Staff SW Engineer, Machine Learning

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Staff SW Engineer, Machine Learning in the United States. This role sits at the intersection of advanced machine learning, geospatial intelligence, and production-scale software engineering, focused on transforming satellite and remote sensing data into actionable intelligence.

Accountabilities

• Design, develop, and deploy machine learning solutions for satellite imagery and geospatial intelligence applications, leveraging both classical and deep learning techniques.
• Build and optimize production-grade ML models and pipelines in Python, integrating them into large-scale analytics systems.
• Conduct applied research in computer vision, time series analysis, predictive modeling, and related domains to solve domain-specific challenges.
• Develop and refine algorithms, loss functions, and experimental frameworks, including training, evaluation, and performance analysis of models.
• Collaborate with infrastructure and ML engineering teams to ensure scalable, robust, and efficient deployment of analytics systems.
• Translate research concepts from academic literature into production-ready implementations, including experimentation and validation.
• Participate in technical discussions, product strategy alignment, and cross-functional collaboration with stakeholders and leadership.
• Ensure high-quality code delivery, including testing, documentation, and maintainability for long-term system reliability.

Requirements

• 8 years of experience in machine learning engineering, data science, or related software engineering roles.
• Bachelor's degree or higher in Computer Science, Mathematics, Physics, Statistics, or a related quantitative field.
• Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, Keras, or scikit-learn.
• Proven experience developing and deploying machine learning models in production environments.
• Deep understanding of supervised and unsupervised learning, deep learning architectures, and predictive modeling techniques.
• Experience implementing algorithms from research papers and translating them into working systems.
• Strong background in working with large datasets, including preprocessing, statistical analysis, and visualization using tools like Pandas and NumPy.
• Familiarity with geospatial, remote sensing, or satellite imagery data is highly desirable.
• Strong communication skills, with the ability to explain complex technical concepts to technical and non-technical audiences.
• Nice to have: experience with MLOps tools (MLflow, Kubeflow, W&B), Kubernetes, AWS, or geospatial libraries (GDAL, Rasterio, Shapely).
• Nice to have: exposure to tracking/motion detection, maritime analytics, or asynchronous cloud processing systems.

Benefits

• Competitive compensation package aligned with experience and location, including base salary and additional benefits.
• Comprehensive health coverage including medical, dental, vision, life, and disability insurance.
• 401(k) retirement plan with employer matching contributions.
• Flexible PTO policy, paid holidays, parental leave, and volunteer time off.
• Employee Stock Purchase Program and additional financial wellness benefits.
• Professional development support, training opportunities, and conference participation.
• Fully remote work flexibility within the United States.
• Access to cutting-edge ML infrastructure, geospatial datasets, and high-impact mission-driven projects.

How Jobgether Works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

Data Privacy Notice

By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws.