高级机器学习工程师
Senior Machine Learning Engineer
在前线业务中运营是一个大多数软件从未触及的运营难题。按班次计算的劳动力成本、因地区和岗位而异的合规要求，以及利润如此微薄，以至于一个设计糟糕的排班表可能会让一个季度的业绩陷入困境。这比大多数白领SaaS要难得多，但前线行业雇佣了全球80%的劳动力，却只获得了过去十年软件投资的1%。
我们认为这是因为问题需要人工智能，而不仅仅是更好的软件，才能被解决。前线组织掌握着大量运营数据。如果正确使用，这些数据可以让员工真正承担起运营业务的部分职责，而不仅仅是报告业务情况。这就是我们在Sona正在构建的，而且现在还处于早期阶段，你目前构建的系统三年后仍将是基础性的。
我们从N47、Felicis、Gradient和Northzone筹集了超过1亿美元，与英国和美国的100多家企业客户签约，并在伦敦、纽约、奥斯汀和里斯本开设了办公室。更多关于在Sona工作的信息[这里](https://www.sona.ai/careers)。
**职位简介**
你将加入一个由两人组成的机器学习团队，以及一个为多个连锁餐厅的多样化目标提供每半小时需求预测的预测系统。我们的预测模型进入了一个复杂的环境，其预测会引发关键的机器和人工决策，面临反馈循环和高度变化的环境。该系统已经有效运行——现在面临的挑战是将其从少数客户扩展到上百个客户。
你将负责客户上线的全流程：验证数据、选择模型、进行用户验收测试、上线以及之后监控性能。你将参与客户会议，建立关系，并了解实际情况——不仅仅是模型是否准确，还包括厨房是否准备了正确的食物量。
**如果你喜欢这个职位，是因为：**
- 你喜欢对产品和结果全程负责。在Sona，机器学习的成功不仅在于行业最佳的模型，还在于客户能够成功运营他们的业务并感到满意
- 你专注于解决问题，当在“复杂且炫酷”和“把简单的东西展示给用户”之间做出选择时，你会选择后者
- 你热衷于与我们的行业专家合作，深入了解客户业务中发生的事情以及在那里的实际工作情况
- 你能超越数据，看到生成这些数据背后的世界
查看英文原文
Running a frontline business is an operational puzzle most software has never touched. Shift-by-shift labour costs, compliance that changes by region and by role, and margins thin enough that a badly built rota can sink a quarter. It's a harder problem than most white-collar SaaS, and yet frontline sectors employ 80% of the global workforce and have received about 1% of the last decade's software investment.
We think that's because the problem needed AI, not just better software, before it was solvable. Frontline organisations sit on enormous amounts of operational data. Used properly, that data lets agents take on real parts of running the business rather than just reporting on it. That's what we're building at Sona, and it's early enough that the systems you build now will still be foundational in three years.
We've raised over $100M from N47, Felicis, Gradient and Northzone, signed more than 100 enterprise customers across the UK and US, and opened offices in London, New York, Austin and Lisbon. More on working at Sona [here](https://www.sona.ai/careers).
**About the Role**
You'll join a two-person ML team and a forecasting system making half hourly demand predictions across diverse targets for multiple restaurant chains. Our forecasting models enter into a complex environment with key machine and human decisions being made on their predictions, facing feedback loops and a highly variable environment. The system works - the challenge now is scaling it from a handful of clients to 100s.
You'll own client launches end-to-end: validating data, selecting models, running UAT, going live, and monitoring performance afterwards. You'll join client calls, build relationships, and understand what actually matters on the ground - not just whether the model is accurate, but whether the kitchen prepped the right amount of food.
**You'll love this role if:**
- You enjoy taking ownership of the product and outcome end-to-end. Machine learning at Sona is a success if we have happy clients running successful businesses as well as the models which are best in industry
- You have a focus on solving the problem and when given the choice between "complicated and shiny" vs "get something simple in front of a user", you choose the latter
- You're excited by working with our industry experts to really understand what's happening in our client's businesses and the realities of working there
- You see beyond the data to the world that resulted in this data generating process, the issues that come with it and the opportunity that it gives us
- You're experienced in and excited by taking a machine learning project from business idea to deployed production system
- You default to AI tools for development and you're excited by what they can achieve for ML. You use Claude Code, Cursor, or equivalent daily - not as a novelty, but as your standard working mode
**Our role won't be for you if:**
- You're hoping to do research and publish research papers as a key element of the work that you do
- You're looking to move into a less technical, more managerial role
- You're keen to get your hands on fancy new technology X and apply it to something
- You prefer to work on one thing and make it perfect before moving on - the role requires pragmatism, parallelism, and iterative improvement
**Requirements**
You'll need these skills/experience to be successful:
- Production ML experience, with a track record of deploying ML systems that handle messy data, fail gracefully, and need monitoring
- Strong ML fundamentals - you can reason about trade-offs in practice, explain the "why" behind feature and model choices, and make good judgement calls when something unexpected happens
- Client-facing deployment experience - you've personally owned an ML deployment end-to-end and are comfortable on calls with non-technical stakeholders
- Strong programming skills in Python, including the ML/scientific Python stack (e.g. numpy, scikit-learn)
- Daily use of AI development tools (Claude Code, Cursor, Copilot or equivalent) as your default working mode
It would be great if you have experience in some of these areas too:
- Forecasting, time-series, or demand-planning - someone who understands lag features, calendar effects, and evaluation integrity intuitively will ramp significantly faster
- Our stack: Python, scikit-learn, MLflow, Docker, GCP
- A small team where ownership is wide and context-switching is normal
**Benefits**
- Salary: £95,000-£110,000
- Fully remote (European timezones)
- Share options
- 35 days annual leave (25 days standard plus 10 flexible public holiday days)
- Extra day of leave for every year of service
- Pension contributions matched up to 5%
- Comprehensive health insurance
- Enhanced parental leave & pay
- Salary sacrifice childcare scheme (Workplace Nursery)
- Co-working space stipend for those based outside London
- Annual all expenses paid team retreats
- The latest Macbook and equipment budget for your home office
- Professional development budget
- Unlimited free books
_Note: this represents a typical benefits package for a UK-based, full-time employee. Exact details may vary based on location and employment type but we try to be as fair as possible to all of our team members. Please ask your contact in the Talent team to clarify the available benefits for you._