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Machine Learning Engineer

About Daydream 

Daydream is a dynamic and innovative fashion technology startup poised to revolutionize the industry. Backed by substantial funding, we blend cutting-edge AI technology with the largest fashion catalog to create unique experiences for our customers. Our team is driven by creativity, passion, and a commitment to shaping the future of shopping.


About the role

As a Machine Learning Engineer at Daydream, you will help transform fashion retail with AI. This role requires a unique blend of strong software engineering skills, deep machine learning expertise, innovative modeling and problem solving, and an intense focus on delivering a great experience for the user. You will match users to an amazing look by building industry-leading content understanding (language and vision) and conversational interfaces. You’ll partner with product and other engineering teams to bring cutting-edge AI solutions to life.

As a startup, we need MLEs that love wearing multiple hats: some weeks you’ll be a data scientist, others a backend engineer.


What you’ll do:

  • AI Agent Creation

    • Work with product to create unique and effective AI agent experiences in product

  • Production

    • Build, deploy, and optimize production-ready, scalable backend and ML services

    • Implement ML models for various applications in fashion retail (conversational interfaces, language and image understanding)

    • Automate repetitive work, and enable rapid experimentation

  • Connect

    • Develop and maintain robust data pipelines for training and serving ML models

    • Implement efficient data processing and feature engineering pipelines

    • Integrate external domain-specific knowledge (fashion) from experts and other sources

    • Collaborate with data engineers to ensure high-quality, reliable data inputs for ML models

  • Monitor

    • Build continuous monitoring of model performance

    • Develop automated retraining pipelines to keep models up-to-date

    • Troubleshoot and debug issues in production ML systems

  • Collaborate

    • Work closely with the team to deliver innovative AI/ML product experiences

    • Productionize ML models, and improve model architectures

    • Partner with product managers to translate business requirements and user journeys into technical solutions

  • Research

    • Innovate unparalleled fashion search, discovery, and computer vision techniques

    • Contribute to company AI research initiatives and potential patent applications

    • User-centric and product-centric focus - build tech to solve real user problems


Who you are

  • 3+ years of experience in machine learning engineering or similar roles

  • Able to solve hard problems independently, while meshing with a team to accomplish shared goals

  • Systems experience

    • Strong systems and backend engineering skills, including service design, testing, deployment, and monitoring; Kubernetes experience is a plus

    • Strong programming skills in Python; familiarity with other languages is a plus

    • Proficiency in working with large datasets and distributed computing systems

    • Proficiency in cloud platforms (e.g., AWS, GCP, Azure) for ML model deployment

    • Experience with ML model serving technologies

    • Willingness to learn Go, Typescript, or whatever programming language is needed to do the job

    • Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes)

  • AI and ML experience

    • Solid understanding of machine learning algorithms, deep learning, and statistical modeling

    • Good modeling instincts for real problems and datasets

    • Extensive experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn

    • Experience with natural language processing (NLP) for applications like chatbots or search

    • Experience with image understanding models, and use in applications

    • Experience in deploying ML models to production environments

    • Understanding of ML ops practices and tools for continuous integration and deployment of ML models

    • Experiencing improving AI/ML product features through the use of data, feedback, and analysis

Additional Valuable Experience

  • Advanced degree in Computer Science, Machine Learning, or a related field

  • Fashion or e-commerce industry

  • Deep natural language understanding experience

  • Deep knowledge of computer vision techniques, particularly as applied to fashion and retail

  • Graph neural networks or knowledge graphs

  • Reinforcement learning techniques

  • Open-source ML projects or research publications

  • Data privacy and ethical AI practices

  • GPU acceleration and distributed training of large-scale models

What we offer

  • Competitive salary, equity and benefits (medical, dental, vision, 401k, etc.)

  • Flexible vacation and remote working 

  • The opportunity to be part of a groundbreaking, AI-focused company.

  • Collaborative work environment with a team of talented, fun-loving individuals.

  • Opportunity to learn and grow in your career while shaping the future of fashion, shopping and technology

Commitment to Diversity

Daydream is proud to be an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees, regardless of race, color, religion, gender, sexual orientation, gender identity, age, national origin, or disability status.

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Average salary estimate

$100000 / YEARLY (est.)
min
max
$80000K
$120000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Machine Learning Engineer, Daydream

At Daydream, we’re on a mission to revolutionize the fashion industry through the power of AI, and we're looking for an exceptional Machine Learning Engineer to join our dynamic team. As a key player in our innovative startup, you will have the exciting opportunity to blend your machine learning expertise with a passion for fashion technology. In this role, you will not only develop sophisticated AI agents and algorithms but also work closely with product and engineering teams to create unique and engaging user experiences. Your skills will shine as you build, deploy, and optimize cutting-edge ML services that transform how users discover fashion. There’s no two days the same at Daydream; one day you might be deep into data pipelines, while the next, you’re crafting conversational interfaces. Your systems and backend engineering prowess, paired with your familiarity in cloud platforms and ML frameworks, will drive our ambitious vision. With your knack for problem-solving and innovative input, you’ll contribute to groundbreaking projects aimed at enhancing user interactions with fashion. Join us in this thrilling adventure of combining AI and retail, and become a vital member of a team focused on shaping a revolutionary shopping experience!

