Technical Lead - Materials Informatics Software

Technical Lead, Materials Informatics Software

About the Role :

We are seeking a highly skilled and experienced Technical Lead to spearhead the development of advanced software solutions for materials discovery, characterization, and simulation. This role is crucial for driving the technical vision, architecture, and implementation of robust and scalable platforms that empower scientific innovation in materials science. You will lead a dedicated team of software engineers, focusing on technical excellence, mentorship, and hands-on contribution to the codebase.

Technical Responsibilities :

Architectural Design & Strategy :

- Define and evolve the technical architecture for complex materials informatics platforms, ensuring scalability, reliability, and security.

- Make critical technical decisions regarding system design, technology stack, and integration patterns.

- Evaluate and recommend new technologies, tools, and frameworks to enhance development velocity and product capabilities.

Software Development Leadership :

- Provide technical guidance and leadership to a team of software engineers, fostering a culture of technical rigor and innovation.

- Lead the design and implementation of core software components, APIs, and data models.

- Conduct comprehensive code reviews, ensuring adherence to coding standards, best practices, and performance optimization.

- Debug complex technical issues across various system layers and environments.

Coding & Implementation :

- Actively contribute to the codebase, writing high-quality, maintainable, and well-tested code.

- Develop sophisticated algorithms and data structures for processing, analyzing, and visualizing large-scale materials data.

- Implement interfaces and integrations with scientific computing libraries, materials simulation software (e.g., DFT, MD), and experimental data sources.

Data Management & Analytics :

- Design and implement robust data pipelines for ingesting, transforming, and managing diverse materials datasets.

- Optimize database schemas and queries for performance and scalability in materials data storage.

- Develop and integrate machine learning models for predictive materials science, property prediction, and novel materials design.

DevOps & Infrastructure :

- Oversee the implementation of continuous integration, continuous delivery (CI/CD) pipelines for software deployments.

- Work with cloud infrastructure (IaaS, PaaS) to ensure optimal resource utilization and system performance.

- Implement monitoring, logging, and alerting solutions to maintain system health and identify technical bottlenecks.

Required Technical Skills

Programming Languages :

- Expert proficiency in Python (including libraries like NumPy, Pandas, SciPy, scikit-learn).

- Strong proficiency in at least one additional language like C++, Java, or Go.

Software Engineering Principles :

- Deep understanding of software design patterns, data structures, and algorithms.

- Experience with microservices architecture and API design (RESTful, GraphQL).

- Strong command of version control systems (Git) and collaborative development workflows.

- Solid grasp of testing methodologies (unit, integration, end-to-end testing) and test-driven development (TDD).

Cloud & Distributed Systems :

- Extensive experience with at least one major cloud platform (AWS, Azure, or GCP), including compute, storage, and networking services.

- Hands-on experience with containerization technologies (Docker) and orchestration (Kubernetes).

- Familiarity with distributed computing concepts and technologies.

Databases & Data Management :

- Expertise in relational databases (e.g., PostgreSQL, MySQL) including advanced SQL, schema design, and performance tuning.

- Experience with NoSQL databases (e.g., MongoDB, Cassandra) for flexible data modeling.

- Understanding of data warehousing, ETL/ELT processes, and data governance best practices.

Machine Learning & Data Science (Contextual to Materials) :

- Practical experience applying machine learning techniques (e.g., regression, classification, clustering, deep learning) to scientific or engineering datasets.

- Familiarity with ML frameworks such as TensorFlow or PyTorch.

- Knowledge of data visualization tools and techniques (e.g., Matplotlib, Plotly, D3.js).

Materials Informatics & Scientific Computing :

- Experience with scientific computing libraries and numerical methods relevant to materials science.

- Understanding of materials data formats (e.g., CIF, VASP OUTCAR, JSON, HDF5).

- Familiarity with open-source materials science toolkits (e.g., ASE, pymatgen) is highly advantageous.

Preferred Technical Skills :

- Experience with High-Performance Computing (HPC) environments and parallel programming (MPI, OpenMP, CUDA).

- Knowledge of graph databases (e.g., Neo4j) for materials relationship modeling.

- Experience building interactive web applications for data exploration (e.g., React, Angular, Vue.js).

- Contributions to open-source scientific or materials software projects.

- Familiarity with specific materials simulation software packages and their output