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AI Research Engineer

About Us

At Rossum, our mission is to leverage cutting-edge AI technology and an intuitive UI to eliminate needless paperwork, empowering businesses worldwide to move faster, which we believe ultimately contributes to a better world.


Our revolutionary document processing platform helps hundreds of customers automate data extraction and improve communication efficiency every day. By advancing the way companies exchange critical information, we reduce overhead and accelerate business processes on a global scale.


If you’re an AI Research Engineer at the start of your career and looking for a place to grow fast, learn from experienced colleagues, and work on real AI challenges or someone with a few years of experience ready to level up—Rossum could be the perfect place for you. We’re looking for curious, driven talent who want to tackle real-world AI challenges and push boundaries in a dynamic environment. If that sounds like you, let’s chat!

About the Team

At Rossum, we are building a product-oriented applied AI Research team that works closely with a dedicated Tech Lead and Product Manager to ensure our work delivers maximum value to our users.

The team follows its own agile workflow with scientific method practices at its core: focusing on research, experimentation, iterative development, continuous improvement. Our researchers take end-to-end ownership of their work (with mentorship as needed), with the freedom to experiment, innovate, and contribute to shaping the product.

The team collaborates closely with the AI Platform and Annotation teams to bring AI capabilities into our product. All 3 teams are led by one of Rossum co-founders, Tomáš Tunys.

About our Tech Stack

We pride ourselves on building a modern, scalable, and efficient AI pipeline and fostering forward-thinking engineering culture.

While Python is our main programming language, our environment is intentionally diverse to support cutting-edge research:

  • Frameworks & Libraries: PyTorch, PyTorch Lightning, Hugging Face Transformers, TensorFlow, along with good old NumPy, Pandas, and scikit-learn for ML modeling.

  • MLOps: Kubeflow Pipelines for training automation, Kubernetes for scalable deployments, Triton Inference Server, ONNX Runtime, vLLM, and more for flexible inference.

  • Tooling & Monitoring: JupyterLab, IPyWidgets/Holoviz-based debugging tools, Prometheus, Grafana, and EFK stack for observability.

  • Databases & Hosting: PostgreSQL (with pgvector extension for vector similarity), GitLab (code repositories, CI/CD).

You’ll be encouraged to experiment with the latest tools and frameworks to push the boundaries of what our AI platform can do.

About You

We are seeking Junior to Mid-Level AI Research Engineers who have a good foundation in software development and a willingness to learn and apply AI/ML techniques to transform the way the world operates through technology.

Successful candidates typically possess:

  • Programming Skills: Basic to intermediate-level proficiency in Python (or other programming languages) and familiarity with AI/ML frameworks (PyTorch, TensorFlow, etc.), as well as software engineering practices like Git and CI/CD.

  • CS & Math Foundations: Have a good understanding of algorithms, data structures, optimization, probability, and statistics.

  • ML Fundamentals: Familiarity or good understanding of ML fundamentals and the underlying principles (potentially from coursework, internships, or personal projects), and an enthusiasm for deepening these skills.

  • Basic MLOps or Cloud Experience: Any experience with cloud-based environments (AWS/GCP/Azure) or orchestration tools (Kubeflow, Airflow, MLFlow) is a plus, though not strictly required.

  • Ownership & Innovation: Be proactive, curious, and excited to try new approaches – comfortable receiving guidance from senior team members where needed.

  • Communication & Collaboration: General ability and willingness to discuss and share ideas and findings with cross-functional teams and stakeholders.

  • Growth Mindset: Willingness to learn from feedback, adopt best practices, and continuously improve.

  • Passion for AI Frontier: A genuine desire to contribute to impactful AI solutions and become more knowledgeable in machine learning over time.

About the Role

As a Junior to Mid-Level AI Research Engineer, you’ll work on designing and implementing AI/ML solutions to challenging problems, with mentorship from experienced team members. You’ll play an integral role in bringing AI-powered features to production and influencing how we innovate for our users.

More specifically, you will:

  • Contribute to AI projects: Take part in ideation, model prototyping, and deployment under the guidance of senior engineers.

  • Collaborate Cross-functionally: Work closely with Product, AI Platform, and Data Annotation teams to identify use cases and deliver business value.

  • Learn and Grow with our Platform: Stay up-to-date with AI/ML trends; you’ll be encouraged to experiment with new technologies and integrate them into our platform.

  • Focus on Quality & Reliability: Help ensure that the AI solutions are performant, robust, well-monitored, and maintainable.

  • Embrace a Learning Culture: Share findings with teammates, contribute ideas in group discussions, and continuously sharpen your AI/ML/engineering skills.

What we offer  

  • Cutting-Edge AI & Research – Work on state-of-the-art AI solutions, from deep learning to large-scale document processing.

  • End-to-End Impact – Contribute across the full ML lifecycle, from prototyping to production.

  • Collaboration with Experts – Join a highly skilled team solving complex AI challenges.

  • Fast Iteration & Real-World Application – See your research quickly translate into impactful solutions.

