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Staff Machine Learning Engineer 151 views

They are looking for a Staff Machine Learning Platform Engineer to lead their engineering efforts in creating a sustainable platform for their Data Scientists. They’re looking for candidates who not only enjoy writing code and seeing their ideas come to fruition on a global scale, but are also able to upskill the engineers around them by leading by example. You will be leading the development of innovative Machine Learning first platforms to serve their near and long term strategic goals, as they transition into Google Cloud.

What You’ll Do

    • Architect and develop sustainable Machine Learning platforms to serve the needs of their Data Scientist community, who are delivering cutting edge intelligent solutions to help provide their customers with a home they love
    • Partner with product leaders to understand technical pain points for data scientists and other engineers and translate those into a clear and robust engineering solution
    • Be able to think outside the current technology/stack limitations to push the boundaries on what is possible and be able to deliver a feasible solution collaboratively
    • Leverage your deep knowledge of distributed systems engineering to build Wayfair’s next-generation Machine Learning capabilities
    • Advise engineering and product management on the technical roadmap, ensuring that the vision aligns with broader company objectives
    • As a senior engineering leader, you will lead by example to help grow other engineers by mentoring and developing learning opportunities
    • Promote a culture of engineering excellence and strengthen the technical expertise of their engineering and product teams

What You’ll Need

    • 5+ years of experience in software engineering and designing systems at scale
    • 2+ years of experience with one or more Python, Scala, or R with emphasis on common ML platforms components, such as Tensorflow, PyTorch, or other Deep learning frameworks
    • Experience developing data pipelines, and orchestrating the deployment of ML models, including experience with Kubernetes
    • Strong understanding of containerization (Docker, etc.), and associated software engineering best practices
    • Excellent communication skills with demonstrated experience driving teams forward and ability to influence technical decisions to line up with the company’s strategy
    • Hands-on experience driving software developments within high-growth environments at scale
    • Excellent organizational, analytical, and hypothesis-driven critical thinking skills to transform data into actionable insights
    • Bachelor’s degree or equivalent practical experience

      Nice to Have:

    • The mix of start-up and large-company experience
    • Familiarity with Machine Learning platforms offered by Google Cloud, AWS, or Azure, and how to implement them on a massive scale

Wayfair is one of the world’s largest online destinations for the home. Whether you work in their global headquarters in Boston or Berlin, or in their warehouses or offices throughout the world, they’re reinventing the way people shop for their homes. Through their commitment to industry-leading technology and creative problem-solving, they are confident that Wayfair will be home to the most rewarding work of your career. If you’re looking for rapid growth, constant learning, and dynamic challenges, then you’ll find that amazing career opportunities are knocking. No matter who you are, Wayfair is a place you can call home. They’re a community of innovators, risk-takers, and trailblazers who celebrate their differences, and know that their unique perspectives make them stronger, smarter, and well-positioned for success. They value and rely on the collective voices of their employees, customers, community, and suppliers to help guide them as they build a better Wayfair – and the world – for all. Every voice, every perspective matters. That’s why they proud to be an equal opportunity employer. They do not discriminate on the basis of race, color, ethnicity, ancestry, religion, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, veteran status, or genetic

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