Founding Senior Machine Learning Engineer

SentiLink

1d•$170,000 - $240,000•Remote

About The Position

SentiLink provides innovative identity and risk solutions, empowering institutions and individuals to transaction with confidence. We’re building the future of identity verification in the United States replacing a clunky, ineffective, and expensive status quo with solutions that are 10x faster, smarter, and more accurate. We’ve seen tremendous traction and are growing extremely quickly. Our real-time APIs have helped verify hundreds of millions of identities, starting with financial services and rapidly expanding into new markets. SentiLink is backed by world-class investors including Craft Ventures, Andreessen Horowitz, NYCA, and Max Levchin. We’ve earned recognition from TechCrunch, CNBC, Bloomberg, Forbes, Business Insider, PYMNTS, American Banker, LendIt, and have been named to the Forbes Fintech 50 list every year since 2023. Last but not least, we’ve even made history - we were the first company to go live with the eCBSV and testified before the United States House of Representatives on the future of identity. SentiLink supports a variety of ways to work, ranging from fully remote to in-office. We operate as a digital-first company with strong collaboration across the U.S. and India. We maintain physical offices in Austin, San Francisco, New York City, Seattle, Los Angeles, and Chicago in the U.S., and in Gurugram (Delhi) and Bengaluru in India. If you’re located near one of these offices, we would love for you to spend time in the office regularly. Some roles are hybrid or in-office by design. For example, our engineering team in India works primarily from our Gurugram office. Role: We’re looking for a founding Senior Machine Learning Engineer to help scale and operationalize our ML systems end-to-end. This is a highly technical role focused on building the infrastructure, tooling, and processes that allow our Data Science team to develop, deploy, monitor, and iterate on machine learning models efficiently and safely. This person will be a foundational owner of our ML platform and will define the interfaces between Data Science, Engineering, and Infrastructure. You’ll work on systems that power real-time production ML, ensuring we can confidently ship models, measure their impact, and detect issues early. This is a high-ownership role for someone who wants to build ML systems that power real-world fraud prevention at scale. Technologies: Python, SQL, MLflow, Datadog, Grafana, Prometheus, Airflow/Dagster/Prefect, Docker, Kubernetes, AWS, PostgreSQL, Git, CI/CD pipelines, GitHub Actions.

Requirements

5+ years of relevant experience, with a degree in Computer Science, Engineering, Mathematics, or a related technical field.
Strong software engineering fundamentals, with proficiency in Python and SQL, and strong working knowledge of Git and modern CI/CD workflows.
Hands-on experience with ML experimentation and model tracking tools.
Strong proficiency with model monitoring and observability tooling.
Experience with ML infrastructure and orchestration technologies, such as Docker, Kubernetes, and workflow orchestration frameworks.
Familiarity with model serving and deployment frameworks.
Proven experience deploying and operating machine learning models as production services, with an emphasis on reliability and performance.
Demonstrated ability to build 0-to-1 prototypes and proof-of-concepts, rapidly standing up ML services and experimentation environments.
Experience designing, building, and optimizing ML pipelines for training, evaluation, and deployment.
Highly adaptable and able to learn quickly in fast-moving environments with evolving technical requirements.
Candidates must be legally authorized to work in the United States and must live in the United States.

Responsibilities

Own SentiLink’s real-time ML model monitoring domain, leading the design, implementation, and ongoing improvement of monitoring systems and workflows.
Own our ML experimentation, model tracking, and versioning infrastructure, ensuring strong reproducibility and visibility across the model lifecycle.
Drive improvements to the model development process, reducing inefficiencies, improving code quality, resolving DS tooling gaps, and enabling faster iteration.
Serve as the primary technical owner of key touchpoints and interfaces between Data Science and Engineering/Infrastructure, defining standards and workflows.
Support efforts to optimize model behavior in production, including latency, reliability, maintainability, and operational best practices.
Investigate and diagnose model performance issues on an ad-hoc basis, including partner escalations and analysis of model behavior in real-world scenarios.
Evaluate, prototype, and recommend new ML infrastructure, tools, and data capabilities, partnering with DS to validate impact and support adoption.