Software Engineer, Machine Learning​/ ML Engineer, AI Engineer (Applied​/Software)

What You'll Do

Build, release, and maintain production-grade software and machine learning systems that power customer-facing features and core platform services for a Financial Services product.

• Design, develop, test, and deploy product-facing features with strong emphasis on correctness, reliability, and scalability
• Design, implement, and maintain ML models and AI-powered services supporting product features and decisioning systems
• Build end-to-end ML pipelines: data ingestion, feature engineering, model training, evaluation, deployment, and monitoring
• Integrate models into production systems ensuring low-latency inference and reliable service operation
• Collaborate closely with product managers, data scientists, and engineers to translate business problems into ML solutions
• Own ML components across their lifecycle: experimentation, implementation, deployment, production monitoring, and iterative improvement
• Optimize model performance and reliability through validation, A/B testing, and performance tuning
• Apply software engineering best practices to ML codebases: automated tests, code reviews, documentation, and version control
• Participate in incident response, debugging, and remediation for production ML and data systems
• Ensure ML and data systems meet security, privacy, and compliance requirements for sensitive Financial Services data
• Document model designs, assumptions, evaluation results, and operational considerations to support maintainability and knowledge sharing

What You’ll Bring

Minimum requirements (education/experience and technical skills):

• Master’s degree in Computer Science, Computer Engineering, Software Engineering, or a related field (or foreign equivalent) and at least 1 year of relevant experience in the job offered or a related occupation
• Hands-on experience with one or more programming languages used in production ML systems:
  Python, Java, Scala, or Go
• Experience with machine learning frameworks (Tensor Flow, PyTorch, scikit-learn, XGBoost, etc.)
• Familiarity with data processing and ETL frameworks (Spark, Flink, Airflow, etc.)
• Strong knowledge of feature engineering techniques and tooling
• Experience with supervised and unsupervised learning methods and model evaluation metrics
• Experience with ML pipeline and deployment tools (MLflow, TF Serving, Torch Serve, Kubeflow, Docker, Kubernetes, CI/CD)
• Familiarity with model monitoring and observability tools (Prometheus, Grafana, Sentry, custom monitoring)
• Strong software engineering skills: testing, code reviews, modular design, and documentation
• Ability to reason about system performance, latency, and scalability in production settings
• Excellent collaboration and communication skills; experience mentoring or guiding peers is a plus
• Background checks required

Preferred (nice-to-have):

• Production ML / MLOps experience in Fin Tech or Financial Services environments
• Experience designing low-latency inference pipelines and distributed model serving
• Familiarity with privacy-preserving ML, data governance, and regulated-industry compliance

Compensation & Benefits

In addition to the base pay range listed below, this role is eligible for bonus opportunities, equity, and benefits. Final pay will be determined based on job-related factors (education, training, experience, location, business needs, market).

• Base pay range: $161,637 - $200,000 per year (aligned to the compensation zone for the location where work will be performed)
• Performance-driven compensation with multipliers for outsized impact, bonus programs, and equity ownership
• Comprehensive benefits (example highlights): employer-paid health insurance for employees, dependent coverage, lifestyle / wellness spending accounts, employer-paid life & disability insurance, fertility and mental health benefits, 401(k) matching
• Paid time off, company holidays, sick time, and parental leave
• Exceptional office experience, events, and team-focused amenities

(Exact benefits and Total Rewards may vary by region and employing entity. See the hiring entity’s Total Rewards materials for details.)

Work Location & Schedule

• This role is based in Menlo Park, CA. In-person attendance is expected at least three days per week.
• Telecommut…