AI/ML Engineer (Fintech)

Location: Chicago, IL; New York, NY; or San Francisco, CA, USA. The in-office policy generally requires four days per week in the office and Fridays from home for employees near an office; a fully remote program is also mentioned.

Base salary: $125,000–$292,000 USD per year, depending on location, skills, qualifications, and experience. Full-time employees may also be eligible for bonuses, equity, and benefits.

Company

hirify.global is a financial technology company building user-friendly financial products and platforms that help members manage and improve their financial lives.

What you will do

• Build, train, evaluate, deploy, monitor, and iterate deep learning and classical machine learning models on large-scale financial, transactional, and behavioral datasets.
• Design and improve feature pipelines, batch and real-time inference, monitoring, and model retraining systems.
• Own machine learning problems end to end, from problem framing through production deployment and measurable business impact.
• Partner with Product, Engineering, Analytics, and Risk to turn ambiguous business problems into effective ML solutions.
• Use experimentation to connect model performance with member outcomes and business metrics.
• Contribute to shared ML platforms, tooling, and engineering standards.

Requirements

• Experience building and deploying deep learning models in production, including architecture selection, training dynamics, and evaluation.
• Strong machine learning fundamentals, including classical modeling and evaluation design.
• Hands-on experience across the ML lifecycle: training, experimentation, optimization, deployment, and monitoring.
• Experience handling real-world data issues such as label definition, leakage, class imbalance, and train/serve skew.
• Strong proficiency in Python and SQL; experience with PyTorch and distributed computing such as Spark or PySpark.
• Working knowledge of AWS, SageMaker, Airflow, Kafka, Redis, Snowflake, and modern MLOps practices, plus clear communication with technical and non-technical partners.

Nice to have

• Experience with fraud, risk, abuse detection, or adversarial modeling.
• Experience with credit or underwriting models, model governance, explainability, or fairness requirements.
• Experience with personalization, recommendations, marketing measurement, or experimentation at scale.
• Experience with transformers for tabular or semi-structured data, large-scale pretraining, ML platform work, or applied research publications.

Culture & Benefits

• Benefits include health, financial, and wellbeing programs, commuter support, and backup child, elder, and pet care.
• Generous vacation, company-wide paid days off, and an annual wellness stipend.
• Up to 22 weeks of paid parental leave for birthing parents and 12 weeks for non-birthing parents.
• Family planning reimbursement and time off to support local community organizations.
• Inclusive, entrepreneurial environment focused on financial progress for members.