Senior Software Engineer – Algorithm & ML Development

Overview

Senior Software Engineer role focused on Python-based algorithm and machine learning development for scientific and operational data, including migration of analytical workloads from AWS to Databricks.

What you'll do

• Design, develop, and maintain algorithms for processing, analyzing, and modeling scientific and operational data.
• Develop robust Python-based computational workflows using appropriate scientific computing, data analysis, and machine learning libraries.
• Analyze complex datasets to identify patterns, derive meaningful features, and develop predictive or diagnostic insights.
• Develop and optimize algorithms for areas such as signal processing, image analysis, anomaly detection, predictive analytics, and other data-driven applications.
• Evaluate algorithm performance through systematic testing, validation, benchmarking, and comparison against defined performance criteria.
• Collaborate with domain experts and data scientists to translate scientific and business problems into practical analytical and algorithmic solutions.
• Collaborate with data engineers to define data requirements and consume reliable, curated datasets without duplicating ingestion and ETL responsibilities.
• Develop reusable, modular, testable, and maintainable Python components for algorithmic and analytical workloads.
• Improve the performance and scalability of computational workloads, including the adoption of distributed processing where appropriate.
• Support the migration of existing algorithmic and analytical workloads from the current AWS-based data platform to Databricks.
• Refactor and adapt existing Python and analytical workloads to operate effectively with Databricks, Spark/PySpark, Delta-based datasets, and distributed compute where appropriate.
• Develop reusable features and feature-generation logic for experimentation, model training, batch inference, and production applications.
• Contribute to the adoption of structured ML lifecycle practices including experiment tracking, reproducibility, model validation, versioning, deployment, and monitoring.
• Work with platform and ML engineering teams to productionize algorithms and machine learning models through repeatable and automated workflows.
• Participate in technical design discussions, solution reviews, estimation, and implementation planning for algorithm and ML solutions.
• Apply software engineering practices including source control, automated testing, code review, CI/CD, documentation, and observability to algorithm and ML development.
• Mentor less experienced engineers and contribute to engineering standards and reusable development practices.

What you'll need

• 8–12 years of professional experience in software engineering, algorithm development, scientific computing, data science, machine learning, or related technical disciplines.
• Strong hands-on programming experience with Python and experience developing production-quality Python software.
• Strong experience with scientific computing and data analysis libraries such as NumPy, pandas, SciPy, or equivalent technologies.
• Experience designing and implementing data-processing, analytical, mathematical, or predictive algorithms.
• Experience working with complex datasets and developing data-driven models, features, or analytical workflows.
• Experience in one or more areas such as machine learning, predictive analytics, signal processing, image processing, time-series analysis, optimization, or statistical modeling.
• Good understanding of machine learning fundamentals including feature engineering, model training, validation, evaluation, and inference.
• Experience translating exploratory or prototype algorithms into maintainable and production-ready implementations.
• Good understanding of software engineering principles including modular design, object-oriented programming, testing, maintainability, and performance optimization.
• Experience working with cloud-based data and compute environments, preferably AWS.
• Working knowledge of SQL and experience accessing and analyzing structured datasets.
• Understanding of scalable or distributed data-processing concepts and the considerations involved in processing large datasets.
• Experience with source control systems such as Git.
• Understanding of CI/CD, automated testing, DevOps practices, and modern software development workflows.
• Ability to collaborate effectively with data engineers, data scientists, domain experts, platform engineers, and application-development teams.
• Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, Physics, Electronics, Engineering, or a related technical discipline.

Nice to have

• Hands-on experience with Apache Spark or PySpark for distributed data processing.
• Experience developing machine learning or analytical workloads using Databricks notebooks, jobs, or equivalent managed compute environments.
• Familiarity with Delta Lake and lakehouse-based data architectures.
• Experience with MLflow or equivalent tools for experiment tracking, model management, and ML lifecycle management.
• Experience developing and managing reusable features for machine learning, including exposure to feature stores or feature engineering frameworks.
• Familiarity with Unity Catalog or similar approaches to data, feature, and model governance.
• Experience productionizing machine learning models for batch or real-time inference.
• Understanding of MLOps practices including model versioning, reproducibility, automated testing, deployment, monitoring, and retraining workflows.
• Experience optimizing Python or Spark workloads for performance and scalability.
• Experience with containerization technologies such as Docker and deployment into cloud-based environments.
• Experience integrating analytical algorithms or ML models into APIs, digital services, applications, or embedded systems.
• Experience working with scientific instrumentation, engineering data, image data, sensor data, or other complex technical datasets.
• Experience working in industrial R&D, scientific, engineering, or product-oriented development environments.

Details

• Work schedule: standard Monday to Friday.
• Environmental conditions: office.
• Does not own core data ingestion or ETL pipelines; focuses on the analytical and algorithmic layer consuming curated data.