Senior Backend Engineer

About The Position

We are seeking a highly skilled Engineer to join our innovative team focused on developing and optimizing next-generation engines.

In this role, you will be responsible for designing, enhancing, and scaling bioinformatics pipelines using state-of-the-art AI models to deliver high-quality mathematical models of human biology.

The ideal candidate will possess a strong background in backend and data engineering.

Responsibilities
Lead the design of scalable and maintainable software architectures for AI systems and data pipelines.
Take a major part in leading the designing, development, and optimization of high-scale pipelines and data infrastructure.
Make critical technical decisions regarding tools, frameworks, and methodologies to be used in projects.
Develop technical roadmaps and strategies to align with business objectives and drive technological advancements.
Work closely with data scientists, machine learning engineers, bioinformatics developers, and product managers to integrate algorithms into end-to-end AI solutions and workflows.
Provide guidance, mentorship, and training to junior developers, ensuring high code standards and execution excellence across the team.
Requirements
Relevant education or experience in Computer Science, Machine Learning, Mathematics, Statistics, or a relevant experience
6+ years of experience as a software engineer, with at least 1-2 years in a leadership or senior technical role
Proficiency in Python & R
Knowledge of data processing frameworks such as pandas
Proficiency in version control systems such as Git
Experience with SQL and NoSQL databases
Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) for model training and deployment
Familiarity with containerization technologies like Docker and Kubernetes
Understanding of computational methods for large-scale data
Experience in the development of high-data and compute-intensive systems
Demonstrated ability to optimize algorithms for scalability and performance in large-scale data environments
Experience working in multidisciplinary teams with data scientists, software engineers, and product managers
Strong communication skills to effectively convey complex technical concepts to both technical and non-technical audiences
Excellent problem-solving and communication skills
Advantages
Knowledge of data processing frameworks such as pandas
Experience with SQL and NoSQL databases
Familiarity with containerization technologies like Docker and Kubernetes.
Understanding of computational methods for large-scale data.
Experience with machine learning libraries and frameworks such as Pandas, Numpy, SciPy, TensorFlow, PyTorch, Keras, etc.
Experience with R and R libs like dplyr, caret, xgboost, Bioconductor, etc
Strong communication skills to effectively convey complex technical concepts to both technical and non-technical audiences.