ML Engineer - Computer Vision (Remote Position)

EducationPals; United States; Remote; USD 120,000 - 170,000; Full time

Job summary

EducationPals is seeking a seasoned Computer Vision scientist with 3+ years of industry experience to advance our AI initiatives in CV, image classification, and related tasks. You will work with Python/C/C++, TensorFlow and OpenCV, delivering robust models and scalable pipelines while collaborating with cross-functional teams.

Ideal candidates hold an advanced degree in a related field and demonstrate strong communication skills, an understanding of ML theory, and experience with distributed

Qualifications

• Experience with modern deep learning techniques in CV including convolutional networks, residual networks, attentional models, etc.
• Strong written and verbal communication skills
• Experience with parallel computing technologies such as OpenMP, CUDA, OpenACC, MPI
• Experience with big data and distributed computing technologies such as Hadoop, MapReduce, Spark, Storm, etc.
• Demonstrable proficiency in more than one applicable programming languages is a plus
• Solid fundamentals in mathematics, statistics, and machine learning theory
• Minimum 3 years of commercial experience (or equivalent) in computer vision
• Proficiency in at least one applicable programming language such as Python, C, C++, Java
• Proficient knowledge of and experience with image classification, segmentation, or captioning
• Proficiency in image/video processing and computer vision tools such as PIL/Pillow, GraphicsMagick, FFmpeg, OpenCV, SimpleCV, etc.
• Proficiency in machine learning tools such as TensorFlow, Keras, Caffe, Theano, MLLib, Torch, etc.

Skills

Deep learning CV

Communication skills

Parallel computing

Big data

Python

C

C++

Java

Image classification

Image segmentation

Image captioning

OpenCV

TensorFlow

Keras

Caffe

Theano

MLLib

Torch

Mathematics

Statistics

Education

Advanced degree in CS/ML/Math/Stats/Physics/Computational Linguistics

Job description

Visit www.entefy.com and www.entefy.com/blog We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about ho

• Experience with modern deep learning techniques in CV including convolutional networks, residual networks, attentional models, etc.
• Strong written and verbal communication skills
• Advanced degree in Computer Science, Machine Learning, Mathematics, Statistics, Physics, or Computational Linguistics
• Experience with parallel computing technologies such as OpenMP, CUDA, OpenACC, MPI
• Experience with big data and distributed computing technologies such as Hadoop, MapReduce, Spark, Storm, etc.
• Demonstrable proficiency in more than one applicable programming languages is a plus
• Solid fundamentals in mathematics, statistics, and machine learning theory
• Minimum 3 years of commercial experience (or equivalent) in computer vision
• Demonstrable proficiency in at least one applicable programming language such as Python, C, C++, Java
• Proficient knowledge of and experience with image classification, segmentation, or captioning
• Proficiency in image/video processing and computer vision tools such as PIL/Pillow, GraphicsMagick, FFmpeg, OpenCV, SimpleCV, etc.
• Proficiency in machine learning tools such as TensorFlow, Keras, Caffe, Theano, MLLib, Torch, etc.

Courses relevant to this posting

Relevance is based on skills and concepts, not a guarantee that you qualify.

Covered skills: AI fundamentals Machine learning Deep learning Python Data science

Skills to close: None listed

Build the skills for this job

Coming-soon courses matched to this posting: outline, waitlist, and skill path.

1. Text Representation and Semantic Search

Convert human language into computational representations using embeddings and build semantic search systems with vector databases.

2. Retrieval-Augmented Generation Pipelines

Build systems that ground model outputs in retrieved context rather than relying on memorized training data.

3. Evaluation Frameworks for Production NLP

Design test suites that measure accuracy, safety, robustness, and bias before and after deployment.

4. Safe Input Handling and Guardrails

Protect systems from ambiguous, adversarial, and risky user inputs with validation and fallback strategies.

5. Monitoring and Debugging NLP in Production

Instrument systems to detect failures, track performance drift, and build feedback loops for continuous improvement.

6. Cost Control and Latency Optimization

Optimize NLP systems for speed and budget without sacrificing reliability or user experience.

7. Domain-Specific NLP Applications

Apply production NLP patterns to customer operations, compliance, knowledge work, and field applications.

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