About CareerTakes

CareerTakes is a next-generation AI recruiting platform that connects early-career talent with real roles at established companies across regulated industries.

👉 Important disclosure: CareerTakes is a third-party recruiting platform supporting this hiring process. If selected, you will be employed directly by our client, a Financial Services company.

Applicants for this role may also receive access to additional matched opportunities through the CareerTakes platform.

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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

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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 (TensorFlow, 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, TorchServe, 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 FinTech 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

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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.)

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Work Location & Schedule

• This role is based in Menlo Park, CA. In-person attendance is expected at least three days per week.
• Telecommuting or hybrid arrangements may be permitted in accordance with client policies and local requirements.
• The role may require occasional in-office collaboration and participation in core team events.

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Compliance & Hiring Notes

• This position is being recruited by CareerTakes on behalf of a confidential client in the Financial Services industry.
• Background checks are required as part of the hiring process.
• Offers and employment are contingent on successful completion of required checks and compliance with applicable local, state, and federal law.
• CareerTakes and our client will consider reasonable accommodations for qualified applicants with disabilities during the application and interview process. If you need assistance, please contact us.

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How to Apply

Apply by clicking APPLY NOW (or follow the CareerTakes application flow). Indicate job code AIML4BMPI4 in your application and upload your resume and any relevant portfolio or GitHub links. Selected candidates may be invited to additional matched opportunities through CareerTakes.

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Equal Opportunity & Hiring Transparency

CareerTakes and our client are Equal Opportunity Employers committed to building a diverse and inclusive workforce. We prohibit discrimination or harassment of any kind. To support a fair and efficient hiring process, AI tools may be used to assist with application review or resume screening. These tools do not replace human decision-making. Final hiring decisions are made by people.

If you have questions about how your data is used, please contact us directly.