Perks & Benefits

  • 5 weeks of vacation + 5 sick days

  • Stock options plan

  • Flexible hours & hybrid work model

  • Extra two weeks of paternity leave

  • High-end laptop & tech setup

  • English & Czech language lessons

  • Multisport card for 2,700+ sports facilities in Czechia

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CEO of Rossum
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Tomas Gogar
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Average salary estimate

$65000 / YEARLY (est.)
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$50000K
$80000K

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What You Should Know About AI Research Engineer, Rossum

As an AI Research Engineer at Rossum in the vibrant city of Prague, you'll have the unique opportunity to dive into the cutting-edge realm of artificial intelligence that directly impacts businesses worldwide. Imagine working on exciting projects that eliminate needless paperwork and empower efficiency through innovative AI technology! Whether you’re just starting your career or already have some experience under your belt, this role is designed for you to grow fast in a collaborative and dynamic environment. At Rossum, our AI Research team is dedicated to improving communication through our revolutionary document processing platform. You will work closely with seasoned experts, engage in exploration and innovation, and contribute significantly to our product's development. Your tasks will involve collaborating cross-functionally, sharing ideas, and taking ownership of AI/ML projects—all while developing your skills in a supportive atmosphere. With a modern tech stack including Python, PyTorch, and various MLOps tools, you’ll find plenty of opportunities to learn and experiment. Plus, with benefits like flexible working hours and generous vacation time, Rossum ensures that work-life balance is prioritized. If you are eager to tackle real-world AI challenges while making a tangible difference, we invite you to join us in shaping the future of AI at Rossum!

Frequently Asked Questions (FAQs) for AI Research Engineer Role at Rossum
What responsibilities does an AI Research Engineer at Rossum have?

As an AI Research Engineer at Rossum, you will engage in designing and implementing AI/ML solutions for various challenging problems. Your responsibilities will include contributing to AI projects from ideation through to deployment, collaborating with cross-functional teams to identify and deliver business value, and staying up-to-date with the latest AI/ML trends. Overall, your work will have a significant impact on bringing AI features to production and enhancing our platform.

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What qualifications are required for the AI Research Engineer position at Rossum?

To thrive as an AI Research Engineer at Rossum, you should possess a basic to intermediate level of proficiency in Python or other programming languages and be familiar with AI/ML frameworks like PyTorch or TensorFlow. A solid foundation in computer science and mathematics, particularly in algorithms, optimization, and statistics, is crucial. Additionally, keen communication skills and a willingness to learn and collaborate are essential for success in this role.

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What programming languages and frameworks are used by AI Research Engineers at Rossum?

At Rossum, AI Research Engineers primarily work with Python, leveraging various frameworks and libraries, including PyTorch, TensorFlow, and Hugging Face Transformers. The diverse tech stack also includes tools for MLOps like Kubeflow and Kubernetes, enabling the team to build efficient, scalable AI solutions. This rich environment supports your growth and experimentation with cutting-edge technologies.

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How does Rossum support the growth and development of AI Research Engineers?

Rossum fosters a learning culture by encouraging AI Research Engineers to pursue continuous improvement through mentorship, collaboration, and hands-on experience. You'll have the chance to experiment with new technologies, share your findings, and participate in discussions, allowing you to deepen your expertise and engagement with AI/ML methodologies.

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What benefits can I expect as an AI Research Engineer at Rossum?

As part of the Rossum team, you can look forward to an array of enticing benefits including five weeks of vacation, five sick days, stock options, and flexible working hours. Additionally, Rossum offers a spacious hybrid work model, language lessons, a multisport card, and a high-end laptop setup, ensuring you have everything you need to thrive both personally and professionally.

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Common Interview Questions for AI Research Engineer
Can you describe a project where you utilized AI or machine learning?

In answering this question, provide a specific example highlighting your role in the project, the tools you used, the challenges faced, and the results achieved. Focus on your direct contributions and the impact your work had, which shows your ability to apply theoretical knowledge in practical scenarios.

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How do you approach learning new AI technologies or frameworks?

Discuss your method for staying updated with the latest advancements in AI, whether through online courses, workshops, or personal projects. Highlight your enthusiasm for continuous learning and your proactive approach to acquiring new skills relevant to your work as an AI Research Engineer.

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What key considerations do you take into account when developing machine learning models?

When developing machine learning models, it's essential to consider data quality, model selection, overfitting, and validation strategies. Explain how you balance these factors to optimize model performance and reliability. Providing examples from previous experiences can strengthen your answer.

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Explain a time you faced an obstacle during an AI project. How did you overcome it?

This question offers an opportunity to showcase your problem-solving skills. Choose a specific obstacle, detail the steps you took to handle it, and describe the outcome. Emphasize your analytical skills and ability to work under pressure, which are crucial for an AI Research Engineer.

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How do you ensure your AI solutions are scalable and maintainable?

Explain your understanding of best practices in software engineering and MLOps. Discuss methods like code reviews, documentation, continuous integration, and deployment strategies that help ensure AI solutions are robust and easily scalable.

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What does cross-functional collaboration mean to you in an AI role?

Talk about the importance of collaborating with different teams, such as product managers and data analysts, to gather insights and share knowledge. Discuss past experiences where effective communication led to better outcomes and value for the project.

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How do you measure the success of an AI project?

Success can be measured through several key performance indicators (KPIs) such as accuracy, efficiency, and user satisfaction. Discuss how you define success based on project goals and stakeholder expectations, providing examples of any past projects you've measured.

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What AI techniques do you find most exciting right now?

Be prepared to discuss current trends and emerging techniques in AI that excite you, such as transformer models, reinforcement learning, or automated machine learning. Show your passion for the field and your willingness to adapt to new advancements.

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Describe your experience with data preprocessing and cleaning.

Explain your approach to managing raw data, including methods you utilize for data cleaning like handling missing values, outlier detection, and normalization. Providing a practical example of how this has improved model performance in the past can strengthen your response.

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What role do you see AI playing in future business processes?

Discuss the transformative power of AI in improving efficiencies, decision-making, and customer experiences within various business sectors. This question enables you to demonstrate your vision for the future and your commitment to leveraging AI's potential in meaningful ways.

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B2B communication is one of the most complicated processes in modern society, because businesses interact by exchanging myriads of document types in different forms by using many different channels. Our goals are: -to create effortless collabora...

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DATE POSTED
April 3, 2025

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