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Machine Learning Engineer (Recommender Systems & Databricks)

Who we are:

Factored was conceived in Palo Alto, California by Andrew Ng and a team of highly experienced AI researchers, educators, and engineers to help address the significant shortage of qualified AI & Machine-Learning engineers globally. ​We know that exceptional technical aptitude, intelligence, communication skills, and passion are equally distributed worldwide, and we are very committed to testing, vetting, and nurturing the most talented engineers for our program and on behalf of our clients.


We are seeking a Machine Learning Engineer who is passionate about building state-of-the-art recommender systems and leveraging Generative AI. You'll work with large-scale data using tools like Databricks and Spark, contributing to innovative AI solutions that enhance personalized experiences while being part of a supportive, dynamic, and collaborative team.  In return, you will be rewarded with an amazing team that supports you, a rich culture, shared success, and the flexibility to work– from the comfort of your home. #LI-Remote


What you will be doing:
  • Design and implement recommender systems to improve product discovery and enhance customer engagement across digital and physical platforms.
  • Build and manage scalable machine learning pipelines for data processing, feature engineering, model training, and deployment using tools like Databricks and Spark.
  • Develop and refine machine learning models to tackle complex problems, leveraging diverse datasets and optimizing performance through parameter tuning and experimentation.
  • Collaborate with software engineers, data scientists, and business stakeholders to integrate models into production and address key business challenges with machine learning solutions.
  • Monitor and maintain deployed models, ensuring performance, reliability, and alignment with evolving business needs through continuous improvement practices.
  • Stay informed on advancements in AI and machine learning, incorporate innovative techniques, and contribute to knowledge-sharing initiatives to improve overall practices.


What you must bring:
  • Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field.
  • Proven experience as a Machine Learning Engineer, demonstrating successful development and deployment of recommender systems.
  • Strong programming skills in languages such as Python along with experience with machine learning libraries/frameworks like TensorFlow, PyTorch, or scikit-learn.
  • Extensive experience handling large-scale data processing and analysis using Spark/PySpark within Databricks, including its native platform services.Solid understanding of machine learning algorithms, deep learning, and statistical modeling techniques.
  • Strong knowledge of experimental design, A/B testing, and performance evaluation metrics for machine learning solutions.
  • Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization (Docker) is a plus.
  • Excellent verbal and written communication skills in English.


At Factored, we believe that passionate, smart people expect honesty and transparency, as well as the freedom to do the best work of their lives while learning and growing as much as possible. Great people enjoy working with other passionate, smart people, so we believe in hiring right, and are very selective about who joins our team. Once we hire you, we will invest in you and support your career and professional growth in many meaningful ways. We hire people who are supremely intelligent and talented, but we recognize that intelligence is not enough. Perhaps more importantly, we look for those who are also passionate about our mission and are honest, diligent, collaborative, kind to others, and fun to be around. Life is too short to work with people who don’t inspire you.  


We are a transparent workplace, where EVERYBODY has a voice in building OUR company, and where learning and growth is available to everyone based on their merits, not just on stamps on their resume. As impressive as some of the stamps on our resumes are, we recognize that human talent and passion exist everywhere, and come from many backgrounds, so stamps matter much less than results. All of us are dedicated doers and are highly energetic, focusing vehemently on execution because we know that the best learning happens by doing. We recognize that we are creating OUR COMPANY TOGETHER, which is not only a high-performing fast-growing business, but is changing the way the world perceives the quality of technical talent in Latin America. We are fueled by the great positive impact we are making in the places where we do business, and are committed to accelerating careers and investing in hundreds (and hopefully thousands) of highly talented data science engineers and data analysts. 


 In short, our business is about people, so we hire the best people and invest as much as possible in making them fall in love with their work, their learning, and their mission.  When not nerding out on data science, we love to make music together, play sports, play games, dance salsa, cook delicious food, brew the best coffee, throw the best parties, and generally have a great time with each other.

Average salary estimate

$95000 / YEARLY (est.)
min
max
$70000K
$120000K

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What You Should Know About Machine Learning Engineer (Recommender Systems & Databricks), Factored

At Factored, we're on the lookout for a dynamic Machine Learning Engineer focused on Recommender Systems to join our vibrant team in Latin America. Founded by the renowned Andrew Ng and a dedicated group of AI experts, we’re committed to nurturing top-notch technical talent globally. In this role, you’ll be at the forefront of building innovative recommender systems and utilizing Generative AI to enhance product discovery and customer engagement. You will dive deep into large-scale data processing using cutting-edge tools like Databricks and Spark, contributing to groundbreaking AI solutions that personalize experiences for users. Your responsibilities will include designing and implementing robust machine learning pipelines, collaborating closely with software engineers and data scientists, and fine-tuning models to solve complex challenges. At Factored, we value a collaborative and supportive culture, offering the flexibility to work from home while enjoying a rich team atmosphere. You will have opportunities to learn and grow, and we believe in fostering a workplace where every voice counts in shaping our company’s direction. If you’re excited about harnessing the power of AI and being part of a mission that transforms the tech landscape in Latin America, we’d love to hear from you!

Frequently Asked Questions (FAQs) for Machine Learning Engineer (Recommender Systems & Databricks) Role at Factored
What are the primary responsibilities of a Machine Learning Engineer at Factored?

