Extreme-Scale, Nano & Fundamental Engineering
/Trusted operator
Computational Engineer
The computation another division needs, made correct, fast enough, and reproducible.
Compensation
Engagement
Full-Time
Permanent role. Full-time commitment. This is an on-site role at Kolkata. It is not remote and not hybrid.
Scope of role
Drive specific initiatives with minimal supervision. Deepen craft. Begin mentoring others.
Research classification
Computing quantities that cannot be obtained in closed form, from established physics. Reproducible calculations, convergence and error budgets, released code and datasets.
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.
Scale range 10^-100 m to 10^100 m
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. Scientific integrity is a condition of every role in this department. Assumptions are stated separately from conclusions. Uncertainty is reported. Negative and inconclusive results are written up, not discarded. Speculative work is labelled speculative, including when that makes it less impressive. Fabrication, falsification, and presenting a simulation as a measurement end an engagement here.
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.
01 — The role
Why this role exists at EduRankAI
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.
02 — The work
What you will own
- 01 Build research software that other divisions depend on, to a standard that survives being handed over.
- 02 Build and maintain simulation platforms, and make them run at the size the science actually needs.
- 03 Optimise scientific workloads with a measured before-and-after, never an asserted one.
- 04 Develop AI models for scientific problems, always reported against a competent baseline.
- 05 Build digital twins, and state plainly what each one does and does not represent.
- 06 Create data pipelines with provenance, so a number can be traced to the run that produced it.
- 07 Hold the reproducibility standard: seeds, environments, and a result that comes back the same.
- 08 Maintain code quality and testing across the department, including in other divisions' repositories.
- 09 Own a whole problem end to end: framing it, doing the work, and reporting the result with its limits.
- 10 Review a colleague's work when asked, and say plainly when a result is not supported by its method.
03 — The expertise
What we look for
04 — The bar
Who thrives here
- → Education: Master's degree or doctorate in a relevant discipline, or equivalent demonstrated research output.
- → Experience: 2 to 5 years of relevant work, or a doctorate in the field.
- → Scope of the role: A whole problem end to end: framing it, doing the work, and reporting the result with its limits.
- → Must have: Fluent in Python, and able to write code somebody else can read, test and extend. Also: Data structures and algorithms to the level of choosing correctly and justifying the choice.
- → Portfolio: at least one piece of work — code, a written result, a thesis chapter, a preprint — that somebody outside your institution can read and assess. It does not need to be published.
Terms of Engagement
How working time works at this level
A scheduled week: five days, eight hours a day, with two full rest days and latitude over when within the day you work.
Type
Mid-level
Working days
5 per week
Rest days
2 per week
Scheduled week
40 hrs
Per day
8h
Measured by
Objectives agreed at the start of each cycle. Hours recorded for compliance are never used as a performance score.
Where this stands legally
Within the 9-hour day and 48-hour week ceiling of the applicable state Shops and Establishments Act, with at least 24 consecutive hours of weekly rest and a break of at least 30 minutes after five hours of continuous work. Hours are recorded for statutory compliance only. Work beyond the scheduled week is agreed in advance and compensated with time off in lieu.
The full per-level model is published at Working Hours by Level.
05 — Hiring process
What to expect after you apply
- 01
Application review
Every application is read personally within five business days. We respond either way.
- 02
Take-home or live exercise
Role-specific. Time-boxed. Real problems we are actually working on, not invented puzzles.
- 03
Conversations
Deep technical and values conversations with the team you would join. No trick questions. No panel ambushes.
- 04
Offer or honest no
If yes: digital offer letter, signed in-portal, transparent terms. If no: written feedback if you want it.
The standard
What you deliver, and how it is judged
Deliverables
- Software with tests, a recorded environment, and documentation another division can act on.
- Performance work reported as a measured speed-up with the machine and problem size stated.
- Models reported with held-out metrics and a baseline, never with training metrics alone.
- Pipelines whose outputs carry provenance back to the run and the code version.
Evaluation criteria
- Does somebody else's checkout produce the same result.
- Is the baseline present, competent, and honestly reported.
- Is the performance claim measured rather than asserted.
- Would the code survive its author leaving.
Preferred, not required
Nothing in this list is a bar to applying.
Tools you would work in
Reports to: Computer Science, AI & Scientific Computing Lead
Works with: D01, D02, D03, D04, D05, D06, D07, D09, D10, D11, D12, D13, D14, D15
Before you start
What we will collect. What it costs. What we will not do with it.
We will collect
- Name, email, phone — Account + application updates. No marketing.
- Resume / portfolio link — Human review of your work.
- Date + place of birth — Identity verification only.
- Your written responses — Selection rubric. Read by humans.
- Government ID (later) — Anti-fraud at offer / interview stage. Not at signup.
We will never
- Sell your data
- Share with third-party recruiters
- Use for advertising
- Train models on it
- Send marketing email
Our situation
EduRankAI is a small, independent organization building long-term capabilities in educational intelligence, advanced AI systems, and research infrastructure. We take no advertiser money, no donations with strings attached, and no investor pressure on hiring decisions. Applying is free, and every application is read by a human — recruitment, technical, academic and leadership teams. It buys us the right to be honest.
Ready to apply?
We read every application personally. If you are the right person for this role — regardless of pedigree, background, or where you are based — you will hear back from us within five business days.
Apply through this page. You will be asked for your education, your experience, and links to work we can actually read — a repository, a write-up, a thesis chapter, a preprint. Send the piece of work you would defend, not the one with the best title. If a result in it turned out to be wrong, say so; we would rather read that than not know. Every application is read by a person. We assess applications on evidence of the work. We do not filter on institution, on age, on gender, on caste, on religion, on disability, or on where you are from. If any part of this process is inaccessible to you, tell us and we will change it for you.
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