Machine Learning Engineer (Model Efficiency & Interpretability)

We are looking for engineers who go beyond "training bigger models."You will focus on understanding what happens inside models, improving efficiency, reliability, and interpretability—often without relying on massive compute. Model Efficiency & Edge OptimizationDesign and optimize lightweight neural networks (e.g., ShuffleNet, EfficientNet) for high parameter efficiency and real-time performance. Improve latency, memory footprint, and throughput under real-world constraints (on-device / real-time systems). Apply and extend techniques such as quantization, pruning, distillation, and operator-level optimization. Model IntrospectionAnalyze model weights, activations, and internal representations to understand decision mechanisms. Investigate failure cases and error patterns, especially under distribution shift or long-tail scenarios. Develop tools or methods to attribute model behavior (e.g., neuron-level analysis, feature attribution, representation probing). Study and improve robustness of models under transformations such as quantization or compression. Quantization & Numerical AnalysisDiagnose and mitigate performance degradation caused by quantization or reduced precision. Analyze weight/activation distributions and sensitivity to precision changes. Design improved quantization strategies to maintain accuracy under strict compute constraints. Fine-grained Engineering & DebuggingDive deep into model execution to identify bottlenecks at the kernel / operator / graph level. Build experiments to validate hypotheses about model behavior, rather than relying on brute-force scaling. Maintain a strong focus on measurable improvements (latency, memory, stability, error rates). RequirementsCore RequirementsStrong foundation in deep learning and neural network architectures. Hands-on experience with model efficiency optimization (quantization, pruning, distillation, etc.). Experience working under resource constraints (edge devices, real-time systems, or low-latency services). Key Differentiator (Very Important)Demonstrated ability to analyze model internals, not just train models. Experience with: Weight / activation distribution analysis Debugging model behavior beyond metrics Understanding why a model works or fails PreferredExperience with: Model compression or deployment frameworks (TensorRT, ONNX, TVM, etc.) Numerical stability / low-precision training Interpretability or mechanistic analysis of neural networks Prior work showing deep investigation into model behavior, not just scaling experiments.