Senior Deep Learning Engineer, Accuracy Evaluation

Job not on LinkedIn

🔥 0 minutes ago

🌐 Poland, Spain, +3 more countries – Remote

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💵 zł375k - zł650k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 1%

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Logo of NVIDIA

NVIDIA

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Design and build decision-grade evaluation environments for NVIDIA's frontier models spanning reasoning, multimodal, long-context, and agentic systems • Produce auditable accuracy signals that gate every major model release • Research and develop novel evaluation methodologies for emerging model families and capability domains • Build and operate evaluation infrastructure and pipelines, including benchmark environments, regression CI systems, and statistical analysis tooling • Partner with model research, training, and customer teams to translate evaluation signals into release decisions, training iteration direction, and competitive positioning • Collaborate across NVIDIA to bring flagship models from the community and partners to life • Deliver advanced models with lightning-fast inference using enterprise-grade GPU clusters

🎯 Requirements

• BS, MS, or PhD in Computer Science, Machine Learning, Statistics, or a related field • 6+ years of hands-on experience with LLMs • Experience designing and running evaluations for large language models or multimodal AI systems • Experience with agentic, multi-turn, or reasoning-heavy settings • Strong statistical foundations, including experimental design, significance testing, and regression analysis • Proven experience building evaluation infrastructure, including pipelines, benchmark harnesses, and reproducible CI systems • Ability to distinguish signal from noise in benchmark results at scale • Ability to translate quantitative evaluation results into decisions for researchers, product teams, and senior leadership • Deep learning and AI evaluation expertise • Familiarity with open-source evaluation frameworks • Experience designing evaluations for agentic systems • Track record of publishing or contributing to evaluation research, benchmark design, methodology papers, or reproducibility analyses • Experience measuring model accuracy under low-precision inference, including FP8, INT4, and quantization-aware settings • Comfort running large-scale workloads on HPC/Slurm clusters • Experience with reproducible experiment management using MLflow or W&B • Experience optimizing compute costs across hundreds of benchmark runs

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