Software Engineer, Machine Learning

We are looking for intellectually curious, highly motivated individuals to be foundational members of our Machine Learning and Data Platform team. You will partner across the company and use data to design scalable solutions based on a deep understanding of critical business goals. The ideal candidate will support initiatives end to end with software, data & machine learning engineering.

What you'll do:

• Partner closely across the machine learning, platform, and product engineering teams to train models to solve product problems and productionise data science and machine learning artifacts.
• Contribute scalable solutions across various serving stacks at the machine learning service and application layers.
• Build and help set direction for ML infrastructure, such as feature construction patterns, data and model monitoring, online & offline scoring systems, and model usage patterns.
• Develop high quality communication devices such as dashboards, notebooks, documents, and presentations to convey insights across a broad audience.
• Define and advance our technical approach to scalable machine learning.

You

Curious about who thrives at Whatnot? We’ve found that low ego, a growth mindset, and leaning into action and high impact goes a long way here.

As our next Software Engineer, Machine Learning you should have 5+ years of experience, plus:

• Bachelor’s degree in Computer Science, Statistics, Mathematics, Software Engineering, a related technical field, or equivalent work experience.
• Industry experience with a track record of applying practical methods to solve real-world problems on consumer scale data.
• Extensive experience with Python for data science and machine learning software development e.g. Flask, FastAPI, Docker.
• Ability to work autonomously and lead initiatives across multiple product areas and communicate findings with leadership and product teams.
• Experience with operational databases such as PostgreSQL, DynamoDB, Elasticsearch, Redis.
• Proficiency and experience in applied statistical and machine learning fields e.g. Recommendations, Search, Fraud & Anomaly Detection, Experimentation and Causal Analysis.
• Firm grasp of visualization tools for monitoring and logging e.g. DataDog, Grafana.
• Familiarity with cloud computing platforms and managed services such as AWS Sagemaker, Lambda, Kinesis, S3, EC2, EKS/ECS, Kafka, Flink/Spark.
• Professionalism around collaborating in a remote working environment and well tested, reproducible work.
• Exceptional documentation and communication skills.

The salary range may be inclusive of several levels that would be applicable to the position. Final salary will be based on a number of factors including, level, relevant prior experience, skills and expertise. This range is only inclusive of base salary, not benefits or equity in the form of stock options.

Benefits

• Flexible Time off Policy and Company-wide Holidays (including a spring and winter break).
• Health Insurance options including Medical, Dental, Vision.
• Work From Home Support.
• $1,000 home office setup allowance.
• $150 monthly allowance for cell phone and internet.
• Care benefits: $450 monthly allowance on food, $500 monthly allowance for wellness, $5,000 annual allowance towards Childcare, $20,000 lifetime benefit for family planning, such as adoption or fertility expenses.
• Retirement; 401k offering for Traditional and Roth accounts in the US (employer match up to 4% of base salary) and Pension plans internationally.
• Parental Leave: 16 weeks of paid parental leave + one month gradual return to work.

Whatnot is proud to be an Equal Opportunity Employer. We value diversity, and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, parental status, disability status, or any other status protected by local law. We believe that our work is better and our company culture is improved when we encourage, support, and respect the different skills and experiences represented within our workforce.