Computational Engineer

EduRankAI; Kolkata District; In loco; INR 900.000 - 1.500.000; Tempo pieno

Descrizione del lavoro

EduRankAI is seeking an engineer/scientist to join the Department of Extreme-Scale, Nano & Fundamental Engineering, Division D08: Computer Science, AI & Scientific Computing. You will build research software, develop scalable simulation platforms, and ensure reproducibility with rigorous testing and provenance.

The role covers end-to-end work from framing problems to reporting results with explicit limits, using Python, C++, Julia, and Bash, and collaborating across divisions to advance

Competenze

• Fluent in Python and readable, testable code.
• Strong knowledge of data structures and algorithms.
• Experience with Linux and Git workflow.
• Ability to write tests for numerical code and explain tolerances.
• Ability to report baselines alongside models.

Mansioni

• Build research software that other divisions depend on, to a standard that survives handover.
• Build and maintain simulation platforms, and run at the size the science actually needs.
• Optimise scientific workloads with a measured before-and-after, never an asserted one.
• Develop AI models for scientific problems, always reported against a competent baseline.
• Build digital twins, and state plainly what each one does and does not represent.
• Create data pipelines with provenance, so a number can be traced to the run that produced it.
• Hold the reproducibility standard: seeds, environments, and a result that comes back the same.
• Maintain code quality and testing across the department, including in other divisions' repositories.
• Own a whole problem end to end: framing it, doing the work, and reporting the result with its limits.
• Review a colleague's work when asked, and say plainly when a result is not supported by its method.

Conoscenze

Python

C++

Julia

Linux

Git

Testing numerical code

Numerical methods

Data structures & algorithms

Bash

Documentation

Strumenti

Linux OS

Git

Descrizione del lavoro

The computation another division needs, made correct, fast enough, and reproducible.

Department of Extreme-Scale, Nano & Fundamental Engineering, Division D08: Computer Science, AI & Scientific Computing. This division exists to serve all the others. It builds the research software, makes simulations run at the sizes the science needs, develops AI models for scientific problems with honest baselines, and holds the line on reproducibility and code quality across the whole department. This position: The computation another division needs, made correct, fast enough, and reproducible. Scale range for this work: 10^-100 m to 10^100 m. Research classification: Computational. Computing quantities that cannot be obtained in closed form, from established physics. Evidential standing: Computational research. Computed from established physics. A computed number is a prediction, and it is only as good as its convergence, its error budget, and whatever data it can be checked against. A model that fits is not a model that predicts. Work from this division reports a held-out result and a competent classical or numerical baseline alongside every machine-learning claim, and states the domain a surrogate is valid over. A digital twin is a model of a system, not the system, and is described that way. Scale bands are a research classification, not a claim of experimental reach. Most of this span cannot be probed by any apparatus that exists: nothing below about 10^-19 m has been measured directly, and anything at 10^26 m or beyond is inferred from observation rather than engineered. Every opportunity states the kind of work it actually is. Level 3 (Engineer and Scientist). A whole problem end to end: framing it, doing the work, and reporting the result with its limits.

Responsibilities

• Build research software that other divisions depend on, to a standard that survives being handed over.
• Build and maintain simulation platforms, and make them run at the size the science actually needs.
• Optimise scientific workloads with a measured before-and-after, never an asserted one.
• Develop AI models for scientific problems, always reported against a competent baseline.
• Build digital twins, and state plainly what each one does and does not represent.
• Create data pipelines with provenance, so a number can be traced to the run that produced it.
• Hold the reproducibility standard: seeds, environments, and a result that comes back the same.
• Maintain code quality and testing across the department, including in other divisions' repositories.
• Own a whole problem end to end: framing it, doing the work, and reporting the result with its limits.
• Review a colleague's work when asked, and say plainly when a result is not supported by its method.

Skills

• Fluent in Python, and able to write code somebody else can read, test and extend.
• Data structures and algorithms to the level of choosing correctly and justifying the choice.
• Comfortable on Linux, and with Git as a working tool rather than a save button.
• Has written tests for numerical code and can explain what a tolerance in a test means.
• Numerical methods: can state the error behavior of a scheme they have implemented.
• Reports a baseline alongside every model result.
• Python
• C++
• Julia
• Bash