OBS Entropy Labs
Job Sequence
Role Type
Profession
Vacancy Advertised on
Onboarding Open Till
Location
Job Type
Expected Salary Range
Overview
The Machine Learning & Optimization Scientist will lead efforts to develop and optimize machine learning models with a strong focus on MLOps practices, including model management, automated deployment pipelines, and performance optimization. You will collaborate with interdisciplinary teams to create solutions that drive efficiencies in scientific research, engineering, and business operations through mathematically optimized AI systems.
This role will emphasize mathematical optimization, leveraging advanced algorithms to enhance model performance and operational efficiency, while ensuring scalability in both research and production environments.
Key Responsibilities
1. Mathematical Optimization for Model Efficiency
Design and implement mathematical optimization frameworks to improve the performance and efficiency of machine learning models.
Develop optimization strategies for hyperparameter tuning, feature selection, and model selection in complex, large-scale datasets.
Focus on efficient resource utilization and optimization of computational tasks to streamline workflows and reduce bottlenecks.
2. MLOps and Automation
Develop MLOps pipelines for automating machine learning workflows, including model training, deployment, monitoring, and version control.
Optimize data preparation, model training, and inference stages to ensure seamless integration into scalable production systems.
Implement infrastructure for monitoring model performance, ensuring model quality, and reducing downtime in deployment.
3. Advanced Mathematical Modeling
Build mathematical models to represent complex systems for predictive analysis, decision-making, and optimization tasks.
Use stochastic programming, integer programming, and constraint optimization techniques to solve real-world challenges.
Research and develop solutions to mathematically capture uncertainty and improve the robustness of machine learning systems.
4. Interdisciplinary Collaboration and Optimization Solutions
Collaborate with domain experts across fields such as physics, chemistry, biology, engineering, and data science to design and optimize solutions.
Apply optimization techniques to solve problems related to supply chain management, resource allocation, computational efficiency, and more.
Develop innovative solutions that integrate mathematical optimization with machine learning to enhance the accuracy and efficiency of data-driven models.
5. Performance Enhancement and Scalability
Implement strategies for improving the scalability and performance of machine learning models in high-performance computing (HPC) environments.
Optimize workflows to ensure low-latency responses and efficient resource allocation across cloud and on-premise infrastructure.
Drive research and development of efficient algorithms for parallel and distributed computing, focusing on mathematical efficiency and accuracy.
6. Publication and Knowledge Dissemination
Publish research findings in high-impact journals and present findings at scientific conferences and industry forums.
Actively contribute to the MLOps and mathematical optimization community through workshops, collaborative research, and technical discussions.
Ideal Candidate Profile
Educational Background: Ph.D. in Computer Science, Applied Mathematics, Operations Research, Data Science, or a related field, with a focus on optimization and machine learning.
Experience: Proven expertise in mathematical optimization, machine learning, and MLOps, with a strong emphasis on model efficiency, scalability, and performance.
Technical Skills: Strong proficiency in optimization algorithms, machine learning frameworks, and mathematical modeling.
Key Focus Areas
Mathematical Optimization: Developing optimization techniques for efficient resource utilization and performance enhancement in machine learning models.
MLOps: Automating and optimizing machine learning workflows, including model deployment, monitoring, and performance tuning.
Interdisciplinary Optimization: Collaborating with domain experts to develop mathematical solutions for scientific and industrial challenges.
Scalable and Efficient Systems: Designing mathematical frameworks to improve scalability and efficiency of machine learning models in production environments.
Technical Skills
Mathematical Optimization:
Strong expertise in optimization techniques such as linear programming, integer programming, constraint optimization, and stochastic programming.
Machine Learning & MLOps:
Experience developing and managing machine learning workflows using MLOps practices.
Proficiency in machine learning frameworks like TensorFlow, PyTorch, or scikit-learn with a focus on efficiency enhancements.
Programming:
Proficiency in Python, R, Julia, or similar languages with a focus on mathematical modeling and data manipulation.
Performance and Scalability:
Experience designing and optimizing machine learning models for large-scale, distributed systems.
Research and Analytical Skills
Strong ability to conceptualize and implement mathematical and machine learning models to address complex, real-world challenges.
Excellent problem-solving skills for handling large datasets and optimizing computational tasks efficiently.
Preferred Qualifications
Domain-Specific Expertise
Experience applying machine learning and mathematical optimization to scientific research fields such as materials science, bioinformatics, or energy systems.
Publication and Collaboration
Proven track record of publishing research in journals and conferences related to MLOps, mathematical optimization, and machine learning.
Experience collaborating with interdisciplinary teams in developing scalable and efficient solutions.
Project Leadership
Experience leading MLOps initiatives and managing projects involving mathematical optimization and machine learning system development.
Demonstrated ability to mentor junior researchers and contribute to research innovation.
Responsibilities
As a Machine Learning & Optimization Scientist at OBS MIRD Entropy Labs, you will be responsible for developing and implementing advanced machine learning models and mathematical optimization techniques to drive efficiency and scalability in real-world applications. Below are the detailed technical responsibilities for this position:
Technical Responsibilities
1. Mathematical Optimization & Efficiency Enhancement
Design, implement, and optimize mathematical models to improve the efficiency and accuracy of machine learning systems.
Develop and apply optimization algorithms (e.g., linear programming, integer programming, stochastic programming) for hyperparameter tuning, resource allocation, and decision-making.
