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AI Engineer

Substance is looking for a talented AI Engineer to join our dynamic team. In this role, you will leverage your expertise in artificial intelligence and machine learning to develop innovative solutions that meet our clients’ needs. You will be instrumental in building and deploying AI models that drive business growth and improve operational efficiency.

Key Responsibilities:

  • Design, implement, and optimize machine learning models and algorithms.
  • Work with large datasets to perform data cleaning, preprocessing, and feature engineering.
  • Collaborate with product managers and software engineers to integrate AI functionalities into existing applications.
  • Stay current with the latest AI research and industry trends to innovate solutions.
  • Evaluate and improve the performance of AI models through rigorous testing and validation.
  • Document processes and share findings with technical and non-technical stakeholders.

  • Master’s degree in Computer Science, Data Science, AI, or related fields.
  • 3+ years of experience in AI/ML model development.
  • Proficiency in programming languages such as Python, Java, or C++.
  • Hands-on experience with ML frameworks like TensorFlow, Keras, or PyTorch.
  • Solid understanding of machine learning algorithms, data structures, and data mining.
  • Knowledge of cloud services (AWS, Google Cloud Platform, Azure) for deployment of AI models.
  • Excellent analytical and problem-solving skills with attention to detail.
  • Strong communication skills to articulate complex ideas effectively.

At Substance, we value action over words. If your profile aligns with our needs, expect to hear from us within 1-2 weeks. If not, no fluff—just know we appreciate your interest and will keep you in mind for future roles where your expertise can make an impact. We focus on real connections and meaningful matches, ensuring the right opportunity finds you at the right time.

Getsubstance.co Pte. Ltd.

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CEO of Substance
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Guillaume Brunet
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Average salary estimate

$110000 / YEARLY (est.)
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$90000K
$130000K

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What You Should Know About AI Engineer, Substance

Substance is excited to announce an opening for an AI Engineer to join our innovative team! If you're passionate about artificial intelligence and machine learning, this role is a fantastic opportunity to use your skills to create impactful solutions for our clients. As an AI Engineer at Substance, you will lead the charge in designing and optimizing machine learning models that drive business growth and enhance operational efficiency. You'll work with large datasets, performing everything from data cleaning to feature engineering, ensuring our algorithms are robust and effective. Collaboration is key here—so expect to partner with product managers and software engineers to seamlessly integrate AI capabilities into our applications. Staying updated with the latest AI research and trends is just part of the job, helping us maintain our competitive edge. Plus, you'll have the chance to evaluate model performance through rigorous testing and validation, documenting your findings for both technical and non-technical stakeholders to understand. If you hold a Master’s degree in Computer Science or related fields, have over three years of experience in AI/ML development, and are proficient in languages like Python or Java, we want to hear from you! So come and be part of a company that values actions over words and believes in making meaningful connections. Let’s innovate together at Substance!

Frequently Asked Questions (FAQs) for AI Engineer Role at Substance
What responsibilities does the AI Engineer at Substance have?

The AI Engineer at Substance is responsible for designing, implementing, and optimizing machine learning models and algorithms. This includes working with large datasets for tasks such as data cleaning and feature engineering, collaborating with product and software teams for AI integration, staying updated on AI trends, and evaluating model performance through tests and validation.

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What qualifications are required for the AI Engineer position at Substance?

To be considered for the AI Engineer role at Substance, candidates should possess a Master’s degree in Computer Science, Data Science, AI, or a related field, alongside a minimum of three years of experience in AI and machine learning model development. Proficiency in programming languages like Python, Java, or C++, as well as hands-on experience with ML frameworks such as TensorFlow, Keras, or PyTorch, is essential.

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What skills does an AI Engineer need to succeed at Substance?

An AI Engineer at Substance should have strong proficiency in machine learning algorithms, data structures, and data mining techniques. Additionally, knowledge of cloud services for deploying AI models, excellent analytical skills, and strong communication abilities are critical for effectively articulating complex ideas to diverse stakeholders.

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How does Substance support learning and development for AI Engineers?

Substance encourages its AI Engineers to stay current with industry trends and latest research in artificial intelligence, promoting continuous learning and innovation. This commitment helps our team develop cutting-edge solutions and ensures they are well-equipped to handle the evolving challenges in the AI space.

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What is the company culture like for AI Engineers at Substance?

The culture at Substance emphasizes action over words, focusing on creating real connections and meaningful matches. AI Engineers are encouraged to innovate and collaborate within a supportive team environment, where everyone's contributions are valued and recognized.

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Common Interview Questions for AI Engineer
Can you explain a machine learning project you have worked on?

Provide a detailed overview of the project, including the problem you were solving, the data used, the algorithms implemented, and the results achieved. Highlight your role and contributions to emphasize your expertise.

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How do you approach data cleaning and preprocessing?

Discuss your systematic approach to data cleaning, including identifying missing values, outliers, and data types. Mention techniques you use for preprocessing, such as normalization, encoding categorical variables, or feature engineering to prepare the data for model training.

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What machine learning frameworks are you most comfortable with?

Mention specific frameworks such as TensorFlow, Keras, or PyTorch that you have hands-on experience with. Provide examples of projects where you used these frameworks and any challenges you overcame while implementing them.

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How do you evaluate the performance of your machine learning models?

Explain the metrics you use for model evaluation, such as accuracy, precision, recall, F1 score, or AUC-ROC. Discuss methods like cross-validation and design of experiments as part of your strategy for assessing model performance.

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What do you consider when integrating AI functionalities into existing applications?

Address considerations such as system architecture, potential impact on user experience, data privacy and compliance issues, and the importance of collaboration with software engineering teams to ensure smooth integration.

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How do you keep up with the latest trends in AI and machine learning?

Share your practices for staying informed, such as following top AI research journals, participating in online courses, attending workshops or conferences, and being involved in professional networks and communities focused on AI advancements.

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Describe your experience with cloud services for deploying AI models.

Detail your familiarity with platforms like AWS, Google Cloud Platform, or Azure. Talk about particular projects where you've deployed models in the cloud, discussing tools and services you utilized for scaling and managing these deployments.

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What strategy do you use for feature engineering?

Discuss the importance of feature selection and creation in model performance. Provide examples of techniques you apply, such as interaction terms, polynomial features, or dimensionality reduction, and how they impact the model's accuracy.

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How do you handle stakeholder communication regarding complex AI projects?

Emphasize the significance of tailoring your communication style to your audience. Discuss your strategy for simplifying technical concepts, using visuals or examples, and ensuring everyone understands the value and implications of AI-driven solutions.

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What challenges have you faced in AI/ML model development, and how did you overcome them?

Share specific challenges you've encountered, whether technical, logistical, or team-related. Highlight the problem-solving approaches you used and the positive outcomes as a result of your strategies.

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Fondées respectivement en 2012 et en 2013, Substance et Radiance opéraient sous deux noms jusqu’à tout récemment. En fait, jusqu’au moment où, guidés par l’idée et par l’envie d’offrir une approche plus organique à nos clients de toutes tailles, o...

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Full-time, remote
DATE POSTED
March 18, 2025

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