Machine Learning Engineer II (Fraud)
Ladders · United States
- Hybrid
- Full-time
- $175,000 / year
- United States
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Job highlights
- Develop fraud prediction models with Python.
- Build and scale feature pipelines.
- Integrate models into decision systems.
- Monitor model health and retrain.
- Collaborate with cross-functional teams.
About the role
Machine Learning Engineer II Fraud
For our client, we are seeking a Machine Learning Engineer II (fraud) to join the team of a leader in the Finance & Insurance space. This role will lead work at the intersection of data, AI-enabled capabilities, and scalable technology delivery. You will work across engineering, product, operations, and business stakeholders to translate complex requirements into practical technology solutions. The position offers the opportunity to influence architecture, execution quality, and the technology capabilities that enable long-term growth within a regulated financial services environment.
Location
Remote - US based candidates only, no visa sponsorship available
Compensation
$125,000 – $175,000 annually
Responsibilities
- Develop and enhance fraud prediction models using various data approaches
- Build and scale feature pipelines and training datasets from different data signals
- Prototype modeling ideas and drive the best-performing approaches into production
- Integrate models into decision systems, improving reliability and latency
- Monitor model and data health and define retraining workflows
- Collaborate with cross-functional teams to assess requirements and communicate results
Qualifications
- 2+ years as a machine learning engineer or PhD in a relevant field
- Strong Python skills with production-quality code experience
- Knowledge of tabular classification models, preferably using gradient-boosted decision trees
- Experience with deep learning frameworks, ideally PyTorch
- Familiarity with distributed data processing frameworks like Spark
- Proficient with ML lifecycle tools for orchestration and monitoring
- Ability to collaborate effectively and communicate technical concepts clearly
Benefits
- 100% subsidized medical coverage for you and your dependents
- Generous stipends for technology, food, lifestyle needs, and family-forming expenses
- Competitive vacation and holiday schedules for rest and recharge
- Employee stock purchase plan allowing shares of the company at a discount
Our client is an equal opportunity employer. We encourage you to apply even if you don’t meet every qualification—your background could be exactly what this team needs.
Key skills/competency
- Machine Learning Engineer
- Fraud Detection
- Python
- Tabular Classification Models
- Gradient Boosted Decision Trees
- Deep Learning
- PyTorch
- Spark
- ML Lifecycle Tools
- Data Signals
Skills & topics
- Machine Learning Engineer
- Fraud Detection
- Python
- AI
- Data Science
- Machine Learning
- Finance
- Insurance
- Model Development
- Feature Engineering
- PyTorch
- Spark
- Remote
- US Based
How to get hired
- Tailor your resume: Highlight Python, fraud detection, and ML experience.
- Showcase production code: Emphasize your experience with production-quality code.
- Demonstrate ML lifecycle knowledge: Mention experience with orchestration and monitoring tools.
- Articulate collaboration skills: Prepare examples of working with diverse teams.
- Research client's focus: Understand their finance & insurance domain and fraud challenges.
Technical preparation
Practice Python coding for production.,Build gradient-boosted decision trees.,Implement models using PyTorch.,Process data with Spark.
Behavioral questions
Describe a complex fraud problem solved.,How do you ensure model reliability?,Explain technical concepts clearly.,How do you collaborate with stakeholders?
Frequently asked questions
- What are the primary responsibilities of a Machine Learning Engineer (Fraud) at this company?
- The Machine Learning Engineer (Fraud) will focus on developing and enhancing fraud prediction models, building scalable feature pipelines, integrating models into decision systems, and monitoring model performance within the Finance & Insurance sector. This role requires close collaboration with engineering, product, and business stakeholders.
- What technical skills are most important for this Machine Learning Engineer role?
- Strong Python skills with production-quality code experience are essential. Proficiency in tabular classification models (especially gradient-boosted decision trees), deep learning frameworks like PyTorch, and distributed data processing with Spark are highly valued. Experience with ML lifecycle tools is also crucial.
- Is this Machine Learning Engineer position remote, and are there any location restrictions?
- Yes, this Machine Learning Engineer position is fully remote, but it is restricted to US-based candidates only. No visa sponsorship is available for this role.
- What is the salary range for the Machine Learning Engineer II (Fraud) position?
- The compensation for this role is competitive, ranging from $125,000 to $175,000 annually, reflecting the experience and responsibilities involved in advanced fraud detection and machine learning.
- How does the company support its employees in terms of benefits and well-being?
- The company offers excellent benefits, including 100% subsidized medical coverage for employees and dependents, generous stipends for technology, food, and lifestyle, competitive vacation time, and an employee stock purchase plan.
- What is the minimum experience required for the Machine Learning Engineer II (Fraud) role?
- The minimum qualification is 2+ years of experience as a machine learning engineer or a PhD in a relevant field. The company also encourages applications from candidates who may not meet every qualification but possess valuable alternative backgrounds.
- What are the opportunities for growth and impact in this Machine Learning Engineer role?
- This role offers the chance to influence architecture, execution quality, and technology capabilities that drive long-term growth in a regulated financial services environment. You'll work on cutting-edge AI for fraud prevention.