Fingerprint empowers developers to stop online fraud at the source.
We work on turning radical new ideas in the fraud detection space into reality. Our products are developer-focused and our clients range from solo developers to publicly traded companies. We are a globally dispersed, 100% remote company with a strong open-source focus. Our flagship open-source project is FingerprintJS (20K stars on GitHub).
We have raised $77M and are backed by Craft Ventures (previously invested in Tesla, Facebook, Airbnb ), Nexus Venture Partners (previously invested in Postman, Apollo.io, MinIO, Druva) and Uncorrelated Ventures (previously invested in Redis, Rollbar & Gradle).
We have noticed a rise in recruiting impersonations across the industry, where scammers attempt to access candidates' personal and financial information through fake interviews and offers. All Fingerprint recruiting email communications will always come from the @fingerprint.com domain. Any outreach claiming to be from Fingerprint via other sources should be ignored.
We are seeking a Senior ML Engineer to join our Identification team. In this role, you will focus on designing, building, and maintaining production-grade ML solutions and infrastructure that power our fraud detection solutions.
You will collaborate closely with other team members to architect solutions that are reliable, scalable, and efficient. You will own features from concept to deployment and ensure seamless integration with other components in our platform.
Types of Projects and Impact:- Take AI & ML applications from prototype to production, partnering closely with Data Scientists and cross-functional teams to ensure robust and performant deployment of machine learning solutions
- Lead development for ML systems: Design, build, and maintain production-grade ML systems, with a focus on performance, scalability, and maintainability
- Architect end-to-end ML infrastructure: Own the full lifecycle of ML solutions — from feature engineering and data pipelines to model serving, CI/CD, observability, and retraining
- Collaborate across teams: Work closely with data scientists, data engineers, platform teams, and business stakeholders to deliver solutions that align with product and business needs
- Champion MLOps best practices: Establish & maintain infrastructure/tooling for versioning, experimentation, testing, deployment, and monitoring of ML models
- Enable reproducibility and scale: Develop reusable components, templates, and automation to scale ML development across use cases and teams
- This role includes participation in a shared on-call rotation. The schedule will be communicated in advance, and we strive to balance coverage equitably while minimizing off-hours disruptions.
- BS/MS in Computer Science, Data Science, or a related field, or equivalent work experience
- 5+ years of experience as an ML Engineer
- Experience establishing and driving best practices for ML/MLOps in a growing technology organization
- Strong understanding of core ML concepts including supervised and unsupervised learning, model evaluation, and feature engineering
- Hands-on experience with modern ML frameworks such as CatBoost, LightGBM, TensorFlow, or PyTorch, and with large-scale data processing and transformation pipelines for training and serving models
- Experience deploying models to cloud platforms such as AWS, GCP, or Azure, using tools like SageMaker, Vertex AI, or Azure ML.
- Experience leveraging containerization and orchestration technologies such as Docker and Kubernetes
- Experience with CI/CD pipelines and MLOps tooling (e.g., MLflow, Feast, Weights & Biases).
- Ability to thrive in ambiguous environments where you get to work directly with stakeholders with minimal guidance and direction
- Proficient in English for clear communication in a global, remote team
- Experience working with GoLang or similar languages
- Experience working with Vector Databases such as Pinecone, Qdrant or similar technologies
- Practical experience with analytical storage systems like ClickHouse, Snowflake, BigQuery, Redshift, or Databricks.
- Experience with data transformation frameworks like dbt or other data pipeline tools.
- Familiarity with data visualization tools such as Apache Superset, Tableau, or Looker.
- Backend development: Python, GoLang
- ML frameworks: CatBoost, PyTorch
- Cloud platforms: AWS
- Data analytics/processing: ClickHouse, dbt, Apache Superset.
We have noticed a rise in recruiting impersonations across the industry, where scammers attempt to access candidates' personal and financial information through fake interviews and offers. All Fingerprint recruiting email communications will always come from the @fingerprint.com domain. Any outreach claiming to be from Fingerprint via other sources should be ignored.
Offers vary depending on, but not limited to, relevant experience, education, certifications/licenses, skills, training, and market conditions.
Due to regulatory and security reasons, there’s a small number of countries where we cannot have Fingerprint teammates based. Additionally, because Fingerprint is an all-remote company and people can join our workforce from almost any country, we do not sponsor visas. Fingerprint teammates need to be authorized to work from their home location.
We are dedicated to creating an inclusive work environment for everyone. We embrace and celebrate the unique experiences, perspectives and cultural backgrounds that each employee brings to our workplace. Fingerprint strives to foster an environment where our employees feel respected, valued and empowered, and our team members are at the forefront in helping us promote and sustain an inclusive workplace. We highly encourage people from underrepresented groups in tech to apply.
If you are applying as a resident of California, please read our CCPA notice here
If you are applying as a resident of the EU, please read our GDPR notice here
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