Senior Machine Learning Engineer (ML)

The Company

Recurrency is a sales, pricing, and purchasing automation platform for distributors. Despite distribution being a multi-trillion dollar industry, the legacy enterprise resource planning (ERP) systems that exist to help distributors manage their purchasing, inventory, sales, order processing, and accounting are decades behind. For the most part, ERP systems are painfully slow, difficult-to-use, and soul-crushingly manual.

Recurrency’s goal is to reverse ERP stagnation by building a streamlined and intelligent ERP: blazingly fast and complete with powerful automation tools like dynamic pricing and demand forecasting. Using Recurrency can boost a distributor’s revenue and profit margins, while reducing waste and saving time. Most importantly, Recurrency is fully-integrated with the customer’s legacy system, so deploying Recurrency in production can be done in as little as one day.

Founded in Los Angeles and supporting a fully-remote team across the United States, Recurrency is a fast-growing and venture-backed team of talented technologists going all-in on building the next great platform company.

The Role

As a Senior Machine Learning Engineer, you will work with the data science team and report to the Head of Engineering to focus on supporting and developing machine learning solutions. Your work will have a direct impact on the product our customers use everyday. 

The ideal candidate is both forward-thinking and hands-on, has a strong sense of ownership and drive for delivery, and is a good mentor and co-worker. At Recurrency, we pride ourselves on the collaboration between Product, Design, and Engineering and so you have the opportunity to be involved in the entire product lifecycle, from ideation through building, deploying, and continual improvement and evolution.

What You'll Do

• Build, Improve ML infrastructure for model development, training, deployment needs and scaling ML systems
• Translate machine learning models prototypes into production grade models.
• Help define the infrastructure to train, optimize and tune various -categories of machine learning models.
• Work with large datasets to solve hard problems using advanced statistical and Machine Learning techniques
• Deploy models to be able to serve through an API
• Collaborate the Data Science team to effectively support the research process
• Design, develop, optimize and productionize machine learning solutions

About You

• Essential knowledge of Python
• 5+ years of software development experience
• Experience with various databases (Postgres, Snowflake, Redis) and data engineering technologies
• Experience with machine learning frameworks
• Experience architecting machine learning pipelines, including designing and improving infrastructure for ingesting, storing and transforming data
• Built scalable ML infrastructure
• Foundation in data structures, algorithms, and software design with strong analytical and debugging skills.
• Strong interpersonal, communication, and collaboration skills with great problem solving abilities.
• Comfortable working with ambiguity and ability to switch contexts and work on several projects at the same time
• A desire to be part of a diverse and growing team.
• Experience with time series, recommendations a plus

Recurrency aims to ensure a diverse, inclusive, and welcoming work environment. 

Individuals seeking employment at Recurrency are considered without regards to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, or sexual orientation.

Progression overtime

First 30 days:

• You will have reviewed all models
• You will have met the entire team
• You will feel comfortable explaining how AI is used at Recurrency
• Have identified areas that require immediate attention
• Created a state of ML at Recurrency
• Establish a machine learning roadmap at Recurrency
• Strong understanding of AWS usage at Recurrency

Days 60:

• You have built a plan on how to improve our ML infrastructure
• Establish best practices with coding standards, workflows, tools, and product automation
• You have deployed changes to our production environment
• You understand our data pipelines, and are ready to build new ones

Day 90+:

• You have started executing on the foundation plan
• Built tools to monitor data pipeline performance, data quality and models in production
• Deployed improvements to production
• Mentor other ML/DS team members
• Conduct interviews