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Senior Machine Learning Engineer (LLMs)

 

NOTICE: ONLINE RECRUITMENT PROCESS

LOCATION: REMOTELY FROM POLAND

 

SALARY: 26 000 - 35 000 PLN gross/monthly

 

ROLE OVERVIEW

At Brainly, we are on a mission to revolutionize learning through AI, helping millions of students worldwide gain confidence in their educational journey. As part of our AI-driven vision, we are committed to building cutting-edge solutions that enhance accuracy, reliability, and effectiveness of our AI-powered platform.

Benchmarking of AI Models (BEAM) Team plays a crucial role in ensuring high-quality performance of Brainly’s AI solutions. We are focused on designing and implementing robust evaluation pipelines and incremental improvements of AI solutions for real-world educational use cases. By developing comprehensive benchmarks, scalable evaluation pipelines, and iterative AI improvements, we ensure that Brainly’s AI components continually evolve to meet the highest standards of accuracy and effectiveness.

The Role

This is a highly technical individual contributor role within the Benchmarking of AI Models (BEAM) Team, working closely with Senior ML engineers, Data Scientists and AI Data Analysts to ensure seamless deployment and evaluation of AI models at scale. As a Machine Learning Engineer, you will be responsible for AI models deployment, creation and maintenance of scalable and reliable infrastructure and supporting the transition of research code into production-ready solutions. Your work will involve design reviews, best practice implementation, and rigorous code evaluation to ensure stability and efficiency of AI deployments.

Are you motivated to learn quickly and grow in the areas required to succeed in this job? Are you passionate about automating workflows? Do you take ownership of problems and challenges from beginning to end? Do you maintain a positive attitude and a willingness to tackle challenges and complex problems? Do you have a team-player mindset and strong communication skills? Are you highly self-organized? If you answered yes to these questions, you might just be the perfect candidate for this role!

The ideal candidate is a skilled ML Engineer with a passion for AI-driven innovation, cloud infrastructure, and scaling machine learning models to real-world applications.

WHAT YOU'LL DO

  • Operationalization of Machine Learning Models

    • Orchestration of the entire ML model lifecycle, from development and deployment to monitoring, maintenance, and optimization ensuring scalability, efficiency, and reliability.

    • Implementation of automated workflows for model retraining, versioning, and performance tracking to ensure long-term stability.

    • Transformation of Machine Learning artifacts into production systems and services maintaining robust integration with existing engineering infrastructure.

  • Tooling, Infrastructure & Experimentation Support

    • Design and implementation of tools, frameworks, and infrastructure to enhance efficiency of Data Scientists and other stakeholders simplifying areas such as model training and evaluation, data annotation, and processing. 

    • Working with large-scale datasets in structured and ad-hoc exploratory setups to support both creation of well-organized data pipelines and rapid experimentation and prototyping.

    • Supporting Technical Lead and Data Scientists in refactoring and optimizing research code, ensuring high-quality, reusability, and scalability of delivered solutions bridging the gap between AI experimentation and real-world deployment.

  •  Continuous Learning

    • Staying up to date with cutting-edge advancements in AI technology, including state-of-the-art models, algorithms, tools, and frameworks (both models/algorithms and tools/libraries/SaaS/APIs, etc.). Exploring opportunities to incorporate new methodologies, libraries, and services that enhance Brainly’s AI capabilities.

WHAT IS REQUIRED

  • 5+ years experience with deployment and maintenance of Machine Learning models in production.

  • Experience in deploying and maintaining Deep Learning models, particularly Large Language Models (LLMs).

  • Strong command of writing production-level code in Python, with a focus on best engineering practices, in particular for training & deploying models.

  • PyData stack along with quick frontend frameworks e.g. streamlit.

  • Proven expertise in Cloud Computing (preferably AWS and services like IAM, EC2, S3, ECR, EKS, Redshift, Athena, Glue, Lambda, SecretManager) for storage, data pipelines, ML pipelines, and ML deployment.

  • Machine Learning frameworks such as: Tensorflow, PyTorch, JAX, scikit-learn, Transformers (HuggingFace).

  • Proven track record of development of data and machine learning pipelines.

  • Knowledge of Linux/Unix system, shell scripting.

  • Parallel computing (multi-processing, async, GPUs, types of AI parallelism).

  • Culture of DevOps and high-quality software standards.

  • Fluency in English.

