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Lead Machine Learning Engineer

Stuart is a leading tech-enabled logistics platform that transforms on-demand delivery across sectors like food, grocery, and retail. Operating in over 130 cities across Europe, Stuart connects businesses with a network of independent couriers, providing access to fast, flexible, and efficient deliveries.


Our Mission 🚀

We are an impact-driven company that aims to build the future of logistics for a more sustainable world: shared, efficient and reliable. We are committed to creating a new standard for urban deliveries that meet today’s environmental and social challenges while offering a premium delivery experience blending speed, flexibility and convenience.

Stuart is a highly diverse and inclusive company of 280+ employees from different nationalities and backgrounds working across France 🇫🇷, Italy 🇮🇹, Poland 🇵🇱, Spain 🇪🇸 and the UK. 🇬🇧


It’s the right moment and the right place for us to make an impact on millions of people, as home delivery services hit a record high. And guess what? You can help us fulfil our vision 🙌


The role ✨


We are looking for a Lead Machine Learning Engineer, based in Barcelona, Spain, to drive the Machine Learning (ML) engineering efforts within a highly talented team of Data Scientists and ML Engineers. You'll take charge of critical initiatives that enable the team to develop, deploy, and scale innovative machine learning services in domains such as real-time courier incentive & positioning optimization, prediction of estimated times of arrival (ETAs) & risk signals throughout the package lifecycle, and fraud detection.


As a technical leader, you'll not only make key decisions to improve data quality and model performance but also play a hands-on role in building and optimizing advanced solutions to deliver impactful ML products at scale.


Our hybrid working model is 3 days/week in the office.


What will you be doing? 🤔


Build and Scale ML Services: 

Lead the design, implementation, and optimization of our ML backend, enabling the efficient development and deployment of new ML driven features & products.


End-to-End Ownership: 

Own ML services from prototype to production, ensuring performance, reliability, and scalability. This includes:

- PySpark Pipelines: Design and implement efficient pipelines for large-scale training data preprocessing.

- Real-Time Inference with Kafka: Integrate real-time data streaming and model inference.

- APIs for Real-Time Predictions: Develop and deploy RESTful APIs to serve models for real-time inference.

- ML Model Lifecycle Management: Oversee training, storage, retrieval, deployment, and automated retraining of models applying MLOps best practices.

- Monitoring Dashboards: Implement and maintain real-time performance and system health monitoring dashboards.

- CI/CD Pipelines: Automate testing, validation, and deployment of ML assets (code, pipelines, models) with CI/CD workflows.


Mentor and Lead:

Guide and mentor team members, fostering a high-performing, learning-driven engineering culture.


Help Shape our Product Strategy:

Collaborate on product strategy, contribute to roadmap planning, and drive technical decisions across our ML stack.


What do we need from you? 😎

- Background: 5+ years of hands-on ML engineering experience in production environments, developing data and feature engineering pipelines, optimizing and deploying ML models, and integrating solutions into production systems.

- Software Engineering Expertise: Advanced level in Python with deep knowledge of data structures, algorithms, object-oriented programming, and CI/CD workflows. A nice-to-have would be another language, ideally Scala.

- ML Infrastructure and Cloud Proficiency: Strong expertise in building ML infrastructure for event-driven and batch pipelines via Kafka, PySpark, Airflow, DBT, Docker, and Kubernetes. Skilled in optimizing AWS services like S3, Redshift, and EKS for scalability and cost efficiency.

- Collaborative Communication: Excellent skills in articulating complex technical concepts to diverse audiences, aligning technical solutions with strategic business goals.

- Adaptability: Proven ability to excel in fast-changing, ambiguous environments while delivering robust technical solutions.


The stuff you want to know 😉
  • Work in an international, dynamic and passionate environment with a company culture focused on learning and development 🎉
  • Hybrid working model and flexible hours ✨

Current benefits include:
  • Ticket Restaurant by Edenred (€11 daily) 🥗
  • Stuart Academy, offering a wide range of upskilling and development opportunities 🎓
  • Wellness Allowance (€40 monthly) to use in any gym or sport class 🧘
  • Private healthcare provided by Alan 🧑‍⚕️
  • Free monthly Bicing card 🚲
  • Parents-friendly environment - Monthly Childcare voucher Social activities and events (family day, regular community lunch, ceramic class, yoga, gardening class...) 🎈
  • Work-from-Abroad policy (enjoy 30 days per year working from anywhere!) 🏞


At Stuart, we believe that employees today want to evolve in collaborative, high-growth environments where they can demonstrate their abilities and thrive both professionally and personally. We are convinced that employees need to find alignment between their inner values and their company’s culture and mission to unlock their full potential. We work to create a culture of empowerment, continuous learning and growth where everyone can bring expertise, own projects and easily measure their impact 🙌


Stuart is proud to be an equal opportunity workplace dedicated to promoting diversity. We don’t discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status or disability status 💙


Please note: Our Talent Acquisition Team is international coming from across the world 🌍 We kindly ask you to please submit your CV and application in English so that it can be reviewed correctly (unless the job posting is in a language other than English). Thank you 🤗


Want to learn more about us? Visit https://stuart.com/about-us/ 

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What You Should Know About Lead Machine Learning Engineer, Stuart

