苹果Senior Software Engineer - Machine Learning
任职要求
Minimum Qualifications • 6+ years of professional experience of designing and developing large scale distributed cloud applications • Must be able to lead discussions with business partners on understanding business processes, workflows, pain points and review requirements; propose comprehensive and holistic solutions. • Experience of building / deploying ML models in a production environment. • Experience driving end-to-end engineering projects and design for front-end / app development as well as distributed back-end server. • A proven track record of leading projects and guiding engineering team. • Strong Language Proficiency with Chinese and English. • Bachelor’s Degree in Engineering / Computer Science / Information Technology. • St…
工作职责
This role demands the best and brightest; the ideal candidate will be self- motivated, accountable, technically strong and offers a diverse skillset that aligns with the key qualifications listed above. You love working in a dynamic fast paced environment, building and maintaining strong working relationships with a variety of partners, including business partners, application owners, project managers, technology and infrastructure teams to get the work done. You naturally take a deep interest in working closely with business partners to drive the product feature and technology roadmap beyond the first release.
This role requires a blend of skills in software engineering, machine learning, and operations to ensure the smooth functioning of ML systems in production environments. In this role you will: - Lead the team to design and implement automation for model training, testing, validation, and deployment - Collaborate with machine learning engineers to ensure efficient deployment and scaling of ML models - Implement monitoring and alerting systems to track model performance, system health, and data drift - Optimize compute resources for cost and performance efficiency - Manage model versions to ensure traceability and reproducibility
• Design, develop, and deploy robust AI/ML systems with high-quality, scalable, and maintainable code • Translate complex, ambiguous requirements into clear technical plans and lead project execution across engineering efforts • Build scalable infrastructure and platforms to support cutting-edge machine learning workflows, including agentic systems that can plan, reason, and act autonomously • Research and apply state-of-the-art ML techniques—including LLMs, custom model training, and RAG/agent-based architectures—to real-world hardware challenges • Stay current with the fast-evolving AI/ML landscape, continuously improving our tools, systems, and methods to maintain a technical edge • Provide technical mentorship, foster a culture of excellence and inclusion, and help grow team capabilities • Lead design discussions, author technical documentation, and provide thoughtful, actionable feedback to peers • Represent the team in executive reviews, product demos, retrospectives, and cross-functional forums
Lead the development and evaluation of advanced algorithms and models for content generation, especially image and video processing and generation to ensure high quality and engaging content Optimize and scale existing systems to handle large volumes of data and ensure high performanceConduct research and stay up-to-date with the latest advancements in AIGC, computer vision to ensure our solutions are cutting-edgeAnalyze performance and identify opportunities based on offline and online testing, develop, and deliver robust and scalable solutions, make direct impact to both user and advertisers experienceCollaborate with cross-functional teams, including product managers, designers, and other engineers, to define and execute on the ad content generation strategy.
- Keep up to date with and utilize the latest developments in LLM system optimization.- Discover/solve impactful technical problems, advance state-of-the-art LLM technologies, and translate ideas into production.- Optimize LLM inference workloads through innovative kernel, algorithm, scheduling, and parallelization technologies.- Continuously maintain internal LLM inference infrastructure.