Hire the Best Machine Learning Engineers
Fogelsville, Pennsylvania
* Expert Vetted talent in Upwork with 100% job success rate. * I am looking for long term work in solving problems with Machine Learning solution. * I have been working on Machine Learning for over 5 years. * My area of expertise in Machine Learning area are: Computer Vision and NLP. * My live projects include: 'Detect Products of Super-market shelves', 'Detect sharp objects from x-ray image', various Image classification models like to classify inside/outside House, Shoes(of different materiel), 'Text classification' of various articles. * I have also worked on stock forecasting LSTM model using stock data and sentiment data. * I have a certification from Udacity in "Self Driving Car Engineer" Nano Degree
- Machine Learning
- Machine Learning Model
- Python
- pandas
- Computer Vision
- Deep Learning
- Keras
- TensorFlow
- Classification
- Model Tuning
- Amazon Web Services
- Deep Learning Modeling
Tbilisi, Georgia
Hello, I am investing all my time and resources in Upwork ☝ ✅ AWS Certified Solutions Architect Professional ✅ AWS Certified Solutions Architect Associate ✅ AWS Certified Cloud Practitioner I can train, fine tune and deploy production-ready Machine Learning models. *** Degrees *** ✅ Master degree in Automation and Control systems ✅ Bachelor's degree in Engineering Physics. *** Skills *** ➩ AI\Machine Learning--(Scikit-Learn, TensorFlow,HuggingFace,Pytorch) ➩ Programming Languages : Python, C#, Matlab ➩ Microcontrollers--(Raspberry Pi, Arduino) ➩ Mobile Development --(Xamarin) I have experience in hardware as well as the software side of project development. My experience in details is following : AI\Machine Learning 5 years of experience in AI\Machine Learning include: ⬣ PROGRAMMING LANGUAGES : Python, MATLAB ⬣ ML/DL LIBRARIES : TensorFlow, Scikit-Learn, Keras, Pandas, Numpy, OpenCV,Pytorch, HuggingFace ⬣ COMPUTER VISION : 2D,3D Object detection/tracking/pose estimation ⬣ Creating regression and similarity search models *** Certificates*** ✅ AWS Certified Solutions Architect Professional ✅ AWS Certified Solutions Architect Associate ✅ AWS Certified Cloud Practitioner ✅ IELTS certificate Overall Band Score 7.0 Reading 8.5 Writing 6.5 Listening 6.0 Speaking 6.5 ------------------------------------------------------------------------------------------- I AM READY TO IMPLEMENT YOUR PROJECT AND CONVERT YOUR IDEAS INTO A REALITY!
- Machine Learning
- Python
- Deep Learning
- Amazon SageMaker
- PyTorch
- Amazon Web Services
- Cloud Computing
- Google Cloud Platform
- Retrieval Augmented Generation
- AI Agent Development
- Vertex AI
- LangChain
- Large Language Model
- Databricks Platform
- PySpark
Hanoi, Vietnam
Hello, I'm Tam Nguyen Van, a Computer Science graduate from the prestigious Hanoi University of Science and Technology.🎓 Currently, I thrive as a versatile Machine Learning & Deep Learning freelancer, merging a solid foundation in Mathematics and Machine Learning theory with practical programming expertise. My proficiency spans frameworks such as Tensorflow and Pytorch, enabling me to adapt to diverse project requirements seamlessly. Moreover, I boast hands-on experience in deploying Machine Learning models into production environments using REST API, Docker, Flutter, and AWS. 🚀 As a passionate researcher and problem solver in the realm of Machine Learning, I am driven by the challenge of tackling intricate tasks head-on. When you engage me as a freelancer, expect nothing short of timely delivery and impeccable results. My skill set encompasses: • Python. • Tensorflow/Keras, Pytorch, sklearn, pandas. • Machine Learning/Deep Learning. • REST API, Docker. • Flutter. • AWS services. Let's collaborate and bring your projects to life! 🤝
- Machine Learning
- Machine Learning Model
- Deep Neural Network
- TensorFlow
- Computer Vision
- PyTorch
- Natural Language Processing
- Deep Learning
- Keras
- Python
- Data Entry
- Docker
- Amazon S3
- OCR Algorithm
- AWS Lambda
Islamabad, Pakistan
