Senior Engineer, Machine Learning

Job Description

Role Overview

We are looking for a highly skilled Machine Learning Engineer who can design, build, and own end-to-end ML systems in production. This role requires a strong blend of machine learning expertise, backend engineering, and full-stack development, with a focus on building reliable, scalable platforms used by leadership and critical business functions.

Key Responsibilities

• Design, develop, and maintain end-to-end machine learning pipelines, including data ingestion, training, evaluation, deployment, monitoring, and retraining.
• Build and own production-grade ML services that are reliable, scalable, and fault-tolerant.
• Architect and manage async workflows and API-driven systems for ML and data services.
• Integrate ML solutions into complex production environments and distributed systems.
• Design robust systems with a strong focus on failure modes, observability, and guardrails to ensure reliability.
• Develop internal analytical tools used by leadership and cross-functional teams for decision-making.
• Develop interactive internal ML tools and dashboards using Streamlit for model insights, monitoring, and experimentation.
• Experience with cloud platforms (AWS, GCP, Azure).
• Collaborate with data scientists and stakeholders to deliver impactful solutions.

Required Skills & Qualifications

Core Engineering Skills

• Strong proficiency in Python, SQL, and building RESTful APIs
• Experience with asynchronous programming and workflows
• Solid understanding of software engineering best practices: Version control (bitbucket), Unit and integration testing, Code quality and maintainability

Machine Learning & MLOps

• Build or integrate data ingestion pipelines (batch or streaming)
• Experience in performing EDA and understand the analysis.
• Proven experience managing the full ML lifecycle.
• Hands-on experience with MLOps practices and tools:
  • Experiment tracking
  • Model versioning
  • Automated training and deployment pipelines
  • CI/CD for ML systems

Systems, Infrastructure & Orchestration

• Experience building scalable and reliable ML systems in production
• Familiarity with:
  • Containerization (Docker)
  • Orchestration platforms (e.g., Kubernetes, Airflow, Prefect, Dagster)
  • Infrastructure as Code (IaC)
• Experience with distributed data processing systems (e.g., Spark)
• Understanding of workflow orchestration and scheduling for ML pipelines

Full Stack Development

• Experience developing end-to-end applications, including:
  • Backend pipelines and services
  • Frontend/UI components
• Hands-on experience building internal ML dashboards and tools using Streamlit
• Ability to create intuitive interfaces for monitoring models, exploring data, and enabling stakeholder interaction

Qualifications

Required Qualifications

• Master’s or PhD in Statistics, Data Science, Computer Science, or a related quantitative field.
• 3–4+ years of experience in data science or machine learning pipeline.
• Strong expertise in statistical analysis and machine learning techniques.
• Proficiency in:
  • Python (pandas, numpy, scikit-learn, statsmodels)
  • SQL
  • Data visualization tools
• Experience working with large-scale operational datasets.

Preferred Qualifications

• Experience working with Databricks or AzureML.
• Familiarity with big data technologies (Spark, PySpark).
• Experience working with cloud platforms (AWS, Azure, or GCP).
• Knowledge of MLOps practices and model deployment frameworks.

Additional Information

All your information will be kept confidential according to EEO guidelines.