml-warsaw-2027
---
## Responsibilities

- Design and Implement AI-Powered Systems: Architect and build scalable AI-powered systems and infrastructure for large-scale data processing and inference on the Snowflake platform. Develop systems that handle real-world ML workloads with high throughput and low latency requirements. Work closely with infrastructure teams to ensure systems meet enterprise-grade reliability and performance standards.
- Develop and Optimize ML Pipelines: Create robust, scalable, and efficient machine learning pipelines spanning the entire lifecycle from model training to deployment and monitoring. Implement data preprocessing, feature engineering, and model evaluation workflows. Optimize pipeline performance for cost efficiency, throughput, and latency in distributed cloud environments.
- Collaborate on Production ML Integration: Partner with AI researchers and product teams to translate novel ML research methodologies into production-grade implementations. Participate in design discussions to bridge the gap between research prototypes and scalable production systems. Ensure ML models are properly versioned, tested, and monitored in production environments.
- Optimize Model Serving and Inference: Implement efficient model serving solutions that minimize latency and computational costs in distributed environments. Optimize inference infrastructure for different deployment scenarios, including batch processing and real-time serving. Conduct performance profiling and tuning to maximize throughput while maintaining accuracy.
- Maintain High-Quality, Scalable Codebases: Contribute to production codebases with emphasis on clean, efficient, and well-documented code. Implement comprehensive unit and integration tests, maintain detailed technical documentation, and establish robust CI/CD pipelines. Conduct and participate in code reviews to enforce engineering best practices across the team.
- Participate in Code Reviews and Design Discussions: Actively engage in cross-team code reviews and architectural design sessions to ensure adherence to software engineering best practices. Provide constructive feedback on technical implementations and participate in decision-making processes for system design. Share learnings and contribute to the team's collective knowledge base.

## Requirements

### education

- {"name":"Active University Enrollment","description":"Must be actively enrolled in an accredited college or university program in computer science, machine learning, electrical engineering, mathematics, physics, or related quantitative field during the entire duration of the internship. This is a mandatory requirement for all applicants."}
- {"name":"Strong Quantitative Foundation","description":"Coursework in mathematics, linear algebra, statistics, and algorithms strengthens candidacy. Formal education in machine learning, deep learning, or artificial intelligence is highly valued but not mandatory. Academic rigor in STEM subjects demonstrates capability for technical problem-solving."}

### technical

- {"name":"Strong Programming Foundation","description":"Demonstrated proficiency in at least one of Python, Go, or Java with proven ability to write clean, efficient, and scalable code. Python is particularly valuable given its dominance in ML ecosystems. Experience with multiple programming languages is a significant advantage."}
- {"name":"ML Framework Expertise","description":"Hands-on experience with modern ML frameworks such as PyTorch or TensorFlow. Practical knowledge of building, training, and evaluating machine learning models. Familiarity with framework-specific best practices for optimization and deployment."}
- {"name":"Distributed Data Processing Systems","description":"Understanding of Apache Spark, Snowpark, or similar distributed processing systems used for large-scale data manipulation. Knowledge of distributed computing concepts including partitioning, shuffling, and optimization strategies. Experience with big data pipelines is highly valuable."}
- {"name":"Distributed Systems and Infrastructure","description":"Solid understanding of distributed systems concepts including consistency models, fault tolerance, and scalability patterns. Familiarity with model serving infrastructure, inference optimization, and latency reduction techniques. Knowledge of containerization technologies like Docker and orchestration platforms is beneficial."}
- {"name":"Software Development Lifecycle","description":"Prior exposure to professional software development practices including version control (Git), testing methodologies (unit and integration testing), code review processes, and CI/CD pipelines. Understanding of software design patterns and architectural principles. Commitment to applying best practices in production environments."}

