Machine Learning Engineer
⚲ Warszawa
21 917–27 417 zł brutto / mies.
Wymagania
- Python
- Machine Learning
- NLP
- LLMs
- Vector Embeddings
- Vector Search & Retrieval
- Classification Models
- NER
- Ranking & Similarity Models
- AWS
- GCP
- MLOps
- Model Monitoring
- Model Evaluation Frameworks
- Ground Truth Pipelines
- Data Annotation Workflows
Opis stanowiska
Nasze wymagania:
4-7 years of Software Engineering or Machine Learning Engineering experience
2+ years deploying ML systems into production environments
Strong Python programming skills
Experience with modern ML and AI ecosystems
Hands-on experience with AWS or GCP cloud platforms
Experience building and deploying NLP, ML, embedding, or LLM-based solutions
Understanding of model evaluation, precision/recall, golden datasets, and regression testing
Experience working with large-scale production systems
Strong communication and collaboration skills
Ability to work closely with Data Science, Engineering, and Product teams
Zakres obowiązków:
Build and deploy ML and AI systems serving millions of records daily
Work with NLP, embeddings, vector search, classification, ranking, similarity models, and LLM-powered features
Develop and improve model evaluation frameworks and testing pipelines
Build and maintain vector embedding and retrieval systems
Partner with Data Scientists to improve data quality and ground-truth datasets
Design scalable MLOps solutions including monitoring, observability, versioning, and drift detection
Translate business problems into measurable AI features and outcomes
Collaborate closely with Engineering, Data Science, and Product teams
Help shape the future of AI-powered products and features
Technologies: Python, Machine Learning, NLP, LLMs, Vector Embeddings, Vector Search & Retrieval, Classification Models, NER, Ranking & Similarity Models, AWS and/or GCP, MLOps, Model Monitoring, Model Evaluation Frameworks, Ground Truth Pipelines, Data Annotation Workflows
Oferujemy:
People: work with talented, collaborative, and friendly people who love what they do.
Guidance: utilize our learning platform to fully get the training and tools you'll need to become successful here from your first day with us.
Surprise meal stipends: work from home can't stop the enjoyment of someone else making a meal for you!
Work/life harmony: 26 days vacation, floating and set holidays, wellness allowance, and paid parental leave.
Medical insurance, life insurance, and business travel insurance
Stock options as part of our equity-sharing program.
Comprehensive perks program providing stipends for cell phone and internet, home office setup, mental wellness, professional development and tuition reimbursement, plus occasional company-funded meal opportunities throughout the year.
4-7 years of Software Engineering or Machine Learning Engineering experience
2+ years deploying ML systems into production environments
Strong Python programming skills
Experience with modern ML and AI ecosystems
Hands-on experience with AWS or GCP cloud platforms
Experience building and deploying NLP, ML, embedding, or LLM-based solutions
Understanding of model evaluation, precision/recall, golden datasets, and regression testing
Experience working with large-scale production systems
Strong communication and collaboration skills
Ability to work closely with Data Science, Engineering, and Product teams
Zakres obowiązków:
Build and deploy ML and AI systems serving millions of records daily
Work with NLP, embeddings, vector search, classification, ranking, similarity models, and LLM-powered features
Develop and improve model evaluation frameworks and testing pipelines
Build and maintain vector embedding and retrieval systems
Partner with Data Scientists to improve data quality and ground-truth datasets
Design scalable MLOps solutions including monitoring, observability, versioning, and drift detection
Translate business problems into measurable AI features and outcomes
Collaborate closely with Engineering, Data Science, and Product teams
Help shape the future of AI-powered products and features
Technologies: Python, Machine Learning, NLP, LLMs, Vector Embeddings, Vector Search & Retrieval, Classification Models, NER, Ranking & Similarity Models, AWS and/or GCP, MLOps, Model Monitoring, Model Evaluation Frameworks, Ground Truth Pipelines, Data Annotation Workflows
Oferujemy:
People: work with talented, collaborative, and friendly people who love what they do.
Guidance: utilize our learning platform to fully get the training and tools you'll need to become successful here from your first day with us.
Surprise meal stipends: work from home can't stop the enjoyment of someone else making a meal for you!
Work/life harmony: 26 days vacation, floating and set holidays, wellness allowance, and paid parental leave.
Medical insurance, life insurance, and business travel insurance
Stock options as part of our equity-sharing program.
Comprehensive perks program providing stipends for cell phone and internet, home office setup, mental wellness, professional development and tuition reimbursement, plus occasional company-funded meal opportunities throughout the year.
🔍 Dekoder Ogłoszenia
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4-7 years of Software Engineering or Machine Learning Engineering experience
Może oznaczać, że szukają kogoś z doświadczeniem w obu dziedzinach, lub że doświadczenie w inżynierii oprogramowania jest równie cenne co w ML.
🔴
2+ years deploying ML systems into production environments
Wymagane jest praktyczne doświadczenie w wdrażaniu modeli ML na produkcję, a nie tylko teoretyczna wiedza.
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Help shape the future of AI-powered products and features
Oznacza możliwość wpływu na kierunek rozwoju produktów, ale może też sugerować, że wiele rzeczy jest jeszcze w fazie koncepcji i wymagać będzie proaktywności.
🟡
Experience with modern ML and AI ecosystems
Jest to ogólne stwierdzenie, które może obejmować szeroki zakres technologii i narzędzi, a konkretne oczekiwania mogą być doprecyzowane podczas rozmowy.
🟡
Work with NLP, embeddings, vector search, classification, ranking, similarity models, and LLM-powered features
Zakres obowiązków jest szeroki i obejmuje wiele różnych obszarów ML, co może oznaczać potrzebę wszechstronności lub specjalizacji w jednym z nich.