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Machine Learning Engineer
Сильная вакансия в продуктовой компании с глобальным охватом. Привлекает работа с современным стеком (LLM, Generative AI) и возможность влиять на бизнес-показатели, хотя отсутствие четкой вилки зарплаты в описании является минусом.
Сложность вакансии
Роль требует редкого сочетания навыков: глубокой экспертизы в ML/Data Science и солидного опыта в классической разработке (Python, .NET). Дополнительную сложность придает необходимость работы с LLM и интеграция моделей в продакшн-среду.
Анализ зарплаты
Зарплата в объявлении не указана, но для Senior ML ролей в Украине рыночный диапазон составляет $5000–$7500. Предложение Pearl, вероятно, находится в этих пределах или выше, учитывая американские корни компании.
Сопроводительное письмо
I am writing to express my strong interest in the Machine Learning Engineer position at Pearl. With over 5 years of experience in software engineering and a specialized focus on ML for the past 3 years, I am confident in my ability to contribute to your mission of combining advanced AI with human expertise. My background in building end-to-end ML solutions and my proficiency in Python and SQL align perfectly with the technical requirements of your Machine Learning department.
I am particularly drawn to Pearl's data-driven and innovative culture. I have extensive experience working with LLMs and Generative AI tools, which I understand are key areas of focus for this role. Furthermore, my familiarity with .NET for integration work and my experience in designing robust data pipelines will allow me to seamlessly integrate ML models into your production systems. I am eager to bring my analytical mindset and problem-solving skills to help drive product quality and monetization at JustAnswer.
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Описание вакансии
About Us
Pearl is AI for professional services at global scale—combining advanced AI with verified human expertise to deliver help that’s accurate, accountable, and fast. Since 2003, our network has connected millions of customers with licensed professionals across 196 countries, making real expertise available anytime, anywhere.
Our Values
- Data driven: Data decides, not egos
- Courageous: We take risks and challenge the status quo
- Innovative: We're constantly learning, creating, and adapting
- Lean: We focus on customers, using lean testing to learn how to serve them best
- Humble: Past success is not a guarantee of future success
About the Role
As a Senior Software Engineer in our Machine Learning department, you will take ownership of high-impact initiatives that drive product quality, monetization, and user experience. You’ll apply your expertise in Python, data analysis, and modern AI tools to build, deploy, and optimize ML solutions end-to-end. This is a hands-on technical role that blends data science and engineering to deliver measurable business outcomes. You’ll collaborate closely with cross-functional stakeholders and contribute to evolving areas such as LLMs, Generative AI, and ML infrastructure. The role offers exposure to complex data challenges and the opportunity to shape JustAnswer’s AI-driven capabilities.
What You’ll Do
- Build, train, and deploy ML models that improve product performance and monetization.
- Collaborate with data, product, and engineering teams across multiple internal initiatives.
- Design and implement data pipelines for extraction, transformation, and analysis.
- Apply statistical and machine learning techniques to real-world business problems.
- Work on projects involving LLMs and Generative AI technologies.
- Analyze large datasets to identify trends, insights, and optimization opportunities.
- Integrate ML models into production systems and monitor their performance.
- Communicate findings and recommendations clearly to technical and non-technical stakeholders.
- Experiment with emerging AI tools and methodologies to enhance existing workflows.
- Ensure data quality, reliability, and scalability in all deliverables.
What We’re Looking For
- 3+ years of hands-on experience in ML Engineering or Data Science.
- 5+ years total experience in software or data engineering.
- Strong programming skills in Python.
- Proficiency in SQL for data querying and analysis.
- Solid understanding of data processing, analysis, and visualization.
- Experience with LLMs / Generative AI tools (e.g., OpenAI, Copilot, Cursor).
- Ability to build and deploy ML models end-to-end.
- Strong analytical and problem-solving mindset.
- Experience with .NET for integration work
- Upper-intermediate English proficiency (B2 or higher).
- Familiarity with Databricks or similar data platforms (nice-to-have).
- Exposure to AWS or other cloud-based ML environments (nice-to-have).
Our Commitment to an Inclusive Workplace
We welcome people from all backgrounds who seek the opportunity to help build a future where professional services are readily available to all. If you have curiosity, passion, and a collaborative spirit, come work with us. Pearl is committed to an inclusive workplace. Pearl is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, disability, age, or other legally protected status.
AI Disclosure & Informed Consent
Artificial intelligence (AI) technology may be used during the hiring process to record, transcribe, analyze, and rank interview responses. By submitting your application and participating in the interview process, you acknowledge and consent to the use of AI technology in the hiring process. For more information see our AI Disclosure and Consent Policy.
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Навыки
- Python
- SQL
- Machine Learning
- Data Analysis
- LLM
- Generative AI
- .NET
- Databricks
- AWS
- Data Pipelines
Возможные вопросы на собеседовании
Вакансия предполагает работу с LLM и Generative AI. Важно понять, как кандидат оценивает качество ответов.
Как вы подходите к оценке качества и галлюцинаций в решениях на базе LLM в продакшене?
В описании указано требование к навыкам .NET для интеграции. Это специфично для ML-инженера.
Расскажите о вашем опыте интеграции Python-сервисов или ML-моделей в инфраструктуру на базе .NET.
Роль подразумевает полный цикл: от данных до деплоя.
Опишите ваш самый сложный кейс развертывания ML-модели: с какими проблемами масштабируемости вы столкнулись?
Компания ценит подход, основанный на данных (Data driven).
Приведите пример, когда данные заставили вас изменить архитектурное решение или отказаться от гипотезы, которая казалась верной.
Упоминается работа с Databricks и AWS.
Каков ваш опыт работы с облачными платформами данных и как вы оптимизируете затраты на вычисления при обучении моделей?
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