Machine Learning Engineering Manager
Summary
Leads an interdisciplinary ML team at Farfetch's Porto office building a personalization platform for search, recommendations, and hyper-personalization, serving millions of daily requests via Python microservices. Oversees model development, data pipelines, and serving infrastructure while mentoring engineers, data scientists, and ML engineers.
WHAT YOU'LL DO
- Team Leadership: Lead, mentor, and manage a team of software engineers, data scientists, and machine learning engineers, fostering a collaborative and innovative environment.
- Develop and execute the strategic roadmap for machine learning projects focused on search and recommendations, ensuring alignment with business objectives.
- Provide technical guidance and oversight for the design, implementation, and optimization of machine learning models and systems.
- Oversee the development of end-to-end machine learning solutions, including architecture implementation, data pipeline construction, model serving solutions, and productionizing models.
- Work closely with senior leadership, product managers, and other stakeholders to define project requirements, set priorities, and ensure successful project delivery.
- Ensure the quality and reliability of machine learning models and systems through rigorous testing, monitoring, and continuous improvement practices.
- Foster technological innovation within the team, encouraging the adoption of state-of-the-art techniques and tools, and ensuring the team's output evolves with industry advancements.
- Conduct regular performance reviews, provide feedback, and support the professional development of team members.
- Promote a culture of continuous learning and improvement, staying updated with the latest advancements in machine learning and related fields, and encouraging team members to do the same.
WHO YOU ARE
- BSc, MSc, or PhD in relevant fields such as Computer Science, Machine Learning, Data Science, or related disciplines.
- Strong expertise in machine learning, data science, and software engineering, with hands-on experience in Python, TensorFlow, PyTorch, Scikit-learn, and other relevant libraries and frameworks.
- Proven experience in leading and managing engineering teams, with a track record of delivering successful machine learning projects.
- Ability to develop and communicate a clear vision for the team's work, aligning it with the company's goals and ensuring a strategic approach to problem-solving.
- Strong project management skills, including the ability to prioritize tasks, manage resources, and deliver projects on time and within budget.
- Exceptional communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams and stakeholders.
- Passion for staying updated with the best practices and tools of modern software engineering, machine learning advancements, and advocating for continuous improvement within the team.
Skills
As published by lever · 4 questions
Basics
Resume/CV, Full name, Email, Phone, Current location, Current company, LinkedIn URL, GitHub URL, Portfolio URL, Other website, What is your age range?, I identify my gender as:, I identify my ethnicity/race asSelect all that apply, particularly if you consider yourself of mixed, I identify my sexual orientation as:, Do you have a disability or health need?Our definition of disabilities or health needs includes a wide range of physical and mental conditions that have an impact on your health or life activities. Some examples include: having cancer, hearing loss, HIV, Multiple Sclerosis or having a visual impairment. It also includes neurodiversity, which describes people who think and learn differently, such as autism, dyslexia or Attention Deficit Hyperactivity Disorder.
Short answers (2)
- When could you start working?
- What are your salary expectations (gross/monthly)? optional
Pick from a list (2)
- How would you rate your English level?
- This is a hybrid position based in the office identified (3x/week in the office). Can you confirm that you have that availability?