### experience

- {"name":"Software Development Projects","description":"Portfolio of academic or personal projects demonstrating end-to-end software development capability. Contributions to open-source projects showcasing real-world coding practices. Prior internships or professional experience in software development is highly advantageous."}
- {"name":"ML/AI Project Experience","description":"Completed coursework projects, research work, or personal projects involving machine learning model development, training, and evaluation. Experience building and deploying ML models in any context. Participation in ML competitions or hackathons demonstrates practical expertise."}
- {"name":"Team Collaboration and Communication","description":"Demonstrated ability to work effectively in team environments through group projects or collaborative work. Strong communication skills evidenced by presenting technical work to diverse audiences. Ability to articulate complex technical concepts to both technical and non-technical stakeholders."}

## Skills

### required

- {"name":"Python Programming","description":"Production-grade Python development with deep understanding of language idioms, performance optimization, and ecosystem tools. Proficiency with Python's scientific computing stack (NumPy, Pandas, SciPy) and ML libraries is essential for this role."}
- {"name":"Machine Learning Fundamentals","description":"Solid grasp of ML concepts including supervised learning, unsupervised learning, neural networks, model evaluation metrics, and overfitting/underfitting. Understanding of different model architectures and when to apply each approach."}
- {"name":"Data Processing at Scale","description":"Hands-on experience with distributed data processing frameworks. Understanding of how to design data pipelines that efficiently handle large datasets. Knowledge of common data processing patterns and optimization techniques in distributed environments."}
- {"name":"System Design Thinking","description":"Ability to think about scalability, reliability, and efficiency when designing systems. Understanding of trade-offs between latency, throughput, and resource utilization. Experience considering operational concerns in initial design phases."}
- {"name":"Clean Code and Testing","description":"Commitment to writing readable, maintainable code following established conventions. Experience writing unit tests and understanding the importance of comprehensive test coverage. Familiarity with testing frameworks and CI/CD integration."}

### preferred

- {"name":"Go Language Experience","description":"Experience with Go for building efficient, concurrent systems. Go's performance characteristics and simplicity make it valuable for infrastructure and serving applications. Not required but advantageous for infrastructure-focused ML work."}
- {"name":"Java for Production Systems","description":"JVM ecosystem expertise for building production-grade systems. Understanding of Java's concurrency model, performance characteristics, and widely-used frameworks. Beneficial for larger-scale distributed system development."}
- {"name":"Model Serving and Deployment","description":"Experience with serving ML models in production using frameworks like TensorFlow Serving, TorchServe, or custom solutions. Understanding of containerization, versioning, and monitoring for deployed models. Knowledge of edge vs. cloud serving trade-offs."}
- {"name":"Cloud Platform Proficiency","description":"Familiarity with cloud computing platforms (AWS, Google Cloud, Azure) and their ML services. Understanding of cloud infrastructure concepts, resource management, and cost optimization. Experience with cloud-native ML platforms."}
- {"name":"Snowflake Ecosystem Knowledge","description":"Prior experience with Snowflake data warehouse or Snowpark for data processing. Understanding of how Snowflake's architecture enables analytics and ML workloads. Knowledge of Snowflake Intelligence features provides additional context."}
- {"name":"Research Paper Implementation","description":"Experience translating machine learning research papers into working implementations. Ability to understand mathematical notation and convert academic concepts into production code. Demonstrates bridge between research and engineering."}
- {"name":"DevOps and Infrastructure Tools","description":"Familiarity with Docker, Kubernetes, or similar containerization and orchestration technologies. Understanding of logging, monitoring, and alerting systems. Experience with IaC (Infrastructure as Code) concepts beneficial for infrastructure management."}

