{"schemaVersion":"jobsearcher.job.v1","id":"69cd469f721007f937bf2859","url":"https://jobsearcher.com/jobs/69cd469f721007f937bf2859","canonicalUrl":"https://jobsearcher.com/jobs/69cd469f721007f937bf2859","title":"Machine Learning Engineer (Model Efficiency & Interpretability)","description":"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.","company":"Deeprouteai","rawCompany":"deeprouteai","city":"Fremont","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-04-29T10:28:13.617Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541990","title":"All Other Professional, Scientific, and Technical Services","slug":"all-other-professional-scientific-and-technical-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer (Model Efficiency & Interpretability)","description":"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.","datePosted":"2026-04-29T10:28:13.617Z","dateModified":"2026-04-29T10:28:13.617Z","hiringOrganization":{"@type":"Organization","name":"Deeprouteai","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Fremont","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"69cd469f721007f937bf2859"},"url":"https://jobsearcher.com/jobs/69cd469f721007f937bf2859"}}