应用研究工程师
Applied Research Engineer
关于我们
Higharc 是一家获得风险投资支持的初创公司，正在改变新住宅的设计和建造方式。加入一个曾为 Autodesk、Electronic Arts、Nike 和 Apple 开发过产品的创始团队。我们已获得超过 1.75 亿美元的融资，得到了顶级风险投资公司以及超过 18 家战略投资者的支持：包括建筑行业的领军企业、建筑产品制造和分销商。
Higharc 正在寻找一名实践型应用研究工程师，将我们的空间 AI 研究转化为购房者和建筑商使用的功能。你将加入我们的平台研发团队，将已在研究中验证过的模型部署到产品中。
你将负责的工作
我们的团队构建能够理解房屋的 AI，我们的生成式平面图引擎已从概念验证阶段发展为 Higharc 产品中的核心工作流程。你将负责该工作的应用方面。研究团队会向你提出一个明确的问题和一个指标。你将训练和调整模型，围绕它们构建管道，并部署一个符合产品延迟和成本预算的组件。
你将在已有的架构中工作。引擎、模式和评估框架已经就绪，你的任务是让它们更快、可重复，并且便于其他人在此基础上进行开发。
你将：
- 在我们的布局生成引擎周围构建组件层：一个有文档说明的 API 协议、一个产品可以调用的服务，以及一个在每次更改时运行的评估门禁。
- 将模型重新训练变成一个命令即可完成的任务，内置基准测试，结果可供整个团队阅读。
- 负责学习能力的延迟和成本预算。
- 将核心研究扩展到新的功能，例如多层平面图和实时编辑。
- 与基础设施工程师合作进行 MLOps 和部署，并指导研究人员和实习生的工程实践。
- 通过每周的状态更新、清晰的 pull request 和大改动前的设计说明来分享你的工作。
关于你
当你开发的模型在产品中运行时，你会感到最开心。你喜欢处理带有明确指标的问题并将其解决，同时在不觉得需要重写代码的情况下改进现有的代码库。你在考虑 45 秒等待时间对用户意味着什么之前，先考虑它对基准测试意味着什么。你对评估保持诚实：你能判断结果是否是测试集的产物，比起隐藏回归问题，你更愿意书面报告它。
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ABOUT US
Higharc is a VC-backed startup that is changing how new homes are designed and built. Join a founding team who’ve shipped products for Autodesk, Electronic Arts, Nike, and Apple. We have raised over $175M with support from top-notch venture capital firms and more than 18 strategic investors: industry leaders in construction, building products manufacturing, and distribution.
Higharc is seeking a hands-on Applied Research Engineer to turn our spatial AI research into capabilities that homebuyers and builders use. You'll join our Platform R&D team, taking models that have proven themselves in research and shipping them into the product.
WHAT YOU'LL DO
Our team builds AI that understands homes, and our generative floor plan engine has moved from proof of concept to a core workflow in Higharc's product. You'll own the applied side of that work. The research team hands you a defined problem and a metric. You train and adapt the models, build the pipelines around them, and ship a component that meets the product's latency and cost budget.
You'll work inside an architecture that already exists. The engine, the schema, and the evaluation frameworks are in place, and your job is to make them faster, reproducible, and easy for others to build on.
Expect to:
- Build the component layer around our layout synthesis engine: a documented API contract, a service the product can call, and an evaluation gate that runs on every change.
- Turn model retraining into a one-command job, with benchmarks built in and results the whole team can read.
- Own the latency and cost budgets for learned capabilities.
- Extend core research into new capabilities, such as multi-story plans and real-time editing.
- Partner with infrastructure engineers on MLOps and deployment, and mentor researchers and interns on engineering practices.
- Share your work in writing through weekly status updates, clear pull requests, and design notes before big changes.
ABOUT YOU
You're happiest when a model you worked on is running in the product. You like taking a well-defined problem with a metric attached and closing it, and you improve an existing codebase without feeling the need to rewrite it. You think about what a 45-second wait means for a user before you think about what it means for a benchmark. You're honest about evaluation: you can tell when a result is an artifact of the test set, and you'd rather report a regression in writing than hide it behind a demo.
Most of your collaborators are remote and some are part-time, so writing things down comes naturally to you. You can go from a research conversation to a product conversation to an infrastructure conversation in the same week and be understood in all three. You also know enough about buildings to see that a bedroom door opening onto a kitchen is wrong before any metric tells you.
You have:
- 5+ years of professional software development, including at least 2 years training and shipping ML models to real users
- Taken at least one model end to end: data preparation, training, evaluation, serving, and the integration that put it in front of users
- Fluency in Python and PyTorch (TensorFlow or Keras is fine if you're ready to move to PyTorch)
- Experience with geometric or structured data such as floor plans, CAD or BIM, graphs, or 2D and 3D geometry
- Built or maintained an evaluation harness or benchmark and used it to gate changes
- Packaged a model behind an API (FastAPI or similar) and owned its latency and cost
- A master's degree in computer science, machine learning, or a related field, or equivalent shipped work (a PhD is not required)
- Comfort working remotely with a team on US Eastern hours
A major plus if you also bring:
- A background in architecture or building, whether that's a degree, professional practice, or AEC software work
- Experience with constraint solvers like OR-Tools CP-SAT, optimization in general, or generative models for layouts and floor plans
- Plugin or tooling work in Rhino, Grasshopper, or Revit, or experience with Hugging Face, experiment tracking, and cloud model serving on AWS or Modal, plus any publications or open source contributions
WORKING AT HIGHARC
Remote work and travel: Our company is entirely remote, and has been since we were founded in 2018. Remote work means more time with family, less time commuting, and the flexibility to blend work and life. We value in-person collaboration and asynchronous deep-work time, which is why we schedule regular team meet-ups in our hubs across the US and, depending on the role, prioritize hiring in those hubs. If your role requires frequent travel beyond pre-scheduled team meet ups, we will represent that to the best of our ability as early as possible in the interview process.
Compensation and benefits: Higharc offers competitive salaries with significant equity, in a fast-growing, well-funded company. We provide comprehensive medical, dental, and vision coverage, with flexible PTO, and meaningful maternity/paternity leave to all U.S based employees that are full-time. You'll also have access to other big-company benefits such like short and long-term disability plans and a 401K. We also provide a stipend to create the ideal home office and support ongoing L&D.
Please note: we are seeing an uptick of fraudulent recruiting activity claiming to be associated with Higharc. All communication and outreach from our in-house team will come from an @higharc.com email address.