Sr. Machine Learning Engineer, Speech LLM Evaluation

Apple, Inc.; Cambridge (MA); Sur place; USD 166 000 - 292 000; Plein temps

Avantages offerts par ce poste

Stock programs

RSU awards

Employee stock plan discount

Medical & dental coverage

Retirement benefits

Discounted products

Résumé du poste

Apple, Inc. Cambridge, MA is seeking a Sr. Machine Learning Engineer to own data and metrics for evaluating speech LLMs, building datasets and automated judges for robust, real-time evaluation across multilingual scenarios.

You will partner with modeling, infra, and product teams to ensure rigorous evaluation before deployment, contributing to personal-context understanding and privacy-first design within Siri.

Qualifications

• Bachelor's degree in CS/EE or related field, or equivalent practical experience.
• Experience building or working with text, speech or audio evaluation pipelines and metrics.
• Proficiency in Python and building data processing pipelines at scale.
• Experience curating or annotating datasets for machine learning evaluation.
• Working knowledge of statistics for measuring model performance and interpreting results.
• Familiarity with LLM evaluation techniques, automated and human evaluation.
• Strong written and verbal communication to explain evaluation results.

Responsabilités

• Designs and curates audio evaluation datasets reflecting real-world usage.
• Defines and implements evaluation metrics for audio LLMs across accuracy and quality.
• Builds automated evaluation pipelines and LLM-as-judge tooling.
• Analyzes evaluation results to identify gaps and improvements.
• Collaborates with human-evaluation programs to align automated metrics with judgments.
• Works with infrastructure teams to integrate new datasets and metrics.
• Contributes evaluation methodologies for emerging audio LLM capabilities.

Connaissances

Python

Data pipelines

Dataset curation

Statistics

LLM evaluation

Communication

Multilingual evaluation

Audio evaluation

Formation

Bachelor's degree in CS/EE or related field

Outils

Spark

Description du poste

Sr. Machine Learning Engineer, Speech LLM Evaluation

Cambridge, Massachusetts, United States Machine Learning and AI

Join the team redefining what a deeply personal and integrated assistant can be.As part of the Siri organization, you will help shape one of the world's most widely used AI assistants, powered by our next-generation of Apple Intelligence, with capabilities like personal context understanding and on-screen awareness, built with privacy from the ground up. Your work will have direct, meaningful impact for users across iOS, iPadOS, macOS, watchOS, and visionOS.Our Speech Evaluation team sits at the center of Apple's ASR, TTS, and real-time conversational AI efforts, partnering directly with the modeling teams. We're growing the team to take on a role focused specifically on evaluating audio LLMs: designing the datasets that stress-test them and the metrics that decide whether they're ready. You'll help define how Apple measures a new class of models that listen, speak, and reason. This is a rare opportunity to build at the intersection of cutting-edge AI and human-centered design, shipping technology that is centered around users and their needs.

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.

Minimum Qualifications

• 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.

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $165,800 and $292,400, and your base pay will depend on your skills, qualifications, experience, and location.

• Apple shareholder opportunity through participation in Apple's discretionary employee stock programs
• Apple employees eligible for discretionary restricted stock unit awards
• Apple employees can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan
• Comprehensive medical and dental coverage
• retirement benefits
• a range of discounted products and free services
• reimbursement for certain educational expenses - including tuition
• discretionary bonuses or commission payments
• relocation

Learn more about Apple Benefits

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant

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Apple accepts applications to this posting on an ongoing basis.