Most computer vision projects fail not in training — but in deployment. Models that hit 95% accuracy in the lab break down when lighting shifts, hardware stutters, or the camera feed isn't clean. I build systems engineered to survive those conditions — and I've done it across industries, hardware platforms, and deployment environments. I'm a Computer Vision Engineer specializing in end-to-end AI pipelines — from raw camera input to real-time inference, deployed on edge hardware, cloud APIs, or both. ━━ Core services ━━ → Object detection & multi-object tracking — YOLOv8, YOLOv5, ByteTrack, BOTSort, MMDetection → Segmentation, pose estimation & keypoints — MediaPipe, custom model architectures → Edge AI deployment — NVIDIA Jetson Orin/Nano, Raspberry Pi, Hailo — TensorRT, ONNX, INT8/FP16 → Cloud & API deployment — FastAPI, Docker, AWS GPU instances, REST & WebSocket inference APIs → Video analytics & smart camera systems — safety monitoring, defect detection, zone tracking, people counting ━━ Systems I've shipped ━━ ✓ Real-time fall detection on NVIDIA Jetson — production-deployed, sub-100ms latency ✓ Zone-based people tracking & monitoring for safety-critical environments ✓ Industrial defect detection pipeline — TensorRT-optimized, running on constrained edge hardware ✓ End-to-end smart camera system: camera → inference → dashboard & real-time alerts ✓ OpenCV video analytics pipelines with custom pre/post-processing and business logic ━━ What makes my work different ━━ Most CV engineers deliver a model file. I deliver a working system — optimized, integrated, and running reliably in your environment. I lead a small team and personally own system architecture, optimization strategy, and core AI engineering on every project. You get senior-level technical execution, not delegation to juniors. Edge or cloud. Jetson or GPU server. Prototype or production scale. I've built across all of it. ━━ How a typical project runs ━━ 1. Discovery — review your hardware targets, data sources, and latency requirements before any code is written 2. Architecture — design the full pipeline: model selection, optimization path, deployment stack, integration points 3. Build & optimize — iterative development with benchmarked FPS and accuracy metrics at each stage 4. Deployment — containerized, documented, and running on your target environment 5. Handover — clean codebase, inline documentation, and a session so your team can maintain it independently ━━ Full tech stack ━━ Models: YOLOv8, YOLOv5, YOLOv7, MMDetection, Detectron2, PyTorch, TensorFlow, ONNX Runtime Tracking: ByteTrack, BOTSort, DeepSORT, StrongSORT, custom zone logic & counting algorithms Optimization: TensorRT INT8/FP16, ONNX quantization, model pruning, batch inference tuning Edge hardware: NVIDIA Jetson Orin/Nano, Raspberry Pi 4/5, Hailo-8, Coral TPU Cloud & infra: FastAPI, Flask, Docker, AWS EC2/Lambda, GCP, RTSP/RTMP stream processing Vision utilities: OpenCV, FFmpeg, GStreamer, PIL/Pillow, custom pipeline components ━━ Project types I take on ━━ → Greenfield CV systems — full pipeline from scratch to production deployment → Model optimization — take an existing model and make it production-fast on your hardware → Edge porting — migrate a cloud-based CV system to Jetson, Raspberry Pi, or Hailo → Pipeline debugging — diagnose and fix latency, accuracy, or stability issues in live systems → Inference API — wrap your CV model as a scalable, low-latency REST or WebSocket API → PoC → production — take a working demo and harden it for real-world deployment at scale → Team augmentation — embedded senior CV engineer for sprints or longer-term engagements ━━ Industries served ━━ Manufacturing & quality control — defect detection, visual inspection, production line monitoring Safety & security — real-time threat detection, perimeter monitoring, crowd analytics Retail & logistics — shelf analytics, people counting, queue management, warehouse tracking Healthcare — patient monitoring support systems, lab automation, medical imaging pipelines Agriculture — crop health detection, drone-based aerial inspection, field monitoring systems ━━ Common questions ━━ Work with our existing dataset? Yes — I assess quality, recommend augmentation strategies, and fine-tune models on your labeled data. Edge or cloud deployment? Both — Jetson, Raspberry Pi, and Hailo at the edge; AWS GPU instances and containerized APIs in the cloud. Can you take our prototype to production? That's one of my most common engagements — hardening, optimizing, and deploying existing concepts for real-world reliability. Documentation and handover included? Always. Clean code, inline comments, deployment instructions, and a dedicated handover session on every project. If you need computer vision that performs beyond lab conditions — on real hardware, with real data, in real-world environments — let's talk.
