Kikoff
Senior Machine Learning Engineer
San Francisco · Senior
Sponsorship not specified$257k-$312kDetected 13 days ago
PythonData StructuresAlgorithmsAWSGCPDockerKubernetesCI/CDMachine LearningTensorFlowPyTorchData EngineeringData ScienceMLOpsStatisticsA/B TestingValuationCommunicationCollaborationProblem SolvingMentoringUnderwriting
Stay score
odds of building a lasting career here
61Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
Personalize to your clock →H-1B wage level
the lottery is wage-weighted — each level is one more entry
Level I$137,0931 entry
Level II$165,1312 entries
Level III$193,1493 entries
Level IV$221,1874 entries
This range already reaches Level IV — the maximum four lottery entries.
DOL prevailing wage, 2026-27 wage year · Software Developers (15-1252) · San Francisco-Oakland-Fremont, CA. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.
Employer immigration record
from this employer's Department of Labor filings
Files H-1B transfers
8 transfer filings in the last year, covering 8 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
Community outcomes
No reports yet — be the first to help the next applicant.
About the role
- Why Kikoff: This is a consumer fintech startup, and you will be working with serial entrepreneurs who have built strong consumer brands and innovative products.
- 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.
- 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.
Responsibilities
- 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.
- With record revenue growth in 2025 and a unicorn valuation, we've built a suite of products that help millions of people build credit, access liquidity, and save money.
- Join us if you want to build something meaningful and help millions of people move forward financially.
- We value extreme ownership, clear communication, a strong sense of craftsmanship, and the desire to create lasting work and work relationships.
- Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.
- Implement processes for continuous improvement and optimization of models.
Requirements
- Strong understanding of data structures, algorithms, and software design principles.
- Analytical Skills: Strong problem-solving skills with the ability to analyze complex data sets, apply advanced data science techniques, and derive actionable insights.
- Communication Skills: Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.
- You absolutely need to be interested in data if you want to leverage your knowledge of systems.
- If you're excited about this role and believe you can do the work, we encourage you to apply.
- ML Infrastructure and Operations: Design, build and maintain the infrastructure required for optimal extraction, transformation, and loading of data from various sources.
- Educational Background: Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field. Advanced degree preferred.
- Technical Skills:
- 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.
- Scrappy. We had a product goal and put out the MVP, collecting our first users with steady growth via paid channels in four months. We don't cut corners when we know we'll need them but we don't build things without that need. We don't like inefficiency but we dislike operationalizing one-off tasks even more.
- Risk-oriented. Everything has risk, but a mature team knows how to make these tradeoffs. That's why we built the MVP fast--because time is your most valuable asset and is practically fungible with money in the startup world.
- Data-obsessed. We all look at data and pull it, and we believe that understanding the mechanics can yield valuable insights. Complex systems require elegant, not just simple solutions. You absolutely need to be interested in data if you want to leverage your knowledge of systems.
Nice to have
- Advanced degree preferred.
Skills
- Proficiency in programming languages such as Python or Ruby.
- 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
- Communication
- Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.
- What we're like:
- $257,000 - $312,000 USD
- Equal Employment Opportunity Statement
- Kikoff Inc. is an equal opportunity employer.
- Please reference the following for more information.
Compensation
- $257,000 - $312,000 USD
Benefits
- 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.
- Collaboration: Work closely with data scientists, software engineers, and product managers to integrate machine learning models into production systems.
- Mentorship: Provide guidance and mentorship to junior engineers, fostering a culture of learning and growth within the team.
- 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.
Company info
- We are grateful you are reading this, and we know that if you're meant to be with us on this journey, then we will see you soon.
- We're looking for capability and trajectory, not a perfect checklist match.
- Kikoff is a profitable, pre-IPO fintech company on a mission to empower everyone to achieve financial security.
Equal opportunity
- We are committed to complying with all federal, state, and local laws providing equal employment opportunities and considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.
Visa & Work Authorization
- in, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class
This listing is sourced directly from Kikoff's careers page and normalized into a canonical job model.