Senior Machine Learning Engineer
8Bit - Games Industry Recruitment
⚲ Warszawa
25 000 - 32 000 PLN brutto (UoP)
Wymagania
- Machine Learning
Opis stanowiska
Our client, an early-stage AI company, is looking for a Senior Machine Learning Engineer to build and own critical machine learning systems for a proactive AI assistant.
This is a hands-on, high-impact position for an experienced engineer who can take ambiguous problems, design practical solutions, and deliver reliable ML systems at scale. The successful candidate will work across the complete machine learning lifecycle, from data preparation and training to evaluation, inference, production deployment, and continuous improvement.
The role combines strong machine learning expertise with production engineering, technical ownership, and close collaboration with research, product, and engineering teams.
Responsibilities
• Build core machine learning systems that power a proactive, long-horizon AI assistant
• Own ML initiatives end to end, including data preparation, training, evaluation, inference, deployment, and iteration
• Turn research ideas into practical systems that operate reliably in production
• Investigate model failures and system issues using real-world production signals
• Continuously ship, measure results, identify improvements, and refine the system
• Work closely with research, product, and engineering teams to deliver meaningful user impact
• Mentor other machine learning engineers and provide technical guidance through code reviews and strong engineering practices
• Design solutions that account for production constraints such as latency, cost, reliability, efficiency, and safety
Requirements
• Experience building and shipping machine learning systems used by real users
• Strong understanding of how modern machine learning models behave and fail in production environments
• Excellent Python programming skills, experience with PyTorch and/or JAX
• Experience with GPU-based model training and inference systems
• Ability to write reliable, maintainable, production-quality code
• Strong systems thinking and the ability to design solutions beyond experimental scripts or research prototypes
• Ability to work independently, take full ownership, and move complex initiatives through to completion
• Strong problem-solving skills and confidence working with incomplete or ambiguous information
• Clear communication skills and fluent English
WHAT THEY OFFER
• Cash and equity compensation
• Remote-first setup with flexible hours as part of a distributed, global team
• Generous paid time off
• Company laptop provided
• A quick hiring process – 3, occasionally 4, interviews, with fast decisions afterwards
ABOUT THE COMPANY
They’re an early-stage AI company building a proactive assistant aimed at the 5+ billion people currently stuck using non-AI-native tools for everyday things – email, notes, tasks.
The focus is squarely on reliability: long-running workflows, persistent context, and tasks that actually get done, even though the underlying models aren’t fully deterministic.
Stage: Early-stage AI startup
Focus: Proactive AI assistant for everyday productivity
Work mode: Remote-first, distributed team
This is a hands-on, high-impact position for an experienced engineer who can take ambiguous problems, design practical solutions, and deliver reliable ML systems at scale. The successful candidate will work across the complete machine learning lifecycle, from data preparation and training to evaluation, inference, production deployment, and continuous improvement.
The role combines strong machine learning expertise with production engineering, technical ownership, and close collaboration with research, product, and engineering teams.
Responsibilities
• Build core machine learning systems that power a proactive, long-horizon AI assistant
• Own ML initiatives end to end, including data preparation, training, evaluation, inference, deployment, and iteration
• Turn research ideas into practical systems that operate reliably in production
• Investigate model failures and system issues using real-world production signals
• Continuously ship, measure results, identify improvements, and refine the system
• Work closely with research, product, and engineering teams to deliver meaningful user impact
• Mentor other machine learning engineers and provide technical guidance through code reviews and strong engineering practices
• Design solutions that account for production constraints such as latency, cost, reliability, efficiency, and safety
Requirements
• Experience building and shipping machine learning systems used by real users
• Strong understanding of how modern machine learning models behave and fail in production environments
• Excellent Python programming skills, experience with PyTorch and/or JAX
• Experience with GPU-based model training and inference systems
• Ability to write reliable, maintainable, production-quality code
• Strong systems thinking and the ability to design solutions beyond experimental scripts or research prototypes
• Ability to work independently, take full ownership, and move complex initiatives through to completion
• Strong problem-solving skills and confidence working with incomplete or ambiguous information
• Clear communication skills and fluent English
WHAT THEY OFFER
• Cash and equity compensation
• Remote-first setup with flexible hours as part of a distributed, global team
• Generous paid time off
• Company laptop provided
• A quick hiring process – 3, occasionally 4, interviews, with fast decisions afterwards
ABOUT THE COMPANY
They’re an early-stage AI company building a proactive assistant aimed at the 5+ billion people currently stuck using non-AI-native tools for everyday things – email, notes, tasks.
The focus is squarely on reliability: long-running workflows, persistent context, and tasks that actually get done, even though the underlying models aren’t fully deterministic.
Stage: Early-stage AI startup
Focus: Proactive AI assistant for everyday productivity
Work mode: Remote-first, distributed team
🔍 Dekoder Ogłoszenia
🔴
take ambiguous problems, design practical solutions, and deliver reliable ML systems at scale
Oczekuje się, że będziesz samodzielnie definiować i rozwiązywać problemy, które nie są jasno określone, co może oznaczać brak jasnych wytycznych lub dużą niepewność.
🔴
work across the complete machine learning lifecycle, from data preparation and training to evaluation, inference, production deployment, and continuous improvement
Będziesz odpowiedzialny za wszystkie etapy cyklu życia ML, co może oznaczać bardzo szeroki zakres obowiązków i potencjalnie brak specjalizacji.
🔴
Turn research ideas into practical systems that operate reliably in production
Może to oznaczać, że będziesz musiał przekształcać teoretyczne koncepcje w działające rozwiązania, co bywa trudne i czasochłonne, zwłaszcza w nowym środowisku.
🟡
Mentor other machine learning engineers and provide technical guidance through code reviews and strong engineering practices
Oprócz pracy inżynierskiej, będziesz miał obowiązki mentorskie, co może zwiększyć obciążenie pracą.
🔴
early-stage AI company
Firma jest na wczesnym etapie rozwoju, co może oznaczać brak ugruntowanych procesów i potencjalnie większą niestabilność.