SDE Intern in AI Consciousness Systems Research

AI Consciousness Systems Researcher
Cehpoint AI — Experimental AI Research Division
Location: Remote
Employment Type: Full-time / Research Internship
Experience: 0–5 years
Department: AI Research & Advanced Systems
⚠️ IMPORTANT: ASSESSMENT-BASED HIRIN
G
Cehpoint AI follows an assessment-first hiring process for this position
.
Your degree, previous job title, college name, or years of experience are not the primary selection criteria
.
Candidates will be evaluated primarily through a technical research assessment designed to measure
:
- Research abilit
y- Analytical thinkin
g- AI/ML understandin
g- Experimental desig
n- Problem-solvin
g- Scientific reasonin
g- Ability to challenge assumption
s- Ability to develop and explain original idea
s
Candidates who demonstrate strong research ability through the assessment may be considered regardless of conventional academic or employment background
.
The assessment is an essential part of the selection process
.
About Cehpoint A
I
Cehpoint AI is exploring what comes after conventional chatbot and LLM architectures
.
We are researching systems involving machine memory, self-models, perception, introspection, attention, persistent identity, autonomous decision-making, internal state and emergent behaviour
.
We are investigating a difficult question
:
«Can computational systems develop measurable properties that resemble aspects of consciousness?
»
We are not looking for someone to simply build another chatbot
.
We want to experiment
.
The Rol
e
As an AI Consciousness Systems Researcher, you will investigate computational architectures inspired by cognitive science, neuroscience, artificial intelligence and complex systems
.
You will design experiments to study capabilities such as
:
- Persistent self-representatio
n- Internal-state modellin
g- Self-monitorin
g- Metacognitio
n- Introspectio
n- Memory continuit
y- Attention and selective perceptio
n- Goal persistenc
e- Self-evaluatio
n- Uncertainty awarenes
s- Environmental modellin
g- Autonomous information seekin
g
The objective is not to assume that an AI system is conscious
.
The objective is to develop experiments that allow us to investigate and measure these phenomena scientifically
.
Key Responsibilitie
s
Artificial Cognition Researc
h
Research computational models of
:
- Perceptio
n- Attentio
n- Memor
y- Working memor
y- Long-term memor
y- Self-model
s- Metacognitio
n- Decision-makin
g- Internal stat
e- Goal formatio
n- Learnin
g
AI Self-Model Experiment
s
Develop systems capable of maintaining computational representations of
:
“What am I?
”
“What do I know?
”
“What don't I know?
”
“What am I currently doing?
”
“Why did I make this decision?
”
AI Memory Architectur
e
Experiment with
:
- Episodic memor
y- Semantic memor
y- Working memor
y- Procedural memor
y- Associative memor
y- Memory compressio
n- Memory deca
y- Memory retrieva
l- Controlled forgettin
g
Metacognitio
n
Build systems capable of evaluating their own
:
- Decision
s- Confidenc
e- Knowledge gap
s- Error
s- Prediction
s- Goal
s
Autonomous A
I
Experiment with systems following a continuous loop
:
Observe → Remember → Evaluate → Question → Investigate → Decide → Act → Reflec
t
Research Assessmen
t
After the initial application review, shortlisted candidates will receive an AI Research Assessment
.
The assessment may require you to design or prototype an experimental system addressing a research problem such as
:
“Can an AI maintain a persistent computational self-model across multiple independent sessions?
”
You may be asked to provide
:
1. Research hypothesi
s2. Proposed architectur
e3. Experimental methodolog
y4. Baselin
e5. Evaluation metric
s6. Expected result
s7. Failure mode
s8. Potential false positive
s9. Prototype or implementation, where applicabl
e10. Technical explanation of your approac
h
What matters most
?
Not whether your answer matches our expected answer
.
We evaluate how you think
.
A candidate who challenges the premise with strong reasoning may perform better than someone who simply produces a technically impressive implementation
.
Selection Proces
s
Stage 1 — Applicatio
n
Submit your CV, portfolio/GitHub and relevant background. to hr@cehpoint.co.i
n
↓
Stage 2 — Research Assessmen
t
Complete the Cehpoint AI technical/research assessment
.
↓
Stage 3 — Technical Revie
w
Our team evaluates your
:
Reasoning → Research methodology → Technical depth → Originality → Experimental thinkin
g
↓
Stage 4 — Research Discussio
n
Selected candidates discuss their assessment, assumptions and proposed approach with the Cehpoint AI research team
.
↓
Stage 5 — Selectio
n
Candidates demonstrating the required research capability may receive an offer
.
Who Should Apply
?
You may be
:
- An AI/ML enginee
r- A software enginee
r- An independent AI researche
r- A studen
t- A recent graduat
e- A cognitive science researche
r- A neuroscience enthusias
t- A self-taught develope
r- An LLM researche
r- A robotics researche
r- Someone working on an unconventional AI projec
t
Traditional credentials are helpful, but demonstrated ability is what we primarily evaluate
.
Technical Backgroun
d
Useful experience includes
:
- Pytho
n- PyTorc
h- Transformer
s- LLM
s- RA
G- AI agent
s- Reinforcement learnin
g- Neural network
s- Multimodal A
I- Local model inferenc
e- Model fine-tunin
g- Memory architecture
s- Knowledge graph
s- Simulation environment
s
Bonu
s
- Cognitive scienc
e- Computational neuroscienc
e- Philosophy of min
d- Information theor
y- Artificial lif
e- Neuromorphic computin
g- Evolutionary algorithm
s- Complex system
s
The Question We Want You To Think Abou
t
“What evidence would convince you that an AI system is NOT conscious?
”
There is no predefined answer
.
We are interested in the quality of your reasoning, experimental design and ability to distinguish evidence from assumption
.
Cehpoint A
I
Don't apply because you think you have the right degree
.
Apply if you believe you can solve a problem that doesn't yet have a clear answer
.
Hiring is assessment-based
.
Your assessment demonstrates your ability
.

Work arrangement
Yes