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AI Engineer

Strange Loop Labs is looking for Engineers and Scientists to build our core unstructured data processing platform. We're creating a pipeline that takes unstructured customer data such as PDFs and emails, finds the structure in them, and then applies reasoning on top to solve hard customer problems.

We believe that the best AI products come from a combination of the latest ML techniques and a deep understanding of the customer domain. Our people work closely with customers to understand all the sharp-edges. They then take that knowledge to build out the APIs, services, and models needed to solve the problem and delight customers. Working at Strange Loop means working across all areas of technology.

We love if our people have past experience with ML, but it isn't a deal-breaker. An engineer that is passionate about getting into ML will thrive at Strange Loop. We provide the resources, time, and space to be able to learn whatever ML techniques will best serve our customers.

As a Strange Loop AI Engineer, you will:

  • Work closely with customers to deeply understand their problems and needs.

  • Train, test, deploy, and operate multimodal models that power our core AI platform.

  • Research and prototype SOTA models and techniques that improve our customers’ experience.

  • Build efficient, scalable systems and services that deliver a fantastic AI experience. Our customers are shocked by Strange Loop’s latency, availability, and pricing.

The position is fully remote. We are only accepting applicants based in the US, Canada, Australia, and New Zealand.

What You Should Know About AI Engineer, Strange Loop Labs

Strange Loop Labs is on the hunt for an enthusiastic AI Engineer to join our innovative team and contribute to the development of our cutting-edge unstructured data processing platform. At Strange Loop, we tackle the complexities of handling unstructured customer data like PDFs and emails, extracting valuable insights, and applying advanced reasoning to solve challenging customer problems. We believe that the best AI products are born from a combination of the latest machine learning techniques and a profound understanding of the customer domain. Our engineers work harmoniously with clients to grasp their unique challenges and translate that knowledge into the APIs, services, and models that deliver impressive results. While prior experience in machine learning is a plus, it's not a must-have; we value passion and a willingness to learn. As an AI Engineer in our remote-first environment, you'll immerse yourself in various technological aspects, from training and deploying multimodal models to researching state-of-the-art techniques that enhance our offerings. Your role will involve creating efficient, scalable systems that offer exceptional AI experiences, all while ensuring that we maintain our reputation for outstanding latency, availability, and pricing. If you're ready to take the next step in your engineering career while working with a dynamic team of problem-solvers who genuinely care about their customers, join us at Strange Loop Labs!

Frequently Asked Questions (FAQs) for AI Engineer Role at Strange Loop Labs
What are the responsibilities of an AI Engineer at Strange Loop Labs?

As an AI Engineer at Strange Loop Labs, your primary responsibilities will include working closely with customers to identify their unique needs, training, testing, deploying, and managing multimodal models that drive our AI platform, and prototyping advanced models and techniques that enhance customer experience. You'll also focus on building scalable systems and services that consistently deliver a fantastic AI experience, ensuring our solutions meet high standards of performance.

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What qualifications are required for an AI Engineer position at Strange Loop Labs?

To be considered for the AI Engineer position at Strange Loop Labs, candidates should ideally have a background in engineering or computer science, along with a strong interest in machine learning. While experience in ML is advantageous, a passion for learning and applying new techniques is essential. The role requires technical skills in programming and familiarity with data processing frameworks, as well as strong problem-solving capabilities and a customer-centric mindset.

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Is prior experience in machine learning necessary for an AI Engineer at Strange Loop Labs?

While having prior experience in machine learning is definitely beneficial for an AI Engineer role at Strange Loop Labs, it is not explicitly required. The company values a candidate's desire to learn and grow within the field of machine learning. If you have a strong engineering background and a passion for exploring ML, you'll find plenty of opportunities to develop your skills here.

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What is the work environment like for an AI Engineer at Strange Loop Labs?

At Strange Loop Labs, the work environment for an AI Engineer is fully remote, allowing for great flexibility and work-life balance. The company encourages collaboration within a diverse team that includes engineers and scientists passionate about innovation and technology. You'll have access to resources and support to further your understanding of machine learning while working closely with customers to drive impactful solutions.

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What types of models will an AI Engineer work on at Strange Loop Labs?

As an AI Engineer at Strange Loop Labs, you will work on a variety of models aimed at processing unstructured data and enhancing our core AI platform. This includes training, testing, and deploying multimodal models that leverage diverse input data such as text from emails and documents. Additionally, you will prototype state-of-the-art models and techniques that focus on delivering an outstanding customer experience.

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Common Interview Questions for AI Engineer
Can you explain your experience with machine learning models?

When discussing your experience with machine learning models, it’s important to focus on specific projects where you've applied ML concepts. Explain the models you’ve worked with, the data you utilized, the techniques you implemented, and the outcomes of the projects. Highlight your understanding of model training and performance evaluation, as this shows a comprehensive grasp of the subject matter.

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How do you approach problem-solving when developing AI solutions?

For this question, explain your structured approach to problem-solving. Discuss how you start by understanding the customer’s needs, gathering data, and identifying the key challenges. Mention the importance of iterating on your solutions based on feedback and testing, and highlight any experience you have in collaborating with cross-functional teams to ensure alignment.

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What tools and technologies are you familiar with for building AI platforms?

Here, you should mention specific tools and technologies you've worked with, such as Python libraries (like TensorFlow or PyTorch), data processing frameworks (like Apache Spark), and any cloud platforms (like AWS or Google Cloud) that you've used for deploying AI solutions. Demonstrating your versatility with various technologies will make you a strong candidate.

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Describe a challenging project you've worked on related to AI.

In response to this question, outline a specific project that posed challenges and discuss how you navigated those obstacles. Mention the problem at hand, your role in the project, the actions you took, and the results. This shows your resilience and problem-solving capabilities, which are vital for an AI Engineer.

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How do you ensure the models you deploy are effective and efficient?

To ensure the models you deploy are effective and efficient, discuss your approach to performance evaluation, including using metrics to assess predictive accuracy, resource usage, and scalability. Explain how you iterate based on performance reports and real-world usage, adjusting parameters and architectures as needed to optimize the models continuously.

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What do you think is the future of AI in your field?

In answering this question, articulate your vision for the future of AI within the scope of your field, emphasizing trends you’ve observed and innovations on the horizon. Discuss the transformative potential of AI in addressing existing challenges and improving efficiency, as well as the importance of ethical considerations in AI development.

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How do you keep up with advancements in machine learning and AI?

Let the interviewer know that you are proactive about your learning. Mention resources such as online courses, tutorials, research papers, and community forums that you actively explore. Highlight any professional networking groups or conferences you attend to engage with peers and share knowledge.

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Can you share an example of how you've used customer feedback to improve an AI model?

When responding to this question, share a scenario where customer feedback led you to tweak an AI model. Describe the feedback received, the analysis conducted to understand its implications, the changes you implemented, and the resulting improvements. This illustrates your customer-focused approach and adaptability.

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What do you consider when testing a model before deployment?

Discuss the factors you consider when testing a model, including accuracy, performance, and handling edge cases. Explain how you conduct validation using unseen data and perform thorough evaluations against relevant metrics. Mention user acceptance testing with stakeholders to ensure the model meets business needs.

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How do you handle ambiguity in project requirements related to AI development?

In situations of ambiguity, explain how you focus on gathering as much information as possible and working closely with stakeholders to clarify goals. Highlight your ability to synthesize inputs and create flexible plans that account for uncertainties, ensuring that you're prepared to pivot as necessary throughout the development process.

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Full-time, remote
DATE POSTED
January 14, 2025

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