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# Machine Learning Engineer

- **Company:** The DOT Corp
- **Location:** San Francisco, CA, United States
- **Contract:** Permanent contract
- **Skills:** Memory Management, Machine Learning, Pytorch
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/member-of-the-technical-staff-machine-learning-two-dots-8127880

## About the Role

You should be able to take an ambiguous problem, like PDF fraud detection, and turn it into a reasonable technical plan without needing a well-defined box. You should understand the company strategy well enough to know what is more and less likely to be valuable in ML without escalating every decision or planning process to the most senior levels of management.

You should have a strong command of:

 * Tensors, PyTorch, training loops, and model deployment
 * Metrics-driven evaluation and rigorous quality management
 * Statistics, regularization, overfitting, training schedules, and GPU memory management
 * Computer vision, NLP, and multimodal understanding problems
 * Data warehouse-oriented SQL, especially BigQuery
 * Explore-vs-exploit tradeoffs in applied ML work

You should be interested in the company mission through a technical lens: consumer underwriting, document understanding, fraud detection, multimodal understanding, and systems that reveal rather than conceal the real affordability crisis in housing.

Despite the more cerebral nature of the role, this is an applied and impact-focused position. The work requires patience with exploration, but also the judgment to know when a good-enough solution under time pressure is better than searching for a global optimum.

## Description

If you do not know how PyTorch, training, and evaluation work, and cannot talk about real modeling work you have done, we will filter you out at this stage.

 2. Behavioral interview
We will assess whether you are actually interested in working at a startup, whether you can deal with ambiguity, and whether you are more of a pure researcher than an applied builder.

 3. ML foundations interview
We will test rigorous knowledge of math, statistics, ML foundations, metrics and evaluation, tensors, regularization, overfitting, training schedules, and GPU memory management.

 4. Ambiguous problem design
We will ask you to convert a hard, ambiguous problem into a reasonable plan.

 5. Explore-vs-exploit judgment
We will construct a scenario where you need to choose a good-enough solution under time pressure instead of searching for a global optimum.


## Related Videos

- [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning)
- [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning)
- [Guided Memory Management: Rust's Ownership Model](https://www.wearedevelopers.com/videos/809-guided-memory-management-rust-s-ownership-model)
- [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding)
- [What non-automotive Machine Learning projects can learn from automotive Machine Learning projects](https://www.wearedevelopers.com/videos/397-what-non-automotive-machine-learning-projects-can-learn-from-automotive-machine-learning-projects)
- [How Machine Learning is turning the Automotive Industry upside down](https://www.wearedevelopers.com/videos/61-how-machine-learning-is-turning-the-automotive-industry-upside-down)

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