As a founding Senior Machine Learning Engineer, you’ll play a central role in building the backbone of real-time machine learning systems that power identity verification and fraud detection. This is a high-impact position requiring deep technical ownership—from designing monitoring frameworks to enabling reproducible experimentation and ensuring models operate reliably in production.
What You'll Do
- Lead the development and evolution of real-time model monitoring systems, ensuring visibility into model behavior and performance across live environments.
- Design and maintain infrastructure for ML experimentation, model tracking, and version control to support full lifecycle transparency and reproducibility.
- Streamline the model development workflow by addressing tooling gaps, improving code quality, and accelerating iteration speed for data science teams.
- Act as the technical liaison between Data Science and Engineering, defining standards for integration, deployment, and operational handoffs.
- Optimize production model performance with a focus on latency, reliability, and maintainability under real-world conditions.
- Respond to performance anomalies and partner escalations with root cause analysis and targeted improvements.
- Research, prototype, and integrate new tools and platforms that enhance ML capabilities, working closely with data scientists to validate impact.
Requirements
- 5+ years of experience in software engineering or machine learning infrastructure roles, with a degree in Computer Science, Engineering, Mathematics, or a related field.
- Strong programming skills in Python and SQL, with fluency in Git and modern CI/CD practices.
- Hands-on experience with MLflow or similar model tracking platforms, and familiarity with observability tools such as Datadog, Prometheus, or Grafana.
- Proven track record building and maintaining ML pipelines for training, evaluation, and deployment.
- Experience deploying ML models as scalable services, with attention to performance and operational rigor.
- Proficiency with containerization (Docker), orchestration (Kubernetes), and workflow tools like Airflow, Dagster, or Prefect.
- Knowledge of model serving frameworks and deployment patterns in cloud environments, particularly AWS.
- Ability to rapidly prototype and deliver end-to-end ML solutions from concept to production.
- Adaptability in fast-changing technical environments with evolving requirements.
- Must be legally authorized to work in the United States and reside within the country.
Technical Stack
Python, SQL, MLflow, Datadog, Grafana, Prometheus, Airflow, Dagster, Prefect, Docker, Kubernetes, AWS, PostgreSQL, Git, CI/CD pipelines, GitHub Actions
Benefits
- Employer-paid health insurance for employees and dependents
- 401(k) plan with company match
- Flexible paid time off
- Company-wide in-person gatherings
- Home office stipend
- Additional perks available
Work Mode
This role supports a hybrid model across select U.S. and India locations, including Austin, San Francisco, New York City, Seattle, Los Angeles, Chicago, Gurugram (Delhi), and Bengaluru. The company operates digitally-first with strong cross-regional collaboration. While remote options exist, those near office hubs are encouraged to engage in person regularly. Some teams, including engineering in India, primarily work from the Gurugram office.
