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.

1. Model Efficiency & Edge Optimization

• Design 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.

2. Model Introspection

• Analyze 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.

3. Quantization & Numerical Analysis

• Diagnose 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.

4. Fine-grained Engineering & Debugging

• Dive 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).

Requirements

Core Requirements

• Strong 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

Preferred

• Experience 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.