技术员工，机器学习
Member of Technical Staff, Machine Learning
关于 ActAI
如今有超过 50 亿用户在使用基础应用，如电子邮件、笔记、任务、日历等，他们并不具备 AI 原生能力。我们的使命是为全球任何不习惯复杂提示的用户打造主动式应用。我们旨在通过最少甚至无需提示的方式，将智能带入对话、跑腿、组织和工作流程中。
我们的产品专注于实现长期运行工作流的高可靠性、持久上下文和现实任务完成。我们相信产品将大幅减少幻觉现象。
我们的目标是整理每个人的日常生活，让我们都能把时间花在有价值和有意义的事情上。
职位
作为技术团队成员，机器学习方向，你将构建核心 ML 组件。你从第一天起就会参与真实生产系统，学习大规模 ML 在研究环境之外的表现。
这个职位适合希望通过对真实 ML 系统进行发布、调试和迭代来培养强大系统判断力的工程师。
重点
- 在数据、训练、评估和推理方面构建和改进 ML 组件。
- 作为更大生产系统的一部分，对模型进行微调和适应。
- 实现评估和测试以理解模型行为。
- 帮助构建和维护真实数据和合成数据的数据管道。
- 调试模型问题、性能问题和生产事件。
- 迭代发布改进，并从真实用户反馈中学习。
- 与高级 ML 工程师和产品团队紧密合作。
- 在真实的生产约束下工作：延迟、成本、可靠性和安全性
技术栈
- Python
- PyTorch / JAX
- 在 GPU 上运行的生产 ML 系统
理想经验
- 扎实的机器学习和现代神经网络架构基础。
- 有训练、微调或部署 ML 模型的实际经验。
- 能够编写高质量的生产代码并快速学习新工具。
- 好奇心强、易于指导，并渴望从真实生产系统中学习。
- 在指导之下能够处理模糊性，并随时间增长拥有责任感。
- 倾向于发布、迭代和持续改进。
成果
- 生产中的 ML 模型达到预期的准确性、延迟和可靠性目标。
- 生产问题能被快速识别、有效调试并解决根本原因。
- 数据管道、训练循环和推理系统稳健、可重复且易于维护。
- 能够与工程师、产品和研究团队有效协作，交付可靠的产品
查看英文原文
About ActAI
There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.
Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations.
Our objective is to organise anyone's life, allowing us all to spend time on valuable and meaningful things.
Role
As a Member of Technical Staff, Machine Learning, you will build core ML components. You will work on real production systems from day one, learning how large-scale ML behaves outside of research settings.
This role is for engineers who want to develop strong systems judgment by shipping, debugging, and iterating on real-world ML.
Focus
- Build and improve ML components across data, training, evaluation, and inference.
- Fine-tune and adapt models as part of larger production systems.
- Implement evaluation and testing to understand model behavior.
- Help build and maintain data pipelines for real-world and synthetic data.
- Debug model issues, performance problems, and production incidents.
- Ship improvements iteratively and learn from real user feedback.
- Work closely with senior ML engineers and product teams.
- Work under real production constraints: latency, cost, reliability, and safety
Tech Stack
- Python
- PyTorch / JAX
- Production ML systems running on GPUs
Ideal Experience
- Strong foundations in machine learning and modern neural architectures.
- Some hands-on experience training, fine-tuning, or deploying ML models.
- Comfortable writing production-quality code and learning new tools quickly.
- Curious, coachable, and eager to learn from real systems in production.
- Able to work through ambiguity with guidance and grow ownership over time.
- Bias toward shipping, iteration, and continuous improvement.
Outcomes
- ML models in production meet expected accuracy, latency, and reliability targets.
- Production issues are identified quickly, debugged effectively, and root causes addressed.
- Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable.
- Collaborates effectively with engineers, product, and research teams to deliver reliable ML-powered features.
- Iterations on models and systems are driven by real-world signals and measurable improvements.
How We Work
The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product
Interview process
If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.
Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.
We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.
Originally posted on Himalayas