Unknown Company
9 hours ago
ML Solutions Engineer
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Company information
- Company
- Unknown Company
- Location
- Full time Romania
- Posted
- 9 hours ago
Job description
Ideal Candidate
3+ years of professional experience building and deploying ML systems in production.
Strong software engineering skills and hands-on experience with Python, PyTorch, scikit-learn, Pandas, GitHub and Docker.
Experience building cloud-based ML and data pipelines using serverless applications and/or workflow orchestration tools such as Cloud Run, Airflow or Argo.
Experience taking ML models from research or prototype stage to deployed production solutions.
Strong understanding of professional engineering practices, including version control, code review, automated testing, CI/CD and containerization.
Experience independently owning complex technical projects, deliverables and deadlines.
Extensive day-to-day experience with generative coding agents such as Claude Code.
Strong communication skills and ability to work across research, engineering, product and customer-facing teams.
Comfortable working independently in an ambiguous, fast-moving environment.
Experience with geospatial data, remote sensing, or earth observation, including tools such as GeoPandas or GDAL, is a plus.
Experience deploying computer vision models, such as image segmentation or object detection, at production scale is a plus.
Experience with distributed computing infrastructure and enterprise SaaS products, particularly on Google Cloud Platform, is a plus.
Experience combining heterogeneous data sources, such as sensor, survey, and event data, is a plus.
Working knowledge of C++ is a plus.
Strong software engineering skills and hands-on experience with Python, PyTorch, scikit-learn, Pandas, GitHub and Docker.
Experience building cloud-based ML and data pipelines using serverless applications and/or workflow orchestration tools such as Cloud Run, Airflow or Argo.
Experience taking ML models from research or prototype stage to deployed production solutions.
Strong understanding of professional engineering practices, including version control, code review, automated testing, CI/CD and containerization.
Experience independently owning complex technical projects, deliverables and deadlines.
Extensive day-to-day experience with generative coding agents such as Claude Code.
Strong communication skills and ability to work across research, engineering, product and customer-facing teams.
Comfortable working independently in an ambiguous, fast-moving environment.
Experience with geospatial data, remote sensing, or earth observation, including tools such as GeoPandas or GDAL, is a plus.
Experience deploying computer vision models, such as image segmentation or object detection, at production scale is a plus.
Experience with distributed computing infrastructure and enterprise SaaS products, particularly on Google Cloud Platform, is a plus.
Experience combining heterogeneous data sources, such as sensor, survey, and event data, is a plus.
Working knowledge of C++ is a plus.
Job Description
We are looking for an ML Solutions Engineer to join a team building AI-powered solutions that turn complex real-world data into actionable insights. Our customer develops technology to understand changes in the physical world, combining satellite imagery and other data sources to support decisions across infrastructure, population, economic activity, and related areas. In this hands-on role, you will work at the intersection of AI research, software engineering, and product delivery, turning promising ML models into reliable production systems.
Work with the AI Research team to turn new ML models and methods, including computer vision and satellite imagery models, into reliable, production-ready solutions.
Build, train, fine-tune and evaluate ML models and application-specific datasets, ensuring performance and output quality meet customer requirements.
Build and operate scalable end-to-end ML and data pipelines, integrating multiple models, processing steps and large heterogeneous datasets.
Design and deploy solutions on cloud infrastructure, primarily Google Cloud, using serverless services and workflow orchestration.
Write clean, tested and maintainable production code, collaborating closely with Software Engineering, AI Research and Product & Solutions teams.
Develop validation and QA processes, benchmark model outputs and ensure solutions are robust before customer delivery.
Take ownership of complex projects from scoping to delivery, including milestones, timelines, documentation and technical handover.
Act as a technical bridge between research, product and customer needs, helping assess which research capabilities are ready for production.
Use AI coding agents such as Claude Code as part of the daily development workflow.
EU flexible schedule with PST overlap preferred (18-20 EEST)
Short Bytex HR introductory discussion;
1-2h Bytex technical discussion with one of our senior engineers;
1-2h technical discussion with the customer
Offer presentation.
Work with the AI Research team to turn new ML models and methods, including computer vision and satellite imagery models, into reliable, production-ready solutions.
Build, train, fine-tune and evaluate ML models and application-specific datasets, ensuring performance and output quality meet customer requirements.
Build and operate scalable end-to-end ML and data pipelines, integrating multiple models, processing steps and large heterogeneous datasets.
Design and deploy solutions on cloud infrastructure, primarily Google Cloud, using serverless services and workflow orchestration.
Write clean, tested and maintainable production code, collaborating closely with Software Engineering, AI Research and Product & Solutions teams.
Develop validation and QA processes, benchmark model outputs and ensure solutions are robust before customer delivery.
Take ownership of complex projects from scoping to delivery, including milestones, timelines, documentation and technical handover.
Act as a technical bridge between research, product and customer needs, helping assess which research capabilities are ready for production.
Use AI coding agents such as Claude Code as part of the daily development workflow.
EU flexible schedule with PST overlap preferred (18-20 EEST)
Short Bytex HR introductory discussion;
1-2h Bytex technical discussion with one of our senior engineers;
1-2h technical discussion with the customer
Offer presentation.
Required skills
- docker
- production
- documentation
- automated testing
- ci/cd
- communication skills
- github
- python
- pytorch
- software engineering
- cloud
- airflow
- research
- google cloud platform
- c++
- version control
- cloud infrastructure
- scalable
- performance
- distributed computing
- claude code
- code review
- timelines
- benchmarking
- containerization
- data pipelines
- google cloud
- computer vision
- geospatial data
- pandas
- scikit-learn
- customer needs
- satellite imagery
- earth observation
- cloud run
- prototype
- qa processes
- enterprise saas
- maintainable code
- technical projects
- survey data
- end-to-end
- remote sensing
- product delivery
- ml pipelines
- ai research
- customer delivery
- workflow orchestration
- milestones
- technical bridge
- serverless applications
- project scoping
- ml models
- research capabilities
- argo
- datasets
- gdal
- sensor data
- ml systems
- object detection
- event data
- production-ready solutions
- geopandas
- serverless services
- tested code
- robust solutions
- image segmentation
- output quality
- eest
- technical handover
- fine-tune
- deployed solutions
- generative coding agents
- evaluate ml models
- heterogeneous datasets
- daily development workflow
- eu flexible schedule
- pst overlap
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