- Machine Learning
- Artificial Intelligence
- Deep Learning
- Python
- PyTorch
- YOLO
- Computer Vision
- Flask
- React
- Web Application
- Edge AI
- TensorRT
- CUDA
- NVIDIA Jetson
- Node.js
- Object Detection & Tracking
- Image Segmentation
- OpenCV
Bahawalpur, Pakistan
🌟 Top Rated Plus AI Engineer | PhD in Computer Science (AI, ML & Generative AI) I specialize in transforming complex ideas into scalable, real-world AI solutions that deliver measurable impact. With a strong blend of research excellence and practical implementation, I help businesses and researchers build intelligent systems that actually work in production. 🧠 Core Expertise 🔹 Computer Vision (YOLO, Vision Transformers, Object Detection) 🔹 Deep Learning (CNNs, Transformers, Vision Transformers - ViTs) 🔹 Generative AI & Large Language Models (LLMs) 🔹 NLP & Fine-tuning (BERT, LLaMA) 🔹 Predictive Modeling & Data Science 🔹Model Optimization & Deployment 💼 Featured Projects 🚧 AI-based Helmet Detection System (YOLOv8 + Vision Transformers) 🌱 Plant Disease Classification using Deep Learning 📉 Customer Churn Prediction System 🧾 Urdu NLP & LLM Fine-tuning Solutions 🏥 Medical Imaging with Explainable AI (Grad-CAM) 🎯 What I Can Do for You ✔ Design and develop custom AI/ML solutions tailored to your business ✔ Build Computer Vision systems (detection, classification, segmentation) ✔ Fine-tune and deploy LLMs & Generative AI applications ✔ Convert research papers into working, production-ready models ✔ Optimize models for performance, scalability, and deployment 🛠️ Tech Stack 💻 PyTorch | TensorFlow | OpenCV | Transformers | Python | Scikit-learn 💡 Why Choose Me? ✨ Top Rated Plus freelancer with a proven track record ✨ Strong PhD-level research + industry implementation expertise ✨ Clear communication, reliability, and on-time delivery ✨ Focus on building accurate, efficient, and production-ready AI systems 📩 Let’s collaborate to bring your AI idea to life! If you’re looking for a dependable expert in AI, Machine Learning, or Generative AI, I’d be happy to discuss your project. Regards 𝑫𝒓. 𝑺𝒂𝒏𝒂 𝑪𝒉𝒆𝒆𝒎𝒂
- Machine Learning
- Python
- Large Language Model
- Image Classification
- GitHub
- Django
- Flask
- Web Application
- Chatbot
- Research Papers
- Academic Editing
- Research Proposals
- LaTeX
- Publication Design
- Professional Journal Citations
Bialystok, Poland
🎁 𝐆𝐄𝐓 𝐘𝐎𝐔𝐑 𝐅𝐑𝐄𝐄 𝐀𝐈 𝐑𝐄𝐀𝐃𝐈𝐍𝐄𝐒𝐒 𝐀𝐔𝐃𝐈𝐓 - send me a message and I'll analyze your stack, data pipelines, and AI use cases in 3-5 days. I work as a Machine Learning Engineer, AI Engineer, DevOps Engineer, and Python Developer delivering production Machine Learning systems and AI solutions using Python, with a strong focus on LLM, RAG systems, Computer Vision, and full MLOps / DevOps infrastructure. I operate with a team of 90+ engineers across Machine Learning, DevOps, and Backend, delivering complex Machine Learning and AI systems end-to-end, from data pipelines to deployed, monitored, and scaled systems in production for US and European clients. I'm a Machine Learning Engineer, AI Engineer, and Python Developer with 10+ years of experience building Machine Learning systems and AI solutions using Python for SaaS, fintech, manufacturing, and enterprise companies. As a Machine Learning Engineer, I combine Python, Deep Learning, NLP, Computer Vision, and LLM technologies to build scalable, production-grade Machine Learning systems that solve real business problems. 💻 As a Machine Learning Engineer, AI Engineer, and RAG Developer, I build RAG systems and Retrieval-Augmented Generation pipelines using Python, vector databases, semantic search, and knowledge retrieval systems integrated into production workflows. As a Machine Learning Engineer working with RAG pipelines, I design systems connected to SQL databases, CRMs, and internal knowledge bases. One Machine Learning-powered RAG system reduced support workload equivalent to 3 full-time employees, cutting response time from hours to seconds. As an AI Agent Developer using LangGraph and CrewAI, I build multi-agent Machine Learning systems where each agent handles retrieval, reasoning, and execution in a single production pipeline. 