Member of Technical Staff, Machine Learning
8Bit - Games Industry Recruitment
⚲ Warszawa
18 000 - 25 000 PLN brutto (UoP)
Wymagania
- Machine Learning
Opis stanowiska
Our client is looking for a Member of Technical Staff, Machine Learning to help build core machine learning components for a proactive AI assistant.
The successful candidate will work on real production systems from day one, gaining hands-on experience with how large-scale machine learning behaves outside research environments.
This role is suited to an engineer who wants to develop strong systems judgment by shipping, debugging, evaluating, and improving real-world ML systems. They will work closely with experienced machine learning engineers and product teams while gradually taking greater ownership of technical initiatives.
Responsibilities
• Build and improve machine learning components across data preparation, training, evaluation, and inference
• Fine-tune and adapt models as part of larger production systems
• Implement evaluation and testing frameworks to better understand model behaviour and performance
• Help build and maintain pipelines for real-world and synthetic training data
• Investigate model failures, performance issues, and production incidents
• Ship improvements iteratively and use real user feedback to guide further development
• Work closely with senior machine learning engineers, product teams, and other technical stakeholders
• Contribute to systems operating under production constraints, including latency, cost, reliability, scalability, and safety
• Improve the quality and maintainability of ML systems through testing, monitoring, and continuous iteration
Requirements
• Strong foundations in machine learning and modern neural network architectures
• Hands-on experience training, fine-tuning, evaluating, or deploying machine learning models
• Strong Python programming skills; experience with PyTorch and/or JAX
• Familiarity with production machine learning systems running on GPUs
• Ability to write clean, reliable, and production-quality code
• Understanding of the machine learning lifecycle, including data preparation, training, evaluation, inference, and deployment
• Ability to learn new tools and technologies quickly
• Ability to work through ambiguous technical problems with guidance
• Willingness to take increasing ownership as experience and confidence grow
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
The successful candidate will work on real production systems from day one, gaining hands-on experience with how large-scale machine learning behaves outside research environments.
This role is suited to an engineer who wants to develop strong systems judgment by shipping, debugging, evaluating, and improving real-world ML systems. They will work closely with experienced machine learning engineers and product teams while gradually taking greater ownership of technical initiatives.
Responsibilities
• Build and improve machine learning components across data preparation, training, evaluation, and inference
• Fine-tune and adapt models as part of larger production systems
• Implement evaluation and testing frameworks to better understand model behaviour and performance
• Help build and maintain pipelines for real-world and synthetic training data
• Investigate model failures, performance issues, and production incidents
• Ship improvements iteratively and use real user feedback to guide further development
• Work closely with senior machine learning engineers, product teams, and other technical stakeholders
• Contribute to systems operating under production constraints, including latency, cost, reliability, scalability, and safety
• Improve the quality and maintainability of ML systems through testing, monitoring, and continuous iteration
Requirements
• Strong foundations in machine learning and modern neural network architectures
• Hands-on experience training, fine-tuning, evaluating, or deploying machine learning models
• Strong Python programming skills; experience with PyTorch and/or JAX
• Familiarity with production machine learning systems running on GPUs
• Ability to write clean, reliable, and production-quality code
• Understanding of the machine learning lifecycle, including data preparation, training, evaluation, inference, and deployment
• Ability to learn new tools and technologies quickly
• Ability to work through ambiguous technical problems with guidance
• Willingness to take increasing ownership as experience and confidence grow
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
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work on real production systems from day one
Będziesz od razu pracować nad rzeczywistymi, działającymi systemami, co może oznaczać brak czasu na wdrożenie i naukę w bezpiecznym środowisku.
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gaining hands-on experience with how large-scale machine learning behaves outside research environments
Doświadczenie zdobyte będzie praktyczne, ale może oznaczać konfrontację z problemami skalowalności i stabilności, które nie występują w środowiskach badawczych.
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gradually taking greater ownership of technical initiatives
Początkowo będziesz mieć mniejszą odpowiedzialność, która będzie stopniowo rosła, co może oznaczać początkowo ograniczony wpływ na kluczowe decyzje.
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Investigate model failures, performance issues, and production incidents
Duża część pracy będzie polegać na rozwiązywaniu problemów i reagowaniu na awarie, co może być czasochłonne i stresujące.
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Ship improvements iteratively and use real user feedback to guide further development
Praca będzie polegać na ciągłym wdrażaniu małych zmian i reagowaniu na opinie użytkowników, co może oznaczać brak możliwości realizacji dużych, długoterminowych projektów.