高级机器学习工程师
Senior Machine Learning Engineer
关于 ActAI
目前有超过 50 亿用户在使用基础应用，如电子邮件、笔记、任务、日历，但他们并不具备 AI 原生特性。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型应用。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。我们相信产品将大大减少幻觉现象。
我们的目标是整理每个人的生活，让我们都能把时间花在有价值和有意义的事情上。
职位
作为技术团队高级成员，机器学习方向，你是生产环境中关键 ML 子系统的独立负责人。你能够处理模糊的问题，设计实用的解决方案，并交付可在大规模下可靠运行的系统。
这是一个注重深度的亲力亲为、高影响力的职位。
重点
- 构建支撑主动型、长周期 AI 产品的核心 ML 系统。
- 全程负责工作：数据准备、训练、评估、推理和迭代。
- 将研究想法转化为可在生产环境中可靠运行的系统。
- 使用真实生产信号调试模型失败和系统问题。
- 快速迭代：发布、测量结果、优化并重复。
- 与研究、产品和工程团队紧密合作，实现真正的用户影响。
- 通过示范和技术判断，指导和评审其他 ML 工程师的工作。
- 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
- 基于 GPU 的训练和推理系统
理想经验
- 你曾构建并发布了面向真实用户的 ML 系统。
- 你了解现代 ML 模型在生产环境中的表现——以及它们的异常表现。
- 你编写高质量的生产代码，并以系统思维而非脚本思维进行思考。
- 你具有责任感，能独立工作，并推动项目完成。
- 你学习速度快，沟通清晰，并通过迭代不断改进。
成果
- 生产中的 ML 模型和系统持续满足准确性、延迟、可靠性和效率目标。
- 复杂的生产问题被监控、调试并解决，对业务的干扰最小。
- 训练、推理和数据流水线随时间推移保持稳健、可扩展和可维护。
- 基于真实世界信号和用户反馈，推动 ML 系统的可衡量改进。
- 提供指导和帮助
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 Senior Member of Technical Staff, Machine Learning, you are an independent owner of critical ML subsystems in production. You take ambiguous problems, design practical solutions, and ship systems that operate reliably at scale.
This is a hands-on, high-impact role focused on depth.
Focus
- Build core ML systems that power a proactive, long-horizon AI product.
- Own work end-to-end: data preparation, training, evaluation, inference, and iteration.
- Turn research ideas into working systems that run reliably in production.
- Debug model failures and system issues using real production signals.
- Iterate quickly: ship, measure outcomes, refine, and repeat.
- Collaborate closely with research, product, and engineering to deliver real user impact.
- Mentor and review work from other ML engineers through example and technical judgment.
- Work under real production constraints: latency, cost, reliability, and safety
Tech Stack
- Python
- PyTorch / JAX
- GPU-based training and inference systems
Ideal Experience
- You have built and shipped ML systems used by real users.
- You understand how modern ML models behave — and misbehave — in production.
- You write strong, production-quality code and think in systems, not scripts.
- You take ownership, work independently, and push work across the finish line.
- You learn fast, communicate clearly, and improve through iteration.
Outcomes
- ML models and systems in production consistently meet accuracy, latency, reliability, and efficiency targets.
- Complex production issues are monitored, debugged, and resolved with minimal disruption.
- Training, inference, and data pipelines are robust, scalable, and maintainable over time.
- Drives measurable improvements in ML systems based on real-world signals and user feedback.
- Provides mentorship and technical guidance to peers, raising the overall ML engineering standard.
- Collaborates cross-functionally to ensure ML features integrate seamlessly into products and meet business goals.
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