Machine Learning Engineer

• Reproduce and evaluate research methods using open-weight and API-accessible models
• Design evaluation datasets, probes, scoring methods, baselines, calibration tests, and experiment harnesses
• Work with model weights, logits, hidden states, activations, model APIs, and inference infrastructure
• Build and extend evaluation infrastructure, including runners, judges, persistence, experiment orchestration, and reporting
• Turn research workflows into product experiences, including experiment configuration, runs, traces, comparisons, reports, and review workflows
• Investigate verification methods under fine-tuning, merging, quantization, distillation, safety removal, and deliberate evasion
• Design controlled experiments that separate meaningful signals from artifacts or confounders
• Write technical reports distinguishing measured evidence, interpretation, and hypotheses
• Ship production-quality systems with APIs, background jobs, observability, testing, and documentation
• Reproduce a published model-provenance or verification method within six months
• Build a repeatable model-verification runner with versioned inputs, artifacts, metrics, and reports
• Add a verification workflow to Construct and make it accessible through the Eldros UI
• Run controlled experiments across base, fine-tuned, merged, quantized, and distilled models
• Improve understanding of when verification methods succeed, fail, and why

Applied ML Engineer

• Write highly tuned compute kernels for core deep learning operations, including matrix multiplies, convolutions, and normalizations
• Follow software engineering best practices, including regression testing and CI/CD flows
• Collaborate with the CUDA compiler team to generate optimal assembly code
• Collaborate with deep learning training and inference performance teams to identify layers requiring optimization
• Collaborate with hardware and architecture teams on the programming model for new deep learning hardware features
• Develop optimized code to accelerate linear algebra and deep learning operations on NVIDIA GPUs
• Deliver high-performance code to NVIDIA cuDNN, cuBLAS, and TensorRT libraries

Senior Performance Software Engineer, Deep Learning Libraries

• Define the multimodal content representation strategy across video, image, playable, audio, and text
• Ensure shared foundations serve both relevance and moderation
• Partner with product, engineering, and science teams to translate regulatory and contractual obligations into measurable ML objectives
• Define evaluation criteria where labels are scarce, noisy, and contested
• Build evaluation and labeling infrastructure
• Build shared foundations including content embeddings, game-level features, evaluation harnesses, and labeling pipelines
• Lead architectural direction across teams
• Raise the design and review bar
• Mentor senior and mid-level engineers

Staff Machine Learning Engineer, Content Understanding – Moderation

• Execute the multimodal content representation strategy across video, image, playable, audio, and text
• Build content-understanding foundations serving both relevance and moderation
• Partner with product, engineering, and science teams to translate regulatory and contractual obligations into measurable ML objectives
• Define evaluation standards where labels are scarce, noisy, and contested
• Build evaluation and labeling infrastructure
• Build shared foundations including content embeddings, game-level features, evaluation harnesses, and labeling pipelines
• Power recommendations for billions of users while protecting them from harmful content

Machine Learning Engineer jobs in China

Applied ML Engineer

Senior Performance Software Engineer, Deep Learning Libraries

Staff Machine Learning Engineer, Content Understanding – Moderation

Senior Machine Learning Engineer, Content Understanding – Moderation