WHAT IS PREFERRED

  • An academic degree in STEM (science, technology, engineering, mathematics) or a related field.

  • Hands-on experience with large-scale serving of ML models (millions of requests/day).

  • Hands-on experience with Kubernetes (deployment management, package manager e.g. Helm) and microservices.

  • Modern Python tools (e.g. ruff, uv, tox, pre-commit).

  • CI/CD (e.g. GitHub Actions, AWS CodePipeline).

  • IaaC frameworks  (Terraform, CloudFormation, Pulumi).

  • Familiarity with basics in Data Engineering (e.g. SQL and NoSQL, data streaming, Apache Spark, Snowflake).

  • Modern model serving frameworks (torchserve, NVIDIA Triton).

  • Familiar with agile development and lean principles.

WHAT WILL BLOW OUR MINDS

  • Experience with Flyte.

  • Knowledge of Golang.

WHAT YOU GET BY JOINING BRAINLY

  • We want to see you grow along with us – you will have 800$ per year for personal development, extra time for attending conferences and workshops, and unlimited access to an online learning platform (courses from Coursera, Udacity, Udemy, Harvard ManageMentor, Busuu, and many others!)

  • Health is important, which is why at Brainly, we fully cover private health & dental care packages for you and your family and provide you with a sport card (Multisport Plus) 

  • You will also get an access to online individual psychological consultations with professionals in English, Polish & Ukrainian via the Mental Health Helpline

  • Your personal concierge AskHenry will support you in your daily duties, eg. planning your dream vacation

  • You can join internal communities and contribute to charity, diversity and inclusion initiatives, take part in great internal events or represent Brainly at conferences or meet-ups

  • We also provide stock options

 

By sending us your application you agree that Brainly sp. z o.o. will process your personal data to participate in this recruitment process. If you want to know more about how Brainly processes your personal data please click here.

 

ABOUT BRAINLY

Brainly is the #1 AI education tool in the world, with a vision to give every student in the world access to personalized learning, no matter their background or resources. 

Powered by its full-service AI Learning Companion™, Brainly is relied upon by millions of students, parents and teachers every day for personalized, on-demand academic assistance. The platform provides world-class homework help, test prep and tutoring that is verified for accuracy and customized to each student based on their learning style. 

Founded in 2009, Brainly operates in the US, Europe, Asia and Latin America, and is backed by Prosus, Point Nine Capital, General Catalyst, Runa Capital, Learn Capital and Kulczyk Investments.

Learn more at www.brainly.com.

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What You Should Know About Senior Machine Learning Engineer (LLMs), Brainly

Are you ready to make a significant impact as a Senior Machine Learning Engineer at Brainly? Based remotely from Poland, you'll be instrumental in our mission to transform learning through AI. With a salary range of 26,000 - 35,000 PLN gross monthly, this role invites you to join our dedicated Benchmarking of AI Models (BEAM) Team, where you'll ensure the quality and reliability of our AI-driven educational solutions. You'll dive deep into the operationalization of machine learning models, orchestrating their lifecycle from development to deployment. By implementing automated workflows and designing efficient tools and infrastructure, your contributions will enhance the efficiency of our data scientists and the overall user experience for millions of students worldwide. This role calls for a collaboration with senior-level engineers, data scientists, and analysts, allowing you to engage in code reviews and best practices to ensure that our AI solutions are not just operational but outstanding. Brainly is looking for someone passionate about automating processes and embracing continuous learning, all while maintaining strong communication skills. If you are skilled in deploying and maintaining Large Language Models, proficient in Python, and have a keen understanding of cloud computing, especially with AWS, we would love to meet you! Come join us in shaping the future of education through AI!

Frequently Asked Questions (FAQs) for Senior Machine Learning Engineer (LLMs) Role at Brainly
What responsibilities does a Senior Machine Learning Engineer at Brainly have?

As a Senior Machine Learning Engineer at Brainly, you will be responsible for the entire lifecycle of machine learning models, including their deployment, monitoring, maintenance, and optimization. You'll work closely with the BEAM Team to build robust infrastructure and automated workflows that ensure the reliability and efficiency of our AI solutions for educational use. You'll also manage large datasets and support Data Scientists in making effective use of machine learning methods.

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What qualifications are required for the Senior Machine Learning Engineer position at Brainly?