As a Machine Learning Engineer at Factored, your key responsibilities will include designing and building state-of-the-art recommender systems, managing scalable machine learning pipelines for data processing, and collaborating with teams to integrate machine learning models into production. Your role is crucial in leveraging tools like Databricks and Spark to enhance customer engagement and product discovery, focusing on continuous improvement and innovation.

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What qualifications do I need to have for the Machine Learning Engineer position at Factored?

To qualify for the Machine Learning Engineer position at Factored, candidates should possess at least a Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Proven experience in developing and deploying recommender systems, strong programming skills in Python, and proficiency with machine learning frameworks are essential. Experience handling large-scale data with Spark/PySpark and knowledge of cloud platforms can also significantly enhance your application.

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What tools and technologies are used by Machine Learning Engineers at Factored?

At Factored, Machine Learning Engineers primarily utilize tools like Databricks and Spark for data processing and analysis. Experience with popular machine learning libraries such as TensorFlow, PyTorch, and scikit-learn is vital for model development. Knowledge of cloud platforms like AWS, Azure, or GCP, as well as containerization using Docker, are advantageous as they ensure efficient deployment and scalability of your solutions.

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How does Factored support the professional growth of its Machine Learning Engineers?

Factored is dedicated to the professional growth of its Machine Learning Engineers by fostering an environment of continuous learning and collaboration. We invest in our team through mentorship programs, knowledge-sharing initiatives, and opportunities to work on cutting-edge AI projects that encourage skill development and innovation. Our transparent culture emphasizes every team member's voice in helping shape the direction of our company.

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What is the company culture like for a Machine Learning Engineer at Factored?

The company culture at Factored is vibrant, collaborative, and supportive. We believe that passionate individuals who inspire each other contribute to a thriving workplace. Our team engages in both professional and social activities, including sports, music, and cooking, creating a strong sense of community. This atmosphere not only enhances job satisfaction but also promotes personal growth and teamwork, fostering a sense of belonging.

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Common Interview Questions for Machine Learning Engineer (Recommender Systems & Databricks)
Can you describe a recommender system you have built in the past?

When discussing a recommender system, focus on its design, the algorithms used, and the challenges faced during the development. Highlight your role in the project, emphasizing the importance of user data, the approach to feature engineering, and how you measured the system's performance. Be prepared to discuss the impact of the system on user engagement and any modifications you made based on feedback.

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What tools do you prefer for developing machine learning models and why?

Be specific about your preference for tools like TensorFlow, PyTorch, or scikit-learn, backed by reasoning. Discuss the scalability, ease of use, and efficiency provided by these tools in creating and deploying models. Convey your hands-on experience with these technologies and any particular projects that demonstrate your proficiency.

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How do you handle large-scale data processing in your machine learning projects?

Describe your experience with tools like Spark and Databricks for large-scale data processing. Outline the methods you utilize for data cleaning, transformation, and management while emphasizing any challenges you overcame. Providing an example of a specific project will strengthen your response and showcase your problem-solving skills.

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What metrics do you consider essential for evaluating the performance of your models?

Discuss the significance of metrics like accuracy, precision, recall, F1 score, and AUC-ROC for model evaluation. Depending on the context, mention metrics specific to recommender systems, such as Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE). Explain how these metrics guide your decisions for model improvement or retuning.

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How do you approach tuning hyperparameters in your models?

Share your process for hyperparameter tuning, such as using grid search, random search, or Bayesian optimization. Emphasize the importance of finding the right balance between model complexity and performance. Including specific examples of how you enhanced model outcomes through effective tuning will demonstrate your expertise.

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Have you conducted A/B testing on your models? Can you provide an example?

Explain your understanding of A/B testing and its relevance in assessing the performance of different models. Share a specific example where you implemented A/B testing, the hypothesis you tested, and how the results influenced your final model selection or enhancements.

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How would you ensure that your recommender system remains relevant over time?

Discuss strategies for continuous monitoring and updating of your recommender system to adapt to changing user preferences. Highlight the significance of incorporating user feedback and ongoing performance evaluations to maintain alignment with business objectives.

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What is your experience with cloud platforms in machine learning projects?

Detail your experience with cloud platforms like AWS, Azure, or GCP, especially the specific services you’ve used for machine learning. Discuss their roles in data storage, processing, and model deployment, illustrating how these tools have contributed to scalability and efficiency in your projects.

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How do you stay updated with advancements in AI and machine learning?

Illustrate your commitment to continuous learning by discussing resources like research papers, online courses, webinars, or relevant communities. Mention conferences you attend or any contributions you’ve made to the field, showcasing how you leverage new knowledge to innovate and improve your practices.

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Why do you want to work for Factored as a Machine Learning Engineer?

This is your chance to align your values and career goals with Factored’s mission. Discuss your passion for AI, your excitement about working on impactful projects in Latin America, and your appreciation for the company’s culture of collaboration and growth. Conveying genuine enthusiasm for contributing to Factored's vision will leave a positive impression.

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Factored is on a mission to become the best and biggest data science company in the Americas. Our objective is to make a significant impact in Latin America and the USA by accelerating data science careers in the region. We want to show the world ...

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Full-time, remote
DATE POSTED
January 5, 2025

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