🤖 As a Machine Learning Engineer, AI Engineer, and LLM Developer, I deliver end-to-end LLM integration using Python and models like GPT-4/5, Claude, LLaMA, and Mistral. I build AI agents, AI copilots, and Machine Learning-driven automation systems integrated into enterprise workflows. As a Machine Learning Engineer, I handle prompt engineering, context engineering, embedding pipelines, vector databases like Pinecone, Weaviate, and Chroma, and optimization of Machine Learning and RAG systems in production environments. 👁️ As a Machine Learning Engineer and Computer Vision Engineer, I develop Computer Vision systems using Python, YOLOv8, Detectron2, and OpenCV for object detection, segmentation, and real-time analytics. I delivered a Machine Learning system that replaced manual inspection in manufacturing and reduced defect escape rate to near zero. I also build Document AI systems using OCR tools like Textract and Google DocAI, including full Machine Learning pipelines for processing noisy and unstructured data. 🧠 As a Machine Learning Engineer and NLP Engineer, I build Machine Learning systems using Python for Named Entity Recognition, text classification, semantic search, multilingual NLP, question-answering systems, sentiment analysis, and topic modeling. I combine traditional Machine Learning approaches with LLM technologies to deliver production-ready language systems. ⚙️ As a Machine Learning Engineer, MLOps Engineer, and DevOps Engineer, I design, deploy, and scale Machine Learning systems in production using Python and cloud infrastructure. I build Machine Learning and AI infrastructure with Kubernetes, Docker, Terraform, Ansible, Jenkins, Kafka, Grafana, Prometheus, and NGINX across AWS, Google Cloud, and Azure. In one DevOps and Machine Learning case, deployments scaled from 1 per month to 120 per month, deployment time decreased by 85%, and infrastructure costs were reduced by 55%. As a Machine Learning Engineer and DevOps Engineer, I don't just build models - I deploy, monitor, optimize, and scale Machine Learning systems in real environments. I build Machine Learning systems using Python, AI solutions, LLM applications, RAG systems, Computer Vision pipelines, NLP systems, and scalable MLOps / DevOps infrastructure that works in production. If you're looking for a Machine Learning Engineer, AI Engineer, Python Developer, or DevOps Engineer who understands Machine Learning systems, DevOps infrastructure, and real business workflows - you're in the right place. 🚀 If you're building or scaling a Machine Learning or AI product and need a Machine Learning Engineer, Python Developer, or DevOps Engineer who can design, build, deploy, and scale real production systems - not just prototypes - I can help. Most clients come when their Machine Learning systems are slow, unstable, or not delivering results. I redesign, optimize, and turn them into scalable Machine Learning and AI systems powered by Python and reliable DevOps infrastructure. 📩 Send me a message with your current setup, and I'll tell you what's missing, what can be improved, and whether your Machine Learning system is ready for prod
- Machine Learning
- Machine Learning Model
- DevOps
- Artificial Intelligence
- Python
- PyTorch
- TensorFlow
- Deep Learning
- Natural Language Processing
- Amazon Web Services
- Docker
- Kubernetes
- CI/CD
- JavaScript
- AI Development
- Computer Vision
- Data Analysis
- Neural Network
- Data Science
- AI Agent Development
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Resources to help you hire

Cost to hire a Machine Learning Engineer
Explore typical Machine Learning Engineer rates and what businesses pay to hire top talent.

Machine Learning Engineer job description template
Get tips to write a job post that attracts qualified Machine Learning Engineers.

Machine Learning Engineer interview questions
Top interview questions to help you hire the right Machine Learning Engineers, faster.
Resources to help you hire

Cost to hire a Machine Learning Engineer
Explore typical Machine Learning Engineer rates and what businesses pay to hire top talent.

Machine Learning Engineer job description template
Get tips to write a job post that attracts qualified Machine Learning Engineers.

Machine Learning Engineer interview questions
Top interview questions to help you hire the right Machine Learning Engineers, faster.