To qualify for the Senior Machine Learning Engineer position at Brainly, candidates should have at least 5 years of experience with deployment and maintenance of machine learning models, especially Large Language Models. A strong command of production-level Python is essential, and familiarity with cloud services like AWS, along with ML frameworks such as TensorFlow and PyTorch, is highly preferred.

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What skills are essential for success as a Senior Machine Learning Engineer at Brainly?

Success as a Senior Machine Learning Engineer at Brainly requires a blend of technical and soft skills. You should possess a solid understanding of automating machine learning workflows, designing scalable infrastructures, and integrating machine learning artifacts into production systems. Additionally, strong communication skills, a team-player attitude, and a passion for continuous learning are crucial for thriving in a collaborative environment focused on AI-driven solutions.

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How does Brainly support the career development of its Senior Machine Learning Engineers?

At Brainly, career development is a priority. As a Senior Machine Learning Engineer, you’ll receive an annual allowance of $800 for personal development and have access to unlimited online learning courses from various platforms. We also encourage participation in conferences and workshops, enabling you to stay up to date with the latest advancements in AI technology.

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What benefits come with the Senior Machine Learning Engineer position at Brainly?

The benefits of the Senior Machine Learning Engineer position at Brainly are extensive. Alongside a competitive salary, you will enjoy fully covered private health and dental care for you and your family, sports cards for physical wellness, and access to a personal concierge service. Brainly also emphasizes mental health and offers consultations with professionals, ensuring a balanced work-life experience.

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Common Interview Questions for Senior Machine Learning Engineer (LLMs)
Can you explain your experience in deploying and maintaining machine learning models?

When answering this question, detail your experience with various machine learning deployment strategies, specifically illustrating any projects where you've successfully transitioned models from research to production. Highlight your familiarity with CI/CD processes, automated workflows, and monitoring techniques that ensure model performance in a live environment.

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How do you handle challenges when automating machine learning workflows?

Discuss specific instances where you've encountered challenges in automating workflows. Emphasize your analytical approach to identifying bottlenecks and devising practical solutions that streamline processes. Be sure to mention any tools or frameworks you utilized to enhance automation in your projects.

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What practices ensure the scalability and reliability of machine learning models?

Address the fundamental principles such as monitoring, maintaining model performance, and using cloud services like AWS for deploying scalable solutions. Discuss the importance of implementing version control, automated testing, and performance tracking to underpin reliable models in production.

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Can you provide an example of how you refactored research code into production-ready solutions?

Share a specific project where you successfully transformed research code into a production environment. Discuss the techniques you used for refactoring, ensuring code quality, and maintaining reusability while emphasizing the collaborative aspect with team members for effective transitions.

Join Rise to see the full answer
What methods do you use to stay current with advancements in AI technology?

Explain your strategies for continual learning, such as following reputable AI publications, attending workshops, participating in relevant community forums, or using online course platforms. Highlight any recent advancements in AI that you have integrated into your work.

Join Rise to see the full answer
How do you prioritize tasks when managing multiple projects or responsibilities?

Discuss your approach to task management, including how you assess deadlines, project importance, and resource availability. You might mention tools you use for prioritization, or methodologies like Agile to effectively handle multiple responsibilities.

Join Rise to see the full answer
What experience do you have with cloud computing tools in a machine learning context?

Provide specific examples of cloud computing tools you've used in your machine learning projects, focusing on AWS services like S3, EC2, or Lambda. Discuss how these tools facilitated your data processing, deployment, or scaling tasks.

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Describe your experience with both supervised and unsupervised learning.

Elaborate on your hands-on experience with both types of learning. Highlight specific projects where you've successfully applied these techniques, demonstrating the differences in approaches, use cases, and strategies for model evaluation.

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How do you ensure your machine learning models align with business objectives?

Focus on the analytical skills you employ to understand business needs. Discuss how you translate those needs into technical requirements and how you ensure that the models developed not only meet performance metrics but successfully deliver business value.

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What are your experiences with large-scale machine learning deployments?

Discuss your background with deploying machine learning models at scale, emphasizing any challenges faced such as handling high volumes of requests or ensuring low latency in predictions. Share solutions you've implemented to manage those challenges effectively.

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Brainly is the place to learn - for students, by students. We are the world’s largest social learning community bringing middle school and high school students, parents and teachers together to make learning outside the classroom highly engaging, ...

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DATE POSTED
April 7, 2025

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