AI Jobs in Poland

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As a Senior Software Engineer on the AI Voice Agent team, you will work on real-time systems involving live audio streaming and latency optimization integrated with speech providers. You will build and improve conversation intelligence systems that manage LLM layers, including prompt construction, context management, function calling, and dialogue management to create natural, actionable phone conversations. You will develop the action framework allowing configurable API calls with branching logic and runtime execution, supporting tasks like data lookup and ticket creation during calls. You'll manage knowledge ingestion, storage, and retrieval to enhance agent memory and learning over time. You will collaborate with designers to enable customers to create, configure, test, and deploy voice agents through intuitive product experiences. Additionally, you will help develop evaluation frameworks, analytics, call quality metrics, and monitoring instrumentation, and participate in on-call rotation duties.

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Building the best-in-the-world objects manipulation system with a cross-functional R&D team including Hardware, Software, and AI; inventing and implementing strategies for using new robotic grippers to handle previously unpickable items; improving adaptability, scalability, and reliability of the robotic platform; using data to build heuristics for handling different categories of items; and detecting anomalies using a combination of signals to determine if a robot picked more than one item at once or if one item is disassembling.

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Design, automate, maintain, and optimize Python ETL pipelines (Spark/Ray) for large-scale multimodal data. Build and maintain data cataloging, lineage, quality tooling, integrity verification, access controls, and lifecycle management systems. Provide guidance, internal tools, and documentation to colleagues on data best practices. Serve as a custodian of the company’s datasets, ensuring overall data health, quality, and discoverability.

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As an Applied Research Engineer at Labelbox, the responsibilities include developing cutting-edge systems and methods to create, analyze, and leverage high-quality human-in-the-loop data for frontier model developers. The role involves designing and implementing advanced systems aligning human feedback into AI training processes, such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO). Responsibilities also include working on innovative techniques to measure and improve human data quality, developing AI-assisted tools to enhance data labeling, investigating the impact of different types of human feedback on model performance, optimizing human feedback collection algorithms, integrating breakthroughs into Labelbox's product suite to make human-AI alignment techniques scalable, engaging with customers and the AI community to understand evolving data needs, publishing research in top-tier journals and conferences, exploring new frontiers in human-AI collaboration and AI alignment, and creating technical documentation and educational content to establish Labelbox as a thought leader in AI.

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As a Machine Learning Intern at Nomagic, you will dive into complex problems of physical manipulation to enhance robot capabilities. Your responsibilities include expanding the perception abilities of the robotic system to handle a wider variety of products, detecting anomalies such as identifying when a robot picks more than one item or when an item is disassembling, training models to solve multiple problems with various loss functions, and productionizing machine learning models which involves performance monitoring and A/B testing. You will work on developing groundbreaking technology and collaborate with top professionals in an English-speaking environment, with opportunities to play with robots daily and contribute directly to impactful results.

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Design and build AI agents and AI-enhanced features iteratively that help customers debug, fix, and create Playwright and API tests faster. Implement solutions full stack with your team. Get in touch with users to learn from their feedback directly to build solutions that are delightful and solve real problems.

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Contributors may design rigorous electrical engineering problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python (NumPy, Pandas, SciPy); improve AI reasoning to align with industry-standard logic; and apply structured scoring criteria to multi-step problems.

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Contributors design rigorous statistics problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python libraries such as NumPy, Pandas, SciPy, Statsmodels, and Scikit-learn; improve AI reasoning to align with industry-standard logic; and apply structured scoring criteria to multi-step problems.

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Design rigorous electrical engineering problems reflecting professional practice. Evaluate AI solutions for correctness, assumptions, and constraints. Validate calculations or simulations using Python (NumPy, Pandas, SciPy). Improve AI reasoning to align with industry-standard logic. Apply structured scoring criteria to multi-step problems.

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Design graduate- and industry-level mechanical engineering problems grounded in real practice. Evaluate AI-generated solutions for correctness, assumptions, and engineering logic. Validate analytical or numerical results using Python (NumPy, SciPy, Pandas). Improve AI reasoning to align with first principles and accepted engineering standards. Apply structured scoring criteria to assess multi-step problem solving.