Filip Basara

Data Scientist

https://talent.gravityer.com/filip-basara

ML / Research Engineer. Primarily interested in self-supervised and representation learning.

Novi Sad

Key Strengths

Demonstrated strong interest and practical experience in advanced machine learning concepts, particularly in self-supervised learning, diffusion models, and representation learning.
Extensive personal projects showcasing hands-on implementation skills in PyTorch for various deep learning architectures (CLIP, FCOS, ConvNext, I-JEPA).
Focus on 'simple' and 'minimal' implementations suggests an ability to distill complex ideas into core components, which is valuable for understanding and debugging.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's projects are exclusively personal and heavily focused on research-oriented deep learning implementations. While demonstrating strong technical curiosity and initiative, there is no evidence of collaborative work, team projects, or experience in a production environment. This might indicate a potential gap in cultural fit for roles requiring significant team interaction or deployment experience. The projects are highly aligned with a research-focused Data Scientist role.

Soft Skills & Operational Fit

Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback provided.

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Projects

llm-jigsaw

December 26, 2025 – Present

Testing how well LLMs can solve jigsaw puzzles

View Project

simple-ijepa

August 11, 2024 – August 16, 2024

A simple and efficient implementation of Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture (I-JEPA)

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contrastive-neighbor-embedding

May 13, 2024 – May 14, 2024

Non-linear dimensionality reduction using contrastive neighbor embeddings.

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sigmoid-contrastive-loss

April 6, 2024 – July 8, 2024

Implementation of modulated sigmoid pairwise contrastive loss for self-supervised learning on images

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matryoshka-representation-learning

February 6, 2024 – February 7, 2024

A simple implementation of Matryoshka Representation Learning for contrastive self-supervised learning

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simple-clip

November 14, 2023 – February 14, 2024

A minimal, but effective implementation of CLIP (Contrastive Language-Image Pretraining) in PyTorch

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relic

October 16, 2023 – April 7, 2024

A simple PyTorch implementation of the Representation Learning via Invariant Causal Mechanisms self-supervised contrastive learning paper

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simple-object-detection

November 15, 2022 – October 30, 2023

A simple yet effective repo for object detection based on the FCOS architecture.

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simple-diffusion

October 20, 2022 – December 5, 2025

A minimal implementation of a denoising diffusion model in PyTorch.

View Project

simple-convnext

October 19, 2022 – October 21, 2022

Simple implementation of the ConvNext architecture in PyTorch

View Project

Key Strengths

Demonstrated strong interest and practical experience in advanced machine learning concepts, particularly in self-supervised learning, diffusion models, and representation learning.
Extensive personal projects showcasing hands-on implementation skills in PyTorch for various deep learning architectures (CLIP, FCOS, ConvNext, I-JEPA).
Focus on 'simple' and 'minimal' implementations suggests an ability to distill complex ideas into core components, which is valuable for understanding and debugging.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback provided.

Filip Basara

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Projects

Key Strengths

Cultural & Operational Fit