deep learning engineer

Spark AI

<div>
 
 
 AI / Machine Learning Engineer
 
 
 
 
 
 
 
 Role Summary
 
 
 
 
 We are looking for a pure AI / ML Engineer with 3 to 4 years of experience who can design, train, evaluate, and deploy machine learning models into production systems.
 
 
 
 
 
 
 This role covers computer vision, classical machine learning, and deep learning use cases.
 
 
 This is not a data analyst role and not a prompt-engineering role.
 
 
 
 
 
 You are expected to work with datasets, models, training pipelines, and production inference end to end.
 
 Key Responsibilities Model Development &amp; Training
 
 
 
 ● Design and train machine learning and deep learning models for real-world use cases
 
 
 ● Work across problem types:
 
 
 &cir; Computer vision
 
 
 &cir; Tabular data (classification, regression)
 
 
 &cir; Time-series forecasting
 
 
 &cir; Anomaly detection
 
 
 
 
 
 ● Select appropriate algorithms instead of defaulting to deep learning
 
 
 ● Perform hyperparameter tuning and model optimization
 
 
 ● Analyze model failures and iterate based on data, not guesswork Data &amp; Feature Engineering
 
 
 ● Understand, clean, and preprocess structured and unstructured datasets
 
 
 ● Perform feature engineering for classical ML models
 
 
 ● Handle:
 
 
 &cir; Missing data
 
 
 &cir; Outliers
 
 
 &cir; Class imbalance
 
 
 &cir; Label noise
 
 
 ● Design proper train/validation/test strategies
 
 
 ● Prevent data leakage and evaluation mistakes Computer Vision
 
 
 ● Build and deploy CV models for tasks such as:
 
 
 &cir; Image classification
 
 
 &cir; Object detection
 
 
 &cir; Segmentation
 
 
 ● Work with image preprocessing, augmentation, and labeling workflows
 
 
 ● Fine-tune pre-trained vision models when appropriate Deployment &amp; Production
 
 
 ● Convert trained models into production-grade inference services
 
 
 ● Deploy models via APIs, batch pipelines, or streaming systems
 
 
 ● Optimize inference for: &cir; Latency &cir; Throughput &cir; Cost
 
 
 ● Integrate models with backend systems and products
 
 
 ● Monitor model performance and retrain when needed MLOps &amp; Lifecycle Management
 
 
 ● Build and maintain end-to-end ML pipelines
 
 
 ● Track experiments, datasets, and model versions
 
 
 ● Automate training, evaluation, and deployment
 
 
 ● Implement rollback and version control for models
 
 
 ● Monitor model drift and data distribution changes Required Skills &amp; Experience Core ML Knowledge
 
 
 ● Strong foundation in:
 
 
 &cir; Machine learning algorithms
 
 
 &cir; Statistics &amp; probability
 
 
 &cir; Linear algebra
 
 
 ● Clear understanding of when to use:
 
 
 &cir; Linear models
 
 
 &cir; Tree-based models
 
 
 &cir; Neural networks Tools &amp; Frameworks
 
 
 ● Strong experience with Python
 
 
 ● Experience with ML frameworks:
 
 
 &cir; scikit-learn
 
 
 &cir; PyTorch and/or TensorFlow
 
 
 ● Experience with computer vision tools (OpenCV, torchvision, etc.)
 
 
 ● Familiarity with model evaluation metrics across different problem types Deployment &amp; Engineering
 
 
 ● Experience deploying ML models into production
 
 
 ● Familiarity with: &cir; Docker and containerized ML services
 
 
 &cir; Model serving frameworks
 
 
 &cir; Cloud or on- prem deployment environments
 
 
 ● Ability to write maintainable, testable ML code (not just notebooks)
 
</div>

AI machine learning engineer

Pacer Group

<div>
 
 We are looking for a
 
 hands-on Machine Learning Engineer
 
 to work on a high-impact project focused on
 
 extracting structured data from document images (invoices)
 
 .
 
 
 This role sits at the intersection of
 
 Computer Vision, OCR, and Natural Language Processing (NLP)
 
 &mdash;building intelligent systems that convert
 
 unstructured image data into usable business insights
 
 .
 
 
 If you enjoy solving real-world ML problems and improving model performance in production environments, this role is for you.
 
 
 
 
 
 
 Required Skills
 
 
 <ul>
 <li>
 Strong experience in
 
 Machine Learning / Deep Learning
 
 </li>
 <