Stuart, a dynamic tech-enabled logistics platform transforming on-demand delivery in sectors like food, grocery, and retail, is on the lookout for a Lead Machine Learning Engineer in Barcelona. This is a fantastic opportunity to join an impact-driven company dedicated to creating a sustainable future for urban logistics. As the Lead Machine Learning Engineer, you will work closely with a talented team of Data Scientists and ML Engineers to push the boundaries of what's possible in the field of machine learning. You'll be tasked with driving initiatives focused on real-time courier optimization, ETA predictions, and even fraud detection. Your role includes designing and implementing our ML backend, maintaining robust pipelines, and building APIs for real-time predictions. With 5+ years of hands-on experience in ML engineering, you’ll have the chance to lead projects from prototype to production, ensuring scalability and performance. Additionally, Stuart's hybrid working model allows for three days in the office and two days remote, perfect for a balanced work-life blend. Join us as we shape the future of logistics and deliver impactful machine learning solutions that make a difference in the world. Your contributions will be valued, and you'll find our culture fosters continual learning and growth!

Frequently Asked Questions (FAQs) for Lead Machine Learning Engineer Role at Stuart
What are the main responsibilities of a Lead Machine Learning Engineer at Stuart?

As a Lead Machine Learning Engineer at Stuart, your primary responsibilities include overseeing the design and implementation of ML services, managing end-to-end machine learning processes from prototype to production, and creating real-time APIs for model inference. You'll also mentor team members while collaborating on product strategy and driving technical decisions related to the ML stack.

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

To apply for the Lead Machine Learning Engineer position at Stuart, you should have 5+ years of hands-on experience in ML engineering, proficient software engineering skills, especially in Python, and knowledge of ML infrastructure and cloud services. A background in designing data pipelines using technologies like Kafka, PySpark, and Docker is also essential.

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How does Stuart support the career development of its Lead Machine Learning Engineers?

Stuart supports the career development of its Lead Machine Learning Engineers through various initiatives, including the Stuart Academy, which offers a range of upskilling and development opportunities, mentorship programs, and a culture focused on learning and growth.

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What is the work environment like for a Lead Machine Learning Engineer at Stuart?

The work environment for a Lead Machine Learning Engineer at Stuart is dynamic and collaborative, with a focus on continuous learning and empowerment. The company promotes a diverse and inclusive culture, providing a hybrid working model that allows flexibility in your work arrangement.

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What technologies are commonly used in the Lead Machine Learning Engineer role at Stuart?

In the role of Lead Machine Learning Engineer at Stuart, you will frequently work with technologies such as Python, Kafka, PySpark, Docker, and cloud services like AWS. Familiarity with CI/CD workflows, data engineering, and MLOps best practices is also crucial in this position.

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Common Interview Questions for Lead Machine Learning Engineer
Can you describe a machine learning project you led at your previous job?

When answering this question, provide a concise overview of the project, including your role, the problem you aimed to solve, and the technologies you utilized. Highlight how you overcome challenges, streamlined processes, and measured the impact of your solution. This showcases both your technical skills and leadership experience.

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How do you ensure the scalability of machine learning models?

Discuss your approach to building scalable machine learning models, emphasizing the importance of designing robust pipelines, utilizing cloud services for deployment, and employing techniques like model compression and batch processing. Providing specific examples from past experiences will strengthen your answer.

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What strategies do you use for monitoring the performance of machine learning models?

Mention your experience with setting up monitoring dashboards, using performance metrics, and implementing automated retraining processes. Discuss how you analyze model drift and take corrective actions, highlighting your proactive approach to model maintenance.

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Explain the significance of MLOps in machine learning projects.

MLOps is essential as it streamlines the deployment and maintenance of machine learning models. Elaborate on how implementing MLOps practices improves collaboration between data and operations teams, enables efficient version control, and ensures model performance consistency post-deployment.

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

Share your strategies for staying updated, whether through attending conferences, participating in online courses, or engaging with professional communities. Mention specific resources like research papers, blogs, or influencers that you follow to demonstrate your commitment to continuous learning.

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What is your experience with real-time data processing?

Discuss any hands-on experience you've had with real-time data processing technologies, such as Kafka or Spark Streaming. Provide examples of projects where you successfully implemented these technologies and the challenges you faced in ensuring timely and accurate data processing.

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Can you provide an example of a complex problem you solved in machine learning?

Illustrate a specific complex problem, detailing the steps you took to approach it, the methodology applied, and the outcome. Highlight your analytical skills and how your solution improved the system's efficiency or accuracy.

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How would you handle disagreements within your team regarding technical decisions?

Discuss your conflict-resolution strategies, emphasizing the importance of open communication and collaboration. Describe how you would facilitate discussions, encourage diverse perspectives, and ultimately seek a consensus that aligns with the project goals.

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What do you believe is the most important quality for a Lead Machine Learning Engineer?

Speak to qualities such as leadership, technical expertise, and adaptability. Provide examples of how these qualities have helped you succeed in previous roles, reinforcing that a combination of skills contributes to the effectiveness of a lead engineer.

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Describe your experience with deploying machine learning models to production.

Give a detailed overview of your experience deploying models to production, including the tools and frameworks you used (like Docker or Kubernetes), challenges faced during deployment, and lessons learned from operationalizing machine learning models.

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Stuart's ambition is to transform the on-demand delivery market.

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Full-time, hybrid
DATE POSTED
December 20, 2024

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