Software Engineer, Machine Learning
Snap Finance
full-time
Posted on:
Location Type: Office
Location: West Valley City • Utah • United States
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About the role
- Develop and innovate on state-of-the-art, scalable ML models leveraging artificial intelligence, machine learning, optimization, and rules-based approaches
- Design and ship end-to-end ML systems, including data pipelines, feature engineering, training and evaluation workflows, online inference, and feedback loops
- Push the boundaries of credit risk modeling, customer behavior analysis, and creditworthiness assessment
- Partner cross-functionally to onboard new data sources, improve data quality, and create durable, high-signal features
- Propose, gather, and integrate diverse datasets to support advanced modeling initiatives
- Assemble and manage large, complex datasets that meet both functional and non-functional business requirements
- Mentor engineers and raise the technical bar through architectural reviews, documentation, and reusable tooling
- Influence technical direction through high-level decisions around system architecture, modeling strategy, and tooling
Requirements
- MS or PhD in a quantitative field such as Statistics, Econometrics, Mathematics, Physics, Computer Science, or related quantitative field
- BS in the fields described below will be considered if skill set and experience are robust
- Possess broad and deep technical expertise across multiple areas of machine learning
- Strong software engineering skills, system design experience, and comfort owning services in production
- History of tackling challenging technical problems and involvement in making high-level decisions about technology choices and system architecture
- 7+ years experience in one or more of the following areas: machine learning, artificial intelligence, recommendation systems, data mining, or related research
- Strong background in Python, Java , or other general-purpose programming languages
- Experience with modern sequence based deep learning (e.g., transformers, RNNs, and other attention-based autoregressive models) and multimodal learning (structured + text + graph/time-series)
- Extensive experience with traditional classification methods (e.g. Gradient Boosting, Decision Trees, Random Forest)
- Proficiency and working knowledge of at least one major deep learning framework (e.g. PyTorch, JAX)
- Experience with filesystems, server architectures, and distributed systems
- Statistical analysis (e.g., Hypothesis testing, experimental design, hierarchical modeling, Bayesian and Frequentist methods)
- Experience with automated workflows: Airflow, Jenkins, etc.
- Experience with AWS cloud services such as EC2 and S3
- Working knowledge of message queuing, stream processing, and highly scalable data store
- Familiarity with common computing environment (e.g. Linux, Shell Scripting)
- Strong SQL skills
- Proven ability to translate insights into business recommendations
Benefits
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Hard Skills & Tools
machine learningartificial intelligencedata miningPythonJavadeep learningstatistical analysisSQLfeature engineeringcredit risk modeling
Soft Skills
mentoringcross-functional collaborationproblem-solvingtechnical decision-makingcommunication