Huckleberrys purpose is to create life-changing products and experiences through fresh, beautiful, human-centered technology that brings health, well-being, and a bit of magic to every family.
We combine Data, AI, and Behavioural Science to build products that are at the cutting edge of tech today.
The app has 4.9 stars, garnering rave reviews from people who call it life-changing and their #1 recommendation to parents.
We are rapidly growing and building new products to enable every family to thrive.
About the role
We are seeking a backend Python Engineer to join our engineering team. The backend Python Engineer will play an instrumental role in deploying the code that brings the solutions provided by our proprietary AI/ML algorithms into our app, enabling every family to solve sleep for their children.
The Python Engineer will work closely with multiple teams including Data Science, Engineering, DevOps, Pediatric Consulting, and Product Management to ensure that the needs of our users are met and that our solutions scale. This is an exciting opportunity to see the code that you deploy have a transformative impact on peoples lives.
Area of Responsibility
- Design, develop, and maintain scalable back-end code using Python for new and existing products
- Deploy algorithms from our AI/ML team into production in a cloud environment
- Ensure our cloud infrastructure is meeting performance requirements. E.G. uptime, scalability
- Design and develop RESTful APIs
- Build, deploy, and maintain Continuous Integration/Continuous Deployment (CI/CD) pipelines
- Situationally troubleshoot production issues
- Collaborate with cross-functional teams, including product managers, designers, and other engineers, to identify requirements and deliver high-quality solutions
Requirements
- 4+ years of backend Python engineering experience
- Bachelor’s degree (or higher) in Computer Science or a related area
- Experience with Python frameworks such as Django, Flask, or FastAPI
- A proven ability to effectively use data structures like lists, tuples, sets, dictionaries, and an understanding of algorithms for sorting, searching, etc.
- Strong database skills and knowledge including relational databases (like PostgreSQL, MySQL) and/or NoSQL databases (like MongoDB, Firestore, Cassandra)
- Strong ability to design data schemas and efficiently interact with databases through an ORM or SQL
- Experience with Python data manipulation libraries such as Pandas and Numpy
- Strong debugging and testing skills with an ability to write clean, manageable code and debug and test Python applications using tools like PyTest, unittest etc.
- Experience in developing RESTful services and understanding of web request/response protocols
- Experience with cloud platforms like Google Cloud Platform, AWS, or Azure for deploying applications and managing cloud resources
- Basic DevOps & Deployment experience including familiarity with containerization tools like Docker and continuous integration/continuous deployment (CI/CD) pipelines
- Strong analytical and problem-solving skills, capable of troubleshooting and optimizing code
- Strong collaboration and communication skills with a proven track record of working with Product Management, Engineering, Data Science, and non-technical teams to deliver products and services.
Compensation and Benefits
- Salary range $120,000 – $150,000 dependent on experience
- Equity
- Unlimited PTO
- Health
- Vision
- Paid parental leave for primary and secondary caregiver
Huckleberry Labs is an equal-opportunity employer that is committed to diversity and inclusion in the workplace. We prohibit discrimination and harassment of any kind based on race, color, sex, religion, sexual orientation, national origin, disability, genetic information, pregnancy, or any other protected characteristic as outlined by federal, state, or local laws.
Huckleberry Labs makes hiring decisions based solely on qualifications, merit, and business needs at the time.
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CI/CD pandas Artificial intelligence (AI) Data Science Python Amazon Web Services (AWS) Computer Science Azure Flask FastAPI DevOps Google Cloud Platform (GCP) Django Docker NumPy Unit testing pytest Machine Learning Engineering Product management