Machine learning engineer hiring guide
Machine learning engineers help businesses harness the power of data by designing predictive models, building intelligent applications, and automating complex workflows. Companies hire these professionals to develop recommendation engines, fraud detection systems, demand forecasting tools, and other AI-driven solutions looking to turn raw data into measurable business outcomes.
What does a machine learning engineer do?
Machine learning engineers (MLEs) build and deploy algorithms that help businesses predict outcomes, streamline processes, and unlock value from data. They combine software engineering with data science to develop AI-powered systems that run in production environments.
Freelance MLEs can support everything from deep learning and natural language processing (NLP) to computer vision and model optimization. Machine learning helps companies launch recommendation engines, automate decision-making systems, improve personalization, and more.
Their unique blend of software engineering and data science expertise bridges the gap between experimental models and production-ready systems, making them essential for organizations
How to hire a machine learning engineer on Upwork
Upwork makes it easy to connect with skilled ML engineers for projects of any size. Follow these four steps to hire effectively.
Step 1: Craft a targeted job post
The specificity of your job post directly impacts applicant quality. A well-written job post attracts qualified candidates faster by outlining your goals and tech stack in a clear job description.
Specify the use case. Are you building a forecasting tool, chatbot, fraud detection system, or computer vision app?
List key technologies. Mention frameworks like TensorFlow, PyTorch, scikit-learn, or XGBoost.
Clarify deliverables. Define whether you need a model, full pipeline, or production deployment, and include timelines and budget.
Articulate the scope. Identify expected timeline and budget.
For a faster starting point, try Upwork's Job Post Generator, powered by Uma™, Upwork's Mindful AI. Describe your project in a few sentences and Uma will craft a machine learning engineer job post for your review.
Step 2: Filter and evaluate candidates
Prioritize evidence of hands-on ML work over credentials alone. Use Upwork's filters and search tools to sort by skills, certifications, or industries served.
Review portfolios. Look for completed ML projects, GitHub links, or technical blog posts.
Check ratings and feedback. Past client reviews, high Job Success Scores, and talent badges highlight reliability and communication style.
Shortlist strong matches. Many experts come from bootcamp programs or hold professional certifications.
You can use Upwork’s instant video interviews to screen applicants for a best-fit shortlist, with Uma providing side-by-side candidate comparisons.
Step 3: Interview your top choices
The interview stage reveals how candidates think through complex problems. Prepare relevant machine learning questions to assess fit.
Discuss past projects. Ask how they've handled model performance, bias mitigation, or data quality issues.
Test for real-world thinking. See how they'd approach your dataset or describe tradeoffs between model types.
Check documentation habits. A well-documented model is easier to maintain and scale.
Assess communication skills. Gauge their ability to collaborate remotely.
For deep learning projects, prepare specialized interview questions to assess neural network expertise.
Upwork Messages allows you to schedule and conduct live video interviews on the platform, with call transcripts and summaries available after the calls.
Step 4: Agree on scope and begin work
Once you’ve found the right fit, you can send a contract directly on the Upwork platform. Establishing a shared understanding of milestones and success criteria protects both parties.
Set up payment structure. Use hourly payment for ongoing needs or a fixed-price contract with milestones for projects with defined deliverables.
Break down phases. Use milestones like data prep → model training → testing → deployment.
Agree on evaluation metrics. Whether it's accuracy, AUC, or latency, decide how you'll measure success.
Define revision cycles. Outline how many iterations are included, and use Upwork's tools like contracts and milestones to keep things on track.
Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.
The rates and information provided in this article are based on current data and industry sources available at the time of publication. Freelance rates can vary depending on factors such as experience, location, project scope, and market conditions. Readers are encouraged to conduct their own research to confirm current rates and trends, as this information may change over time.
How much does hiring a machine learning engineer cost?
The cost to hire a machine learning engineer on Upwork ranges from $50-$200 per hour.
Rates vary based on project complexity, model type, and the engineer’s experience level.
