7 jobs for "Lead Machine Learning Engineer - Video Recommendation-Content Ecosystem - Singapore"

Staff Machine Learning Engineer, Siri Runtime Systems and Interaction

At Apple

Posted on Oct 19

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Staff Machine Learning Engineer, Siri Runtime Systems and Interaction

At Apple - Zürich

Posted on Oct 19

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Staff Machine Learning Engineer, Siri Runtime Systems and Interaction

At Apple

Posted on Oct 16

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Video Machine Learning Engineer, Audio & Media Technologies

At Apple - San Diego

Posted on Oct 14

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Machine Learning Engineer, Creative Intelligence Systems

At Apple - Cupertino

Posted on Oct 19

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Software Development Engineer in Test - Video Codec

At Apple - Cupertino

Posted on Oct 17

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Software Development Engineer - Test

At Apple - Sunnyvale

Posted on Oct 14

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Apple

Staff Machine Learning Engineer, Siri Runtime Systems and Interaction

SwitzerlandPosted 20 days ago

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As part of Siri Attention and Invocation, we collaborate to deliver the next revolution in human-computer interaction, to inspire and create groundbreaking technology for large-scale systems spanning speech, vision, and generative AI to overcome real-world challenges through innovation and user-centered design that improves the daily experience of millions of our customers.

Description
We are seeking an exceptional Staff Machine Learning Engineer to lead the development of audio and video generation capabilities. In this role, you will drive the technical vision for generating realistic, expressive synthetic speech and visual representations, mentor senior and junior engineers, and shape the roadmap for multimodal generative experiences across our products.","responsibilities":"Design and implement end-to-end systems for generating synthetic audio (speech, acoustics, sound) and/or video for agents interaction
Lead complex generative AI projects from research prototype through large-scale production deployment
Establish best practices for model evaluation, perceptual quality assessment, and monitoring of generative outputs
Drive technical decisions and architecture for multimodal (audio/video) generation systems, balancing quality, latency, and scalability
Identify high-impact opportunities where advances in generative modeling can meaningfully improve agents experiences
Collaborate closely with research, product, design, and infrastructure teams to translate cutting-edge techniques into shipped features
Mentor and provide technical guidance to engineers across the team, raising the bar for ML engineering practices
Stay current with emerging techniques in generative modeling (e.g., diffusion, autoregressive, end-to-end architectures) and evaluate their applicability to our systems

Preferred Qualifications
Proven experience building and shipping machine learning systems in production, with significant focus on generative modeling
Excellent collaboration and communication skills, with a track record of working across research, engineering, and product teams
Strong software engineering skills, with experience designing scalable ML systems and pipelines (e.g., Python, PyTorch/TensorFlow, distributed training infrastructure)
Experience with speech synthesis (TTS), voice conversion, audio acoustics/background modeling, or conversational AI systems
Experience with generative video/animation techniques (e.g., facial animation, lip-sync, avatar rendering, video diffusion)
Publications in generative modeling, speech, audio, or computer vision at top-tier venues (e.g., NeurIPS, ICML, ICASSP, CVPR, Interspeech)
Experience deploying real-time or low-latency generative models at scale
Familiarity with multimodal modeling (joint audio-visual generation, cross-modal conditioning)
Prior experience mentoring engineers or leading technical direction for a team

Minimum Qualifications
Master's or PhD in Computer Science, Electrical Engineering, Machine Learning, or a related field, or equivalent practical experience
Deep hands-on experience with generative audio and/or video architectures (e.g., diffusion models, autoregressive models, GANs, VAEs, end-to-end neural synthesis)
Demonstrated ability to lead complex, ambiguous projects from research through production, and to make sound technical tradeoffs under real-world constraints (quality, latency, compute)
Experience evaluating generative model outputs, including both objective metrics and perceptual/subjective quality assessment

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