Machine Learning Engineer
At a glance
Mid-level Machine Learning Engineering role at EVB. Remote · contract.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and improve machine learning models in Python to meet project goals
- Prepare large datasets and use MongoDB to store and retrieve training and validation data
- Assess model results, tune hyperparameters and compare performance to guide improvements
- Connect data pipelines and preprocessing steps to support model training and inference
- Record experiments and share findings and recommendations with project contributors
What you bring
- You know Python and have substantial experience with a framework such as scikit-learn, TensorFlow or PyTorch
- You have used MongoDB to manage data in machine learning work
- You understand model metrics, feature design and data preprocessing
- You can document technical work clearly and collaborate as project needs change
Who this fits
This contractor role suits a machine learning engineer who can contribute hands-on model development, evaluation and data work. It is remote and calls for collaboration with other contributors.
From the employer
- Job Title: Machine Learning Engineer
- Job Type: Contractor
- Location: Remote
About the hiring company:
Our client is a rapidly growing, venture-backed AI company helping shape the next generation of intelligent systems. By combining world-class human expertise with advanced machine learning workflows, they enable leading AI organizations to build, evaluate, and improve cutting-edge models used across a wide range of industries.
The company works with highly accomplished professionals in fields such as software engineering, finance, healthcare, legal, operations, research, and other specialized domains. These experts contribute directly to the development of advanced AI systems by providing real-world knowledge, evaluations, feedback, and domain-specific judgment that help models reason more accurately and perform more effectively.
Leveraging a proprietary AI-driven talent assessment and matching platform, the organization identifies exceptional professionals globally and connects them with high-impact projects at the forefront of artificial intelligence.
Backed by more than $40 million in funding and supported by a rapidly expanding international network of experts, the company is building critical human intelligence infrastructure for the AI economy and creating meaningful opportunities for professionals to apply their expertise in entirely new ways.
Job Summary: In this role, engaging Machine Learning Engineers to contribute expertise to a dynamic customer project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input.
Scope of Work
- Design, develop, and refine machine learning models using Python and relevant libraries to address project objectives.
- Analyze large datasets and leverage MongoDB to manage and retrieve data efficiently for model training and validation.
- Collaborate with cross-functional contributors to identify areas for model improvement and implement robust solutions.
- Conduct thorough model evaluation, tuning hyperparameters, and benchmarking results to ensure optimal performance.
- Document methodologies, experiments, and outcomes to ensure transparent and repeatable workflows.
- Integrate data pipelines and preprocessing workflows to streamline training and inference processes.
- Deliver actionable insights and recommendations based on data-driven findings and machine learning outcomes.
Preferred Qualifications
- Demonstrated expertise with Python, including deep familiarity with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch.
- Hands-on experience with MongoDB for data manipulation, storage, and retrieval within machine learning projects.
- Strong problem-solving skills and a track record of delivering innovative ML solutions in real-world settings.
- Understanding of model evaluation metrics, feature engineering, and effective data preprocessing techniques.
- Background in deploying or operationalizing machine learning models in cloud or enterprise environments.
- Clear written documentation and communication skills for sharing technical findings and best practices.
- Ability to adapt quickly to evolving project requirements and contribute collaboratively in a remote setting.
A very attractive and competitive package is offered.