Senior Machine Learning Engineer

About Trafilea

Trafilea is a dynamic and innovative Tech E-commerce Group that operates multiple direct-to-consumer brands in the intimate apparel and beauty sectors, with a focus on using data-driven strategies to scale their businesses. In addition to our products, we have our own online community dedicated to promoting body positivity. As a rapidly growing global player, Trafilea is committed to creating high-quality products and services that enhance the customer experience and drive long-term growth.
At Trafilea, we foster a culture of collaboration, innovation, and continuous learning. We believe in investing in our people and providing them with the support and development opportunities they need to grow both personally and professionally. With our remote-first approach, you'll have the freedom to work from anywhere in the world, surrounded by a diverse and talented team that spans the globe.

Mission

As a Senior Machine Learning Engineer, you'll lead the development and deployment of advanced models, driving innovation and efficiency across the organization. This role involves crafting production-ready code, designing systems within the overarching architecture, and applying scientific methodologies to solve complex problems. Bridging the gap between business strategy and technical execution, you will ensure that machine learning projects align with company goals, delivering tangible value.

With your expertise, we'll harness the power of machine learning to unlock opportunities, streamline processes, and maintain our position at the forefront of technological advancement.

General Accountabilities

• Develop and maintain production-ready machine learning code that is testable, well-documented, and accounts for edge cases and errors.
• Design code that aligns with the service architecture, utilizing effective abstractions and code isolation.
• Write comprehensive tests covering edge cases, errors, and happy paths following the testing pyramid.
• Gain a high-level understanding of the team's domain and develop expertise in a specific portion of it, contributing significantly to the team's projects.
• Apply the scientific method to bring insights from research directly into all areas of the team's project, showcasing innovative ideas, technologies, or techniques.
• Master the application of theory to practice, iterating through the KDD process, understanding the Deep Learning ecosystem, focusing on robust and scalable ML processes, and developing efficient data pipelines.
• Integrate business and technical strategies, contributing to business discussions and aligning technical projects with business goals.
• Ensure tasks are critically reviewed, appropriately sized, and prioritized, and dependencies are managed for continuous integration and incremental delivery.
• Deliver and encourage the delivery of constructive feedback within the team and to business stakeholders, fostering a culture of continuous improvement.
• Communicate effectively in both technical and non-technical terms, ensuring clarity and audience engagement, while actively listening and paying attention to nonverbal cues.
• Share knowledge frequently, contribute to team documentation, and encourage a culture of knowledge sharing and mentorship within the team.

Role-related Competencies & Requirements

• Graduated in Computer Science, Mathematics, Statistics, or a related field with a strong focus on machine learning, or advanced student (min 3rd degree completed)
• Minimum of 5 years of experience in machine learning engineering, with a proven track record of developing and deploying robust, scalable machine learning models.
• Expertise in writing production-ready code, with a strong understanding of the testing pyramid and experience in writing comprehensive tests.
• Deep knowledge of the machine learning development lifecycle, including the KDD process, Deep Learning ecosystems, MLOps practices, and data engineering.
• Ability to integrate business strategy with technical execution, contributing to business discussions and ensuring alignment of machine learning projects with business objectives.
• Strong communication skills, capable of conveying complex technical concepts in simple terms to a diverse audience, and fostering a culture of open, effective communication within the team.
• Leadership in fostering a culture of continuous improvement, knowledge sharing, and mentorship within the team.
• Proficiency in advanced ML tools and programming languages used in data science (e.g., Python, R, SQL).
• Proficiency in ML cloud services and Saas tools like MLFlow and AWS ecosystem.
• Proven experience in marketing, e-commerce marketplace, or growth teams.

Expected Outcomes for initial 3 months

First 30 Days:

Understand the current machine learning landscape, including tools, frameworks, and ongoing projects within the company. Establish relationships with key stakeholders and identify immediate areas for impact.

First 60 Days:

Develop and deploy at least one major machine learning model or improvement, demonstrating expertise in code quality, testing, and alignment with business goals. Begin mentoring team members and contributing to knowledge sharing.

First 90 Days:

Lead a significant machine learning project from concept to deployment, showcasing the ability to integrate technical and business strategies. Implement and advocate for improvements in team practices and processes, enhancing overall efficiency and collaboration.