Senior, Engineer

Define and implement accuracy KPIs across precision modes Develop scalable Python-based accuracy evaluation tools and automated pipelines. Implement accuracy-preserving optimizations for inference frameworks (TensorRT, ONNX Runtime, AITemplate, Triton). Build and maintain automated pipelines for accuracy evaluation across multiple frameworks (ONNX, TensorFlow, PyTorch). Develop reusable plugins for preprocessing, post-processing, and metric evaluation. Execute comprehensive accuracy tests for large-scale models (LLMs, vision, diffusion). Validate accuracy under various quantization and precision settings (FP32, FP16, INT8). Perform accuracy analysis with deep understanding of model architecture, including layers, attention mechanisms, and parameter configurations. Identify architecture-driven accuracy degradation trends and propose optimization strategies. Identify issues related to preprocessing drift, tokenization mismatches, operator fallback, and quantization effects. Analyze accuracy differences across hardware targets, firmware versions, and runtime backends. Perform slice-based accuracy analysis (batch size, concurrency, sequence length, domain shifts). Design and run experiments to recover accuracy, including fine-tuning, calibration, and hyperparameter adjustments. Debug accuracy failures by tracing root causes across data preprocessing, model layers, quantization steps, and deployment pipelines. Compare results across different hardware/software stacks and generate actionable insights. Document workflows, maintain dashboards, and publish accuracy results for stakeholders. Strong background in AI/ML model evaluation and accuracy metrics. Solid understanding of model architectures (transformers, CNNs, RNNs, MoE) and their impact on accuracy. Experience with large language models (LLMs) and generative AI accuracy validation. Expertise with inference runtimes (TensorRT, ONNX Runtime, Triton). Understanding of quantization (INT8/FP8/INT4), calibration, QAT, and accuracy trade-offs. Experience with model graph conversion (PyTorch → ONNX → backend engines). Hands-on experience with accuracy pipeline development and automation frameworks. Understanding of video generation model accuracy and multi-modal evaluation benchmarking Proficiency in Python and familiarity with ML toolkits (ONNX Runtime, TensorFlow, PyTorch). Expertise in accuracy analysis, including statistical methods and visualization tools Ability to design experiments for accuracy recovery and debug accuracy failures effectively. Knowledge of quantization techniques and mixed-precision workflows. Experience with data-centre accelerators (NVIDIA A100/H100/B200, AI100 Ultra, Gaudi, TPU). Knowledge of LLM accuracy evaluation tools (lm-eval, HELM, synthetic benchmarks) is an advantage Strong problem-solving and analytical skills with the ability to isolate complex accuracy issues. Familiarity with distributed deployment systems (Kubernetes, cloud inference services). Bachelor's / Masters degree in Engineering, Machine learning/ AI, Information Systems, Computer Science, or related field. 2-5 years' of Software Engineering or related work experience. 2-5years' experience with Programming Language such as C,C++, Python. Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Test Engineering or related work experience. OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Test Engineering or related work experience. OR PhD in Engineering, Information Systems, Computer Science, or related field. 1+ year of work or academic experience with Software Test or System Test, developing and automating test plans and/or tools (e.g., Source Code Control Systems, Continuous Integration Tools, and Bug Tracking Tools).