Senior Machine Learning Engineer, Kikoff
$244-292k
3 days a week in office
Personal finance & credit building platform
Be an early applicant
Personal finance & credit building platform
101-200 employees
Be an early applicant
$244-292k
3 days a week in office
101-200 employees
Company mission
To empower everyone to achieve financial security, no matter where they're starting from.
Role
Who you are
- The ideal candidate will have a strong background in software development, machine learning, and data engineering, with experience in deploying scalable ML models in production environments
- Educational Background: Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field. Advanced degree preferred
- Experience: Minimum of 3 years of experience in machine learning engineering, with a proven track record of deploying ML models in production environments
- Proficiency in programming languages such as Python or Ruby
- Strong understanding of data structures, algorithms, and software design principles
- Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch)
- Familiarity with MLOps practices and tools for continuous integration and deployment of ML models
- Experience with cloud services (e.g., AWS, GCP) and containerization technologies (e.g., Docker, Kubernetes)
- Analytical Skills: Strong problem-solving skills with the ability to analyze complex data sets, apply advanced data science techniques, and derive actionable insights. Proficient in building predictive models, performing statistical analysis, and utilizing machine learning algorithms to identify trends, patterns, and opportunities for optimization
- Communication Skills: Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders
What the job involves
- We are seeking a Senior Machine Learning Engineer to join our team. This role will focus on developing and maintaining machine learning infrastructure and operations, particularly for our cash advance underwriting model and other machine learning use cases
- ML Infrastructure and Operations: Design, build and maintain the infrastructure required for optimal extraction, transformation, and loading of data from various sources. Develop and manage data pipelines and workflows for machine learning models
- Model Development and Deployment: Design, develop, and implement machine learning models for underwriting and other financial service applications. Ensure models are robust, scalable, and maintainable
- Collaboration: Work closely with data scientists, software engineers, and product managers to integrate machine learning models into production systems. Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions
- Performance Monitoring: Monitor and evaluate the performance of deployed models, ensuring they meet the desired accuracy and efficiency metrics. Implement processes for continuous improvement and optimization of models
- A/B Testing and Experimentation: Design and implement experiments to optimize models and ensure they align with business goals
- Mentorship: Provide guidance and mentorship to junior engineers, fostering a culture of learning and growth within the team
Our take
Having a solid credit history is a fundamental building block for financial health and there is a large market of people looking to build or rebuild their credit scores. Roughly half of all American adults have subprime credit or no credit whatsoever, and it’s safe to say there is a demand for credit products tailored to beginners and builders.
Kikoff is a credit-building platform aimed at Millennials and GenZ. Their signature product helps consumers without a credit history to positively influence several key aspects of their credit score. This includes their payment history, how much they owe, and the length of their account history.
Kikoff's goal is to help customers across their entire financial journeys and create a simple and positive banking relationship: from stabilizing cash flows to accumulating savings, and building towards long-term goals such as a mortgage. The company plans to build out its technology and team, across all function areas, to bring new products to market in the future.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
37% employee growth in 12 months
Company
Funding (last 2 of 3 rounds)
Jun 2021
$30m
SERIES B
Jul 2020
$10m
SERIES A
Total funding: $42.5m
Company benefits
- Health Benefits
- Flexible PTO Policy
- Kickoff Paid Holidays
- Office Lunch & Dinners
- Commuter Benefits
- Commuter Flexible Savings
- 401k Matching
- Fitness Benefit Policy
Company HQ
Financial District, San Francisco, CA
Leadership
Cynthia Chen
(Co-Founder & CEO)An experienced Chief Revenue/Risk Officer, Cynthia has worked at OnDeck, LendingHome and Figure. Co-founded Figure in 2018, and Kikoff in 2019.
Christophe Chong
(Co-Founder & CTO)Experience as a Software Engineer at Facebook from 2016-2019, and Head of Growth at Lime between 2018-2019. Co-founded Kikoff in 2019.
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