Staff Machine Learning Engineer, Growth

Strong programming skills: Proficiency in Python and ML libraries such as PyTorch

Domain expertise: Deep understanding of machine learning, deep learning, and emerging AI technologies. Proven track record of building, debugging, and fine-tuning machine learning for user facing products. Experience with causal inference, uplift modeling, and interventional data collection is a plus.

System design & architecture: Strong background in setting up and optimizing ML infrastructure, including containerization (Docker), orchestration (Kubernetes), and CI/CD workflows for ML (e.g., model versioning, automated testing).

Cloud platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure. Familiarity with ML serving solutions like Ray, KubeFlow, or Weights & Biases is a plus.

Data engineering knowledge: Skills in handling and managing large datasets including data cleaning, preprocessing, and storage. Good understanding of batch and streaming pipelines as well as orchestrators like Argo and Airflow.

Collaboration and communication skills: The ability to work effectively in a team and communicate complex ideas clearly with individuals from diverse technical and non-technical backgrounds..

Software leadership skills: A track record of leading projects through completion with quantifiable and measurable outcomes.

5+ years of experience, depending on education, as an MLE, with at least 2 years in a senior or staff-level role

Previous experience in User Growth or Monetization

3+ years of experience designing and developing end-to-end, production grade ML systems

4+ years of experience working in a cloud environment such as GCP, AWS, Azure

3+ years of experience leading projects with at least 1 other team member through completion.

A degree in computer science, engineering, or a related field or equivalent experience.