The residency program identifies top-tier AI talent through practical application rather than traditional testing. Participants operate within a simulated, high-velocity AI laboratory environment where technical proficiency, collaboration, and professional ownership are evaluated through real-time performance.
This immersive environment mirrors an industry startup. Residents resolve complex machine learning challenges, navigate technical ambiguity, and develop scalable solutions amid evolving project requirements.
The residency is designed as a two-phase selection program with full transparency regarding evaluation and advancement.
The program begins with an
initial cohort of 20 participants.
Phase 1
consists of an intensive four-week residency where participants are evaluated on technical performance, collaboration, adaptability, communication, and engineering discipline. At the conclusion of Week 4, selected participants will advance to Phase 2.
Phase 2
consists of an additional four-week advanced residency focused on production-level engineering, operational collaboration, communication, mentorship, and long-term growth potential. Final full-time and internship offers will be determined at the conclusion of Week 8.
Participants should expect
a minimum commitment of 30 hours per week
and are expected to be
at the office on Wednesdays
, although actual workload may exceed this depending on project complexity and team responsibilities.
The program also includes:
Weekly lectures and technical training sessions led by the engineering team.
Dedicated mentors assigned to monitor and guide resident progress throughout the program.
Weekly team gatherings and discussion sessions covering AGI research, emerging AI developments, engineering trends, and broader industry topics.
Upon completion, high-performing residents will be considered for permanent internship or full-time opportunities within the company.
Program Timeline
Registration Opens: Tuesday, June 2
Application Deadline: Saturday, June 13
Interview & Selection Period: J une 15 – June 19
Final Cohort Announcement: Monday, June 22
Phase 1 Residency Duration: June 29 – July 24
Phase 1 Evaluation & Selection: End of Week 4
Phase 2 Residency Duration: July 27 – August 21
Final Evaluation & Hiring Decisions: End of Week 8
Compensation
While the residency is not a salaried position, a monthly stipend will be allocated to offset expenses related to connectivity and commuting. Upon successful advancement to a an internship or full-time role, a formal review regarding the specific compensation framework will be conducted.
Candidate Criteria
We seek individuals with the potential to lead the next generation of AI engineering and research. Evaluation is based on core competencies beyond standard programming skills.
Key assessment areas include:
ML fundamentals and analytical problem-solving.
Self-directed learning and rigorous engineering discipline.
Effective technical communication and team collaboration.
Adaptability and accountability under high-pressure conditions.
A research-oriented approach to delivering technical outcomes.
Strong interpersonal communication and the ability to work effectively within cross-functional teams.
Professional ownership, initiative, and collaborative problem solving.
Applicants are expected to demonstrate foundational technical readiness prior to selection. Preferred prerequisites include:
Foundational understanding of Python and machine learning concepts.
Experience working with datasets, APIs, or ML frameworks such as pytorch.
Familiarity with Git and collaborative development workflows.
Evidence of personal projects, technical experimentation, or GitHub contributions.
Strong problem-solving aptitude and curiosity-driven learning.
Ability to communicate technical ideas clearly both verbally and in writing.
Applicants may also be asked to complete technical assessments, problem-solving exercises, or interviews as part of the selection process.
Core Philosophy
The curriculum prioritizes foundational machine learning and systems thinking over transitory trends. While modern tools are utilized, the emphasis remains on first principles.
Residents manage the end-to-end ML lifecycle, including data preprocessing, statistical validation, model deployment, and performance monitoring. Candidates must demonstrate a deep understanding of underlying frameworks.
Key technical domains include:
Feature engineering and rigorous model evaluation.
Classical algorithms, Computer Vision, and NLP.
MLOps pipelines and production deployment.
Algorithmic fairness, bias mitigation, and reliability.
Cross-functional collaboration and technical communication.
Engineering ownership and operational discipline.
The Four-Week Sprint Structure
The four-week program is organized into distinct phases, replicating the operational lifecycle of a professional AI product team.
Each week introduces progressive complexity and intensity.
Week 1: Problem Definition & Analysis
Teams address complex challenges with unstructured data in domains such as fraud detection and anomaly analysis. Residents must independently develop and propose a technical strategy. Focus areas include exploratory analysis, problem framing, technical communication, and collaborative planning.
Week 2: Engineering & Validation
The focus shifts to pipeline development and experimentation. High standards for code quality, version control, and experiment tracking are required.
Mentors actively monitor resident progress and provide engineering guidance throughout the process. Weekly technical lectures and collaborative learning sessions continue during this phase.
Week 3: Stress Testing & Peer Review
Simulated operational disruptions including data corruption and shifting objectives test team adaptability. Peer reviews are integrated to assess technical communication. Residents are expected to demonstrate resilience, accountability, and effective teamwork under pressure. Weekly discussion gatherings covering AGI developments, industry research, and engineering trends are also incorporated into the residency experience.
Week 4: Deployment & Technical Defense
Residents deliver a production-ready system and defend technical architecture choices before leadership, focusing on scalability and failure modes.
At the conclusion of Week 4, participants undergo the first major evaluation stage. Selected residents will advance to Phase 2 of the program for an additional four-week advanced residency.
Evaluation Metrics & Culture
Selection is based on long-term potential, consistency, and professional curiosity. The program serves as a realistic preview of the organization's engineering culture. Technical performance alone is not the sole evaluation factor. The residency intentionally measures how participants operate within a realistic team environment under evolving requirements and operational pressure. The program serves as a realistic preview of the organization's engineering culture and collaborative expectations.
Strategic Outcomes
The program ensures a pipeline of validated talent and provides clear insights into individual performance within a professional engineering context.
The two-phase residency structure enables the organization to evaluate both short-term technical capability and long-term professional growth potential before final hiring decisions are made at the conclusion of Week 8.
Active Project Participation
Residents will contribute directly
to active internal and open-source initiatives throughout the program rather than working exclusively on isolated training exercises
. Participants may contribute to projects such as:
Training Solutions:
a platform designed to facilitate large-scale training workflows, data operations, and scalable model development infrastructure.
Leyu:
an open dataset initiative focused on low-resource languages alongside an open-source crowdsourcing platform for real-world multilingual data collection.
Internal AI and operational tooling
used across engineering, research, automation, and data workflows within the organization.
Residents may also participate in research-oriented experimentation, infrastructure development, dataset preparation, model evaluation, and production engineering tasks tied to ongoing company initiatives.
P.S
Interested applicants should submit their CV and relevant documents to
recruitment@gheero.et
using the subject line:
“Application for Applied AI & ML Residency Program.”