Senior Data Scientist/ML Engineer

 

We are on a mission to make food deliveries better. Relay is a technology company that is empowering restaurants to take back ownership of their customers and provide an elevated courier service, and, in turn, provides couriers with industry leading pay and flexibility. Relay's proprietary logistics engine is changing how local economies operate and how goods criss-cross through your city. Relay's goal is to bring efficiencies of scale to delivery and push down the cost of delivery for merchants and customers while simultaneously providing a top tier courier experience.

 

We are based in NYC and currently operate in NYC, NJ, Philadelphia, Miami, Chicago, and Washington DC. We fulfill millions of deliveries a year using our distributed network of couriers. Additionally, our software provides a unified management system for orders across all delivery platforms and a scalable courier force to ensure on-time delivery no matter the circumstance.

 

Our office is located in Chelsea/Flatiron.

 

 

You'll be responsible for the end to end development, testing, and deployment of machine learning models. You will design and implement the necessary data pipelines to train and serve your models using container orchestration tools like Airflow and Argo.

 

This is a hybrid position and attendance in the NYC office 2 days/week is required. The annual salary range for this role is $155,000 - 205,000

 

What You'll Do

• Perform and take ownership of MLOps for assigned projects
• Develop, test, and deploy containerized machine learning applications
• Optimize and automate pipelines around performance monitoring, model A/B testing, and change control
• Identify opportunities for improvement of existing models & pipelines
• Actively participate in internal tool development
• Work with business partners to identify and develop machine learning solutions which improve operational and system efficiency

What We're Looking For

• MS in computer science, machine learning, data science, or comparable quantitative discipline. PhD is preferred
• At least 3 years of professional experience as a data scientist or machine learning engineer
• Demonstrated experience deploying and maintaining production models
• Demonstrated experience with common ML frameworks  e.g. scikit-learn, xgboost, lightgbm
• Experience with deep-learning frameworks e.g. tensorflow or pytorch
• Great if you have experience with time series forecasting and or transformer architectures
• Expert level proficiency in Python as well as pandas and numpy
• Advanced understanding of SQL and relational databases
• Strong understanding of probability and statistics
• A plus if you have experience with probabilistic programming frameworks e.g. stan, tensorflow_probability, or pyro
• General Intellectual curiosity - prioritizing the why before the how and what

Technologies We Use

• Python
• Kubernetes
• Postgres
• RabbitMQ
• Argo
• Airflow
• Grafana

What We Offer

• 💸 Competitive base compensation
• ✈️ Unlimited vacation that we encourage you to take
• 🩹 Comprehensive medical, dental, and vision insurance plans
• 🖥️ All new laptops and IT equipment
• 📈 Compelling employee equity packages plus life insurance and a 401k plan with company matching
• 👋 A hybrid office model for NYC employees with a lively and lighthearted office environment in Flatiron

We want you to love the work that you do, the team that you’re working with, the environment that you’re working in. It is often hard to tell from a job description what working at a company is like, but some reasons why Relay may not be a good fit for you:

• Our mission of helping restaurants and the last mile delivery space does not resonate with you
• You desire a consistent 9-5 job.
• You prefer highly structured, well-defined environments
• You’re uncomfortable with ambiguity or “being the first” in figuring something out

$155,000 - $205,000 a year

In addition to the competitive base salary, this role is eligible for a discretionary annual bonus + all the offerings listed above!

Location Note: This role is a hybrid in-office position and is expected to be in our NYC office two days a week.