Enhance the mathematical foundations of models to handle constraints, uncertainties, and multi-objective optimization challenges.
2. MLOps and Automated Machine Learning
Build and optimize MLOps pipelines for the automated training, deployment, monitoring, and scaling of machine learning models.
Implement efficient data pipelines and model version control systems to manage large datasets and track model performance.
Ensure the reliability and reproducibility of machine learning models in production environments.
3. Machine Learning Model Development
Develop and fine-tune machine learning models using supervised, unsupervised, and reinforcement learning techniques.
Design deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models for various scientific and industrial applications.
Optimize computational efficiency for large-scale datasets and parallel computing systems.
4. Interdisciplinary Collaboration and Optimization
Collaborate with domain experts from fields such as physics, chemistry, engineering, and healthcare to design and implement optimization and machine learning solutions.
Solve complex, real-world problems by applying optimization techniques to model physical phenomena, financial systems, and complex data structures.
Develop custom optimization models to enhance performance in resource allocation, supply chain management, and predictive analytics.
5. Performance Tuning and Scalability
Conduct performance optimization of machine learning models to ensure real-time and high-throughput capabilities.
Optimize machine learning algorithms for distributed and parallel computing environments to improve scalability across cloud-based and on-premise infrastructure.
Implement methods for reducing model latency, handling large datasets, and managing high-dimensional data efficiently.
6. Research and Innovation
Lead cutting-edge research on mathematical optimization and machine learning advancements for scientific and commercial applications.
Publish research in high-impact journals and participate in conferences to present novel approaches for machine learning and optimization.
Continuously explore and evaluate emerging techniques in machine learning and optimization to incorporate them into production-level systems.
Qualifications
Educational Background
Academic Profile in Computer Science, Applied Mathematics, Operations Research, Data Science, or a related field with a strong focus on mathematical optimization and machine learning.
Technical Skills
Mathematical Optimization:
Strong expertise in optimization techniques such as linear programming, integer programming, stochastic optimization, and constraint-based methods.
Machine Learning & Deep Learning:
Proficiency in developing machine learning models using frameworks like TensorFlow, PyTorch, or scikit-learn.
Knowledge of advanced machine learning techniques including deep reinforcement learning, transfer learning, and generative models.
MLOps:
Experience in building MLOps pipelines for automated machine learning model management, including model training, deployment, and monitoring.
Proficiency in integrating MLOps tools such as Kubeflow, MLFlow, and Apache Airflow.
Programming:
Proficiency in Python, R, Julia, or similar languages for machine learning and mathematical optimization.
Performance & Scalability
Expertise in optimizing models for distributed computing systems, cloud-based deployments, and high-performance computing (HPC) environments.
Preferred Qualifications
Domain-Specific Expertise
Experience in applying machine learning and mathematical optimization to scientific domains such as computational chemistry, material sciences, computational biology, or financial modeling.
Publication and Collaboration
Proven track record of publishing research in mathematical optimization and machine learning journals or conferences.
Experience working with interdisciplinary teams to create innovative solutions for complex scientific or business challenges.
Project Leadership
Experience leading MLOps initiatives and managing teams working on mathematical optimization and machine learning system development.
Ability to mentor junior researchers and contribute to the research and development of advanced machine learning and optimization methodologies.
Special Domain Requirements
In addition to the core technical responsibilities and qualifications, the Machine Learning & Optimization Scientist position at OBS MIRD Entropy Labs includes specific domain requirements tailored to drive innovation in specialized scientific and industrial applications. Below are the additional special domain requirements:
Special Domain Requirements
1. Scientific Data Analysis and Mathematical Modeling
Experience in applying machine learning and optimization techniques to scientific research domains such as materials science, physics, chemistry, or biological sciences.
Ability to develop mathematical models that capture complex physical phenomena, biological processes, and other domain-specific challenges.
2. High-Performance Computing (HPC) and Simulation
Proficiency in using mathematical optimization and machine learning for simulations in high-performance computing environments.
Expertise in handling large-scale datasets, parallel processing, and distributed computing to solve real-time scientific problems efficiently.
3. Domain-Specific Optimization Challenges
Strong background in solving optimization challenges specific to areas like drug discovery, climate modeling, quantum chemistry, and industrial optimization (e.g., supply chain management, logistics, manufacturing).
Ability to design and implement tailored optimization strategies for unique scientific and industrial needs.
4. Time-Series and Temporal Data Analysis
Experience in applying machine learning techniques to analyze time-series data and optimizing models for predictive accuracy in fields like finance, healthcare, and industrial monitoring.
Knowledge of causal inference, state space models, and anomaly detection in temporal datasets.
5. Multi-Objective and Constraint Optimization
Proficiency in handling multi-objective optimization problems, especially in scientific and engineering domains where trade-offs between different objectives must be optimized.
Expertise in constraint-based optimization, ensuring that solutions are feasible, efficient, and robust.
6. Interdisciplinary Collaboration with Scientific Experts
Experience working with scientists from interdisciplinary teams to co-develop machine learning and optimization models tailored to specific scientific challenges.
Strong understanding of domain-specific nuances in collaborating with experts across various research fields.
These special domain requirements ensure that the Machine Learning & Optimization Scientist is well-equipped to address the unique challenges faced in specialized scientific and industrial domains, driving innovation and delivering scalable, efficient, and high-performance solutions.
