Scientific AI Lead

EduRankAI; Kolkata District; In loco; INR 1.800.000 - 2.800.000; Tempo pieno

Descrizione del lavoro

EduRankAI in Kolkata invites a lead-level AI research engineer to build scalable research software, simulations, and data pipelines with explicit baselines and reproducible results.

You will develop AI models for scientific problems, ensure code quality, and recruit/mentor team members while maintaining transparency about what each model represents. Fluency in Python, C++, Julia, Bash and strong Linux/Git skills are essential.

Competenze

• 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 behaviour of a scheme they have implemented.
• Reports a baseline alongside every model result.

Mansioni

• 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.
• Decide what this area works on, who works on it, and the standard the work is held to.
• Be accountable for the honesty of what leaves the division, including under time pressure.
• Recruit, develop and retain the people the programme needs.

Conoscenze

Python

C++

Julia

Bash

Linux

Git

Algorithms

Testing

Numerical methods

Baseline reporting

Strumenti

Git

Linux

Bash

Descrizione del lavoro

What this division builds for the others, who builds it, and the rule that no model ships without its baseline.

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: What this division builds for the others, who builds it, and the rule that no model ships without its baseline. Scale range for this work: 10^-100 m to 10^100 m. Research classification: Applied engineering. Engineering to a specification, on established science, for something that has to work. Evidential standing: Established science. Rests on settled physics and engineering. The work applies it; it is not what is under test here. 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 6 (Lead). A research programme or an engineering area: what gets worked on, by whom, and to what standard.

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.
• Decide what this area works on, who works on it, and the standard the work is held to.
• Be accountable for the honesty of what leaves the division, including under time pressure.
• Recruit, develop and retain the people the programme needs.

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 behaviour of a scheme they have implemented.
• Reports a baseline alongside every model result.
• Python
• C++
• Julia
• Bash