Staff/Senior Machine Learning Platform Engineer

Summary

We are seeking a seasoned Staff/Senior Machine Learning Platform Engineer to lead the design, development, and scaling of our end-to-end ML platform. You will play a critical role in empowering data scientists and ML engineers to build, train, deploy, and monitor machine learning models at scale. The role requires deep expertise in distributed systems, infrastructure automation, and machine learning workflows.

You will work closely with Data Scientists, MLOps engineers, Data Engineers, and Product Engineering teams in abstracting away the complexities, ensuring performance, and accelerating innovation throughout our ML initiatives.

Job Description

Responsibilities

• Architect and guide the design of a scalable and secure ML platform to support the entire ML lifecycle (data ingestion, feature engineering, model training, deployment, and monitoring).

• Design and implement infrastructure for training models, hyperparameter tuning, experiment tracking, and model registry.

• Orchestrate ML workflows using tools like Kubeflow, SageMaker, MLflow, or similar orchestration tools.

• Collaborate with cross-functional teams to define best practices for reproducible research, model versioning, governance, and CI/CD for ML.

• Collaborate with Data Engineers to facilitate building Data Pipelines for model ready datasets.

• Optimize performance of ML workloads across compute and storage layers using cloud-native and open-source solutions.

• Lead technical discussions, mentor junior engineers, and help set the technical vision for the ML platform roadmap.

• Ensure compliance with security, privacy, and regulatory requirements across the ML lifecycle.

Required Qualification

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

• 10+ years of software engineering experience, including 5+ years working on ML platforms or infrastructure.

• Experience in building large-scale distributed systems and microservices.

• Strong programming skills in Python, Go, or Java.

• Experience with containerization and orchestration (e.g., Docker, Kubernetes).

• Understanding of MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Databricks

• Cloud platform experience (AWS, GCP, or Azure).

• Experience using statistical learning algorithms such as GLM, XGBoost, and Random Forest to solve real world business problems.

• Deep understanding of neural network and transformer algorithms

Preferred Qualifications

• Real-time model inference and streaming ML pipelines experience.

• Deep knowledge of model governance, reproducibility, and monitoring.

• Understanding of model performance metrics and drift detection.

• Exposure to feature stores (Feast, Tecton), and workflow tools (Airflow, Argo).

• Familiarity with regulatory considerations (model auditability, interpretability, data privacy laws such as CCPA/GDPR).

• Experience working with real-time data pipelines (Kafka, Flink, Spark Structured Streaming).

• Experience using TeamCity and Terraform for infrastructure setup and CI/CD.

• Insurance industry or related experience such as banking and finance

What We Offer

• A chance to influence and build the ML platform used across the company.

• Collaborative and diverse team environment with opportunities to mentor and be mentored.

• Competitive compensation, equity, and benefits package.

• Support for continuous learning and development.
   

The US base salary range for this full-time position is $144,000 - $240,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

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