When planning your project budget, consider these typical project-based costs for common machine learning engagements:
ML model prototype
$1,000-$3,000 /project
- Build a regression or classification model
- Deliver trained model with documentation
- Provide baseline performance metrics
Custom prediction pipeline
$3,000-$8,000 /project
- Design end-to-end ML pipeline with data ingestion
- Integrate data preprocessing and feature engineering
- Deploy model to staging environment
NLP or computer vision system
$8,000-$20,000 /project
- Develop a custom NLP chatbot or image recognition model
- Implement transfer learning and fine-tuning
- Deliver production-ready API endpoint
Real-time recommendation engine
$15,000-$35,000 /project
- Build a scalable recommendation system
- Integrate with existing product infrastructure
- Conduct A/B testing and performance optimization
Ongoing ML model optimization
$3,000-$8,000 /month
- Retrain and tune existing models
- Monitor model drift and performance
- Implement incremental improvements
FAQs about machine learning engineers
Frequently asked questions
Is hiring a machine learning engineer worth it?
Yes, hiring a machine learning engineer is worth it if you have a clear, data-driven problem to solve and the infrastructure to support it. These specialists help automate decisions, improve predictions, and power personalization features that can lead to measurable improvements in efficiency and revenue.
However, ML engineers are a high-cost investment. If your needs can be met with simpler tools or basic automation, hiring a machine learning engineer may not be cost-effective. Overall, the role delivers the highest value when used on scalable problems where machine learning can directly improve outcomes.
What qualifications should I look for in a freelance machine learning engineer?
Strong candidates for machine learning engineer roles may hold degrees in computer science, statistics, or software engineering; many also complete professional certificates. Prior experience with model deployment, APIs, and production environments often matters more than formal education. Look for someone who understands optimization, system design, and how to apply ML techniques to your industry.
Which tools and techniques should machine learning engineers know?
Most machine learning engineers use Python and tools like TensorFlow, scikit-learn, or XGBoost for tasks ranging from training neural networks to unsupervised clustering. This is how they use specific tools:
Programming languages (Python, sometimes R/Java). Used to build models and data pipelines
ML frameworks (TensorFlow, PyTorch, scikit-learn). For training and evaluating models
Data tools (Pandas, NumPy, SQL). For cleaning, transforming, and analyzing data
Cloud platforms (AWS, GCP, Azure). To store data, train models, and deploy services
MLOps tools (Docker, Kubernetes, MLflow). For versioning, deployment, and monitoring
Data preprocessing and feature engineering. Improve model accuracy by preparing high-quality inputs
Model selection and evaluation. Choose the right algorithm and measure performance
Hyperparameter tuning. Optimize model performance
Model deployment and monitoring. Ensure models run reliably in production and stay accurate over time
Experimentation and A/B testing. Validate model impact in real-world scenarios
Do machine learning engineers also handle data engineering?
Some machine learning engineers handle data engineering, but not all do. While ML engineers often work closely with data engineers, their primary focus is model development and optimization. If your project includes data ingestion or pipeline design, consider hiring a data engineer alongside your machine learning engineer.
What's the difference between a data scientist and a machine learning engineer?
Data scientists focus more on data exploration, statistical analysis, and model prototyping, while machine learning engineers build and scale models that run in production environments. If you need production-ready systems, hire a machine learning engineer.
What's the difference between a machine learning engineer and an AI engineer?
Machine learning engineering and AI engineering are both vital roles, with key differences. A machine learning engineer focuses specifically on building and deploying ML models. An AI engineer takes a broader approach by designing artificial intelligence systems that may include machine learning and also span robotics, rule-based systems, or computer vision.
What kinds of machine learning models can freelance engineers build?
Freelance ML engineers can build models for both supervised and unsupervised learning tasks, including regression models for forecasting, classification models for fraud detection, and clustering models for customer segmentation. They may also build decision trees, KNN, XGBoost, or logistic regression models depending on your objectives.
How can I make sure the machine learning model performs well?
Clear machine learning model performance starts with solid data preprocessing and feature engineering. A skilled engineer will normalize data, handle missing values, and tune hyperparameters to avoid overfitting. During model training, they'll monitor performance with metrics like precision, recall, or ROC-AUC.
Find more freelancers
Similar Machine Learning Engineer Skills
- Generative Model Specialists
- Transfer Learning Specialists
- Machine Learning Model Specialists
- Deep Learning Experts
- Keras Professionals
- fastText Specialists
- Unsupervised Learning Specialists
- TensorFlow Specialists
- Image/Object Recognition Professionals
- Reinforcement Learning Specialists
- Data Augmentation Specialists
- Supervised Learning Specialists
- Object Detection Specialists
- PyTorch Specialists
- Deep Neural Networks Developers
- Transformer Model Specialists
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