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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Machine Learning Engineer, Speech LLM Evaluation - **Company:** Apple Inc. - **Location:** Cambridge, MA, United States - **Experience:** Expert - **Salary:** $184,700.0 - **Contract:** Permanent contract - **Skills:** Distributed Computing Environment, Python (Programming Language), Machine Learning, Large Language Models, Apache Spark, Model Validation, Information Technology, Data Pipelines - **Published:** September 11, 2026 - **Apply:** https://www.jobmonkeyjobs.com/career/28010252/Sr-Machine-Learning-Engineer-Speech-Llm-Evaluation-Massachusetts-Cambridge-7413 ## About the Role Bachelor's degree in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience. Experience building or working with text, speech or audio evaluation pipelines and metrics. Proficiency in Python and experience building data processing pipelines at scale. Experience curating or annotating datasets for machine learning evaluation or training. Working knowledge of statistics as applied to measuring model performance and interpreting evaluation results. Familiarity with large language model evaluation techniques, including automated (LLM-as-judge) and human evaluation methods. Strong written and verbal communication skills, with the ability to explain evaluation results to both technical and non-technical audiences. Preferred Qualifications Experience evaluating audio-native or multimodal (speech-in, speech-out) large language models. Experience designing or running human evaluation studies (e.g., side-by-side comparisons, MOS ratings) at scale. Familiarity with personalization and named-entity evaluation challenges in speech systems. Experience with multilingual or international audio dataset development. Experience with distributed data processing frameworks (e.g., Spark) for large-scale audio dataset generation. Publication record or demonstrated contributions in speech, audio ML, or NLP evaluation. ## Description This role owns the data and metrics foundation for evaluating speech LLMs (e.g., real-time speech understanding and generation models) across accuracy, robustness, and conversational quality. You'll build and curate evaluation datasets that reflect real usage - from personalized named-entity queries to multi-turn fluid conversations - and design the metrics and automated judges that turn model outputs into actionable, trustworthy signal. You'll work closely with modeling, infrastructure, and product partners to make sure every new model is evaluated quickly, consistently, and at the right level of rigor before it reaches customers. Responsibilities Designs and curates audio evaluation datasets that represent real-world usage, including personalized, multilingual, and conversational scenarios. Defines and implements evaluation metrics for audio LLMs, spanning accuracy, robustness, and conversational/generation quality. Builds automated evaluation pipelines and LLM-as-judge tooling to scale audio model assessment without sacrificing reliability. Analyzes model evaluation results to identify accuracy gaps, regressions, and opportunities for hillclimbing, and communicates findings to modeling teams. Partners with human-evaluation programs to design rating protocols and validate that automated metrics correlate with human judgment. Collaborates with infrastructure teams to integrate new evaluation sets and metrics into shared tooling. Contributes evaluation methodology for new audio LLM capabilities as they emerge, adapting existing frameworks to novel model behaviors. ## Related Videos - [Why and when should we consider Stream Processing frameworks in our solutions](https://www.wearedevelopers.com/videos/1085-why-and-when-should-we-consider-stream-processing-frameworks-in-our-solutions) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Inside the Mind of an LLM](https://www.wearedevelopers.com/videos/1617-inside-the-mind-of-an-llm) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Implementing continuous delivery in a data processing pipeline](https://www.wearedevelopers.com/videos/73-implementing-continuous-delivery-in-a-data-processing-pipeline) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [The Best Large Language Models on The Market](https://www.wearedevelopers.com/magazine/319-the-best-large-language-models-on-the-market) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)