## Tech stack

### tools

### others

### databases

### languages

### frameworks

## Benefits

### benefits

- {"name":"Structured Mentorship Program","description":"Access to dedicated technical mentors from Snowflake's AI R&D team with regular check-ins and constructive feedback. Structured learning objectives and growth planning throughout the internship duration. Guidance from world-class ML engineers and researchers working on cutting-edge AI problems."}
- {"name":"Full Team Integration","description":"Treated as a full-fledged member of the Snowflake AI R&D team, not as a peripheral contributor. Participation in team meetings, design discussions, and collaborative problem-solving sessions. Access to same tools, resources, and information as permanent team members."}
- {"name":"Hands-On AI/ML Project Experience","description":"Opportunity to contribute directly to Snowflake Intelligence and other production AI systems. Work on real-world ML challenges at enterprise scale. Direct exposure to building AI-powered systems that serve thousands of customers worldwide."}
- {"name":"Collaboration with AI Researchers","description":"Work alongside world-class AI researchers and ML engineers engaged in groundbreaking research. Learn state-of-the-art techniques and approaches directly from domain experts. Opportunity to contribute to research initiatives with real-world impact."}
- {"name":"Career Development Opportunities","description":"Strong possibility of transitioning to a full-time position following the internship. Access to Snowflake's career development resources and mentorship. Potential for additional internship opportunities with other teams, enabling career exploration."}
- {"name":"Flexible Duration and Schedule","description":"Flexible internship length from 4 months (minimum) to 12 months (maximum), accommodating academic calendars and personal circumstances. Ability to adjust commitment based on academic and personal needs. Structure allows for both continuous and semester-based engagement."}
- {"name":"Remote-Friendly Work Environment","description":"Access to Snowflake's Warsaw office with collaborative space for team interaction. Ability to work on cutting-edge technology and collaborative tools that enable both in-person and remote contributions. Modern technical infrastructure and development environment."}

## Compensation

- **max:** 8500
- **min:** 6200
- **currency:** PLN
- **stockOptions:** false

## 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.

  
We’re at the forefront of the AI revolution, building the world’s leading data and applications platform. At Snowflake, our "get it done" culture empowers everyone to innovate, create impactful work, and thrive in a highly collaborative environment.

We are looking for a talented and motivated Software Engineering Intern to join our AI-focused R&D team. In this role, you will contribute to cutting-edge AI-powered products, such as [Snowflake Intelligence](https://www.snowflake.com/en/product/snowflake-intelligence/), which helps business users extract insights from large-scale data. Your work will be directly integrated into the Snowflake platform, driving AI innovation in data applications.  

**WHAT WE OFFER:**

* Paid, full-time internship within a dynamic AI-driven R&D team
* Collaboration with world-class experts on groundbreaking AI/ML research projects
* Direct mentorship and structured feedback, including regular check-ins and a final evaluation
* Integration as a full-fledged teammate of the Snowflake AI R&D Team
* Post-internship career opportunities (full-time and/or additional internships)

**WHAT WE EXPECT:**

* Must be actively enrolled in an accredited college/university program during the time of the internship
* Strong programming skills in at least one of: Python, Go, or Java
* Familiarity with ML frameworks (e.g., PyTorch, TensorFlow) and data processing systems (e.g., Spark, Snowpark).
* Understanding of distributed systems, model serving, or inference infrastructure.
* Proven ability to write clean, efficient, and scalable code.
* Prior exposure to the software development lifecycle, with a commitment to applying best practices
* Ability to work independently while effectively collaborating within a team
* Strong communication skills to present insights and recommendations to both technical and non-technical audiences
* Duration: Minimum 4 months (recommended 6 months, up to 12 months supported)  

**WHAT YOU WILL LEARN/GAIN:**
* Design, implement, and maintain AI-powered systems and infrastructure for large-scale data processing and inference.
* Collaborate with AI researchers and product teams to bring novel ML methods into production.
* Develop robust, scalable, and efficient ML pipelines, from model training to deployment.
* Optimize model serving, latency, and cost efficiency in distributed environments.
* Contribute to high-quality, maintainable codebases, including testing, documentation, and CI/CD.

Participate in code reviews and cross-team design discussions to ensure best engineering practices.  

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)