Applied Machine Learning Engineer

Applied Machine Learning Engineer

Furniture.com

 

About Us

Furniture.com is a high growth consumer tech start up that is addressing fundamental challenges in the $200B US furniture space. The company is a B2B and B2C digital aggregator with a simple mission: To make finding furniture easy. 

For consumers, we make it easy to find the right furniture by enhancing search and streamlining the end-to-end customer experience. For retail partners, we deliver a digital platform allowing them to expand their reach with a high-intent, high value furniture audience. 

Furniture.com is growing fast. Our team is comprised of furniture experts (obviously) as well as world class technologists and brand builders. We come from a variety of walks of life and can be found either in Atlanta or in NYC. We are backed by the investment arm of one of America’s largest furniture retailers, although we are independent and proudly building a technology in service of the furniture industry and furniture shoppers. 

 

About the Job 

As an applied machine learning engineer on our team, you’ll utilize established and state-of-the-art machine learning (ML) techniques to successfully deliver business solutions and innovative technologies and assist with enabling and developing machine learning operations at scale. Our team is proud of our mission-driven and passionate approach to building solutions and working together, and we are looking for an intellectually curious and energetic individual who can replicate this model and mindset across our ML initiatives. Being a dynamic and collaborative team player and creative thinker who has fun tackling complex problems at scale, a strong relationship builder who thrives in a fast-paced environment, and an efficient and organized engineer are characteristics we would love to see in our next team member.

 

Main Responsibilities

• Design, build, and productionize machine learning models to provide business enhancements, solve business challenges, and enable creativity and innovation. 
• Frame machine learning problems and/or machine learning opportunities to enable business value, create and test proof-of-concepts, and architect machine learning solutions, ensuring both creativity and relevance.
• Create scalable processes and strategies for optimal model performance with knowledge of training, retraining, deploying, scheduling, monitoring, improving models, and ML operations.
• Assist team in creating machine learning operations strategies and solutions with Databricks. Implement end-to-end solutions for batch and real-time algorithms and predictions.
• Assist team and adjacent teams with delivery of machine learning projects from beginning to end, including business case definition and scoping, data ingestion and automation requirements, algorithm selections and model development, model deployments, and model integrations to the organization. 
• Translate business requirements and user needs into data stories.
• Help maintain analytics data systems and databases.
• Use fundamental statistical analysis to interpret data, paying particular attention to trends and patterns.

Minimal Qualifications

• Bachelor’s / Master’s degree in a quantitative field.
• Expertise in model machine learning frameworks and technologies (e.g. Scikit-learn, Numpy, Pandas, PyTorch)
• Experience and interest in Natural Language Processing (NLP) and advanced techniques.
• Experience working with SQL and NoSQL databases.
• Strong Python and SQL skills 

We offer a competitive salary, bonus, and benefits package. The salary range is $120K–$150K, based on your seniority, expertise, and performance in the interview process.

About the Interview Process

We are following a different interview strategy that circle around the completion of a case study. 

General Interview Process Overview:

1. Hiring Manager Interview
   • In this initial conversation, we’ll introduce you to our company, discuss our projects and culture, and learn more about your previous experience.
   • We may ask high-level technical questions related to your past projects. For example, if you mention working with a classification model on an imbalanced dataset, we might ask, “How did you handle the imbalanced data?”
2. Case Study (Core of the Process)
   • We will provide a dataset and ask you to develop an end-to-end ML solution. Here’s what we’re looking for:
     • Data Cleaning
     • Feature Engineering
     • Train/Test Sampling
     • Normalization (if needed)
     • Training
     • Evaluation
     • Results Presentation
   • Bonus: You’ll also have the chance to propose a deployment strategy for your model.
3. On-Site Interview (3.5 hours)
   • Case Study Presentation (1.5 hours):
     • Present your case study and collaborate with our team to discuss your approach.
   • Project Brainstorming (30 minutes):
     • Engage in a casual brainstorming session with the team on one of our current projects. This allows you to interact informally and share ideas. There aren’t wrong or right answers here, it’s just interaction and having fun putting some crazy ideas out there!
   • Q&A with the Team (30 minutes):
     • This is your opportunity to ask questions about our team, culture, and projects to ensure we’re the right fit for you. 
   • Debrief with Hiring Manager (30 minutes):
     • Wrap up with the hiring manager to ask any final questions and share feedback on the interview process.

After the on-site interview, you can expect a decision within one week.  

The company is an equal opportunity employer. We do not discriminate in hiring or employment against any individual based on race, color, gender, national origin, ancestry, religion, physical or mental disability, age, veteran status, sexual orientation, gender identity or expression, marital status, pregnancy, citizenship, or any other factor protected by anti-discrimination laws.

 

Applicants must be authorized to work in the U.S.