技术负责人（机器学习）
Technical Lead (Machine Learning)
关于公司
我们的客户是一家由东南亚领先科技公司支持的隐秘人工智能初创公司，目前正在组建其全球创始团队。
该公司正在开发一个以人工智能为核心的通信平台，旨在通过将人工智能直接融入对话中，简化日常任务。用户无需在多个应用程序之间切换，即可在一个智能助手内完成计划、组织、比较、研究和完成任务。
面向仍依赖传统生产力工具的数十亿用户市场，该平台专注于提供可靠的AI工作流程、持续上下文、多步骤推理和无缝任务执行。其使命是创造一个显著提升生产力的AI助手，使日常工作的更简单、更直观。
关于职位
我们的客户正在寻找一名技术负责人（机器学习），负责通过将研究转化为可扩展、生产就绪的机器学习系统来执行其AI平台。该职位位于研究、基础设施和产品交叉点，负责确保模型可训练、可部署、可观测，并针对现实世界性能进行优化。
与研究、工程和产品团队紧密合作，该职位将推动稳健ML基础设施的开发，同时平衡性能、可靠性、延迟和成本。
主要职责
- 领导机器学习系统的端到端执行，包括数据管道、训练工作流、评估框架、推理架构和生产部署。
- 使用现代技术如LoRA、QLoRA、监督微调（SFT）、直接偏好优化（DPO）和模型压缩对模型进行微调和优化。
- 设计、构建和运营以延迟、成本效率和可靠性为重点的可扩展推理系统。
- 开发并维护用于合成和真实世界训练数据集的数据管道。
- 与研究团队合作，构建评估框架以衡量模型性能、鲁棒性、安全性和偏见。
- 通过GPU利用、内存效率、推理优化和扩展策略优化生产部署。
- 与应用工程团队紧密合作，将机器学习系统集成到后端、桌面和移动产品中。
- 通过快速迭代、监控和数据驱动优化持续改进生产系统。
查看英文原文
About the Company
Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team.
The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant.
Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive.
About the Role
Our client is seeking a Technical Lead, Machine Learning to lead the execution of its AI platform by translating research into scalable, production-ready machine learning systems. This role sits at the intersection of research, infrastructure, and product, with responsibility for ensuring models are trainable, deployable, observable, and optimized for real-world performance.
Working closely with research, engineering, and product teams, this position will drive the development of robust ML infrastructure while balancing performance, reliability, latency, and cost.
Key Responsibilities
- Lead the end-to-end execution of machine learning systems, including data pipelines, training workflows, evaluation frameworks, inference architecture, and production deployment.
- Fine-tune and optimize models using modern techniques such as LoRA, QLoRA, Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and model distillation.
- Design, build, and operate scalable inference systems with a focus on latency, cost efficiency, and reliability.
- Develop and maintain data pipelines for both synthetic and real-world training datasets.
- Build evaluation frameworks to measure model performance, robustness, safety, and bias in collaboration with research teams.
- Optimize production deployments through GPU utilization, memory efficiency, inference optimization, and scaling strategies.
- Partner closely with application engineering teams to integrate machine learning systems into backend, desktop, and mobile products.
- Continuously improve production systems through rapid iteration, monitoring, and data-driven optimization.
Requirements
- Proven experience building and deploying production-grade machine learning systems used by real users.
- Strong expertise working with large language models and understanding model behavior, limitations, and failure modes.
- Experience developing scalable ML infrastructure, training pipelines, and inference systems.
- Strong software engineering skills with the ability to write maintainable, production-quality code.
- Experience balancing real-world production constraints, including latency, reliability, scalability, cost, and safety.
- Strong ownership mindset with the ability to independently drive technical initiatives from design through deployment.
- Excellent communication and collaboration skills, with experience working in cross-functional, high-performing engineering teams.
Preferred Technical Skills
Experience with the following technologies is preferred:
- Python
- PyTorch and/or JAX
- GPU-based model training and inference systems
Originally posted on Himalayas