Frequently Asked Questions (FAQs) for Machine Learning Engineer Role at Daydream
What are the key responsibilities for a Machine Learning Engineer at Daydream?

As a Machine Learning Engineer at Daydream, your responsibilities include creating AI agents, developing production-ready ML models, maintaining data pipelines, collaborating with various teams, and innovating new features for enhanced user experiences. You will play a crucial role in automating processes and problem-solving to optimize the fashion retail sector.

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What qualifications do I need to apply for the Machine Learning Engineer position at Daydream?

To be considered for the Machine Learning Engineer role at Daydream, you should possess at least 3 years of experience in machine learning or a similar field, strong programming skills in Python, expertise in ML frameworks, proficiency in cloud services, and a solid understanding of machine learning concepts and algorithms. Additional experience in the fashion or e-commerce sector will be a plus!

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How does Daydream support career growth for Machine Learning Engineers?

Daydream believes in fostering growth by providing opportunities to learn and challenge yourself within our AI-focused environment. As a Machine Learning Engineer, you’ll engage with cutting-edge technologies, collaborate with talented individuals, and have the chance to take part in AI research initiatives, all of which nurture professional development and keep your career on a progressive path.

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What technologies will I be working with as a Machine Learning Engineer at Daydream?

In the Machine Learning Engineer position at Daydream, you will work prominently with Python, TensorFlow, PyTorch, and various cloud platforms such as AWS or GCP. Experience with Kubernetes, Docker, and understanding of ML model serving technologies will also be important, enhancing your ability to deploy and manage scalable machine learning systems.

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What does Daydream offer for work-life balance in the Machine Learning Engineer role?

At Daydream, we value work-life balance and offer flexible vacation policies, remote working opportunities, and a collaborative environment that encourages personal wellness. Our goal is to ensure that every Machine Learning Engineer thrives both professionally and personally.

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Common Interview Questions for Machine Learning Engineer
Can you explain your experience with machine learning algorithms?

Certainly! When discussing your experience with machine learning algorithms in the interview, focus on specific projects where you implemented different algorithms. Share your thought process for selecting particular models and how they helped achieve your project's goals. Highlight your familiarity with techniques like supervised and unsupervised learning, and any frameworks you've used.

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How do you handle model performance monitoring?

For model performance monitoring, describe your approach to setting up continuous monitoring systems. Explain how you would track key metrics after deployment and the importance of retraining models with new data. Be sure to mention any tools or frameworks you have used to facilitate this process.

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Describe a challenging problem you faced and how you solved it.

In answering this question, structure your response using the STAR method (Situation, Task, Action, Result). Share an example relevant to your machine learning work, detailing the steps you took to analyze and resolve the problem, and what you learned from that experience.

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What programming languages do you use for machine learning projects?

It’s essential to be honest about your programming background. Highlight your proficiency in Python, as it is widely used in the ML community, and mention any other languages you're familiar with, such as R, Go, or TypeScript. Discuss your experience with specific libraries and frameworks that you utilized in previous projects.

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Can you describe your experience with cloud platforms for ML model deployment?

In your response, outline the cloud platforms you have used, such as AWS, GCP, or Azure, and provide examples of how you deployed machine learning models in these environments. Discuss any experience with setting up cloud infrastructure, managing resources, and scaling solutions.

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What role does data preprocessing play in your machine learning workflow?

Explain the critical role of data preprocessing in preparing datasets for modeling. Discuss your methods for cleaning, transforming, and ensuring the quality of the data, and why it’s essential for the success of your machine learning projects.

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How do you ensure your ML models are user-centric?

In your answer, emphasize the importance of understanding user needs and requirements before model development. Share strategies you’ve employed to gather feedback, conduct user testing, and iterate based on performance metrics and user experience insights.

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What strategies do you use to stay updated with the latest trends in machine learning?

Discuss your proactive approach to remaining informed about industry advancements. Mention relevant blogs, conferences, webinars, and online courses you follow to stay at the forefront of machine learning technology and discussions.

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How do you integrate domain-specific knowledge into your machine learning projects?

Explain the importance of domain knowledge, especially in a specialized industry like fashion. Share specific examples of how you’ve incorporated insights from industry experts or data to improve model accuracy and relevance in your projects.

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What do you think makes a successful Machine Learning Engineer?

Highlight both technical skills and soft skills. A successful Machine Learning Engineer must have strong analytical capabilities, be adept with machine learning technologies, and possess excellent communication skills to collaborate effectively within teams and present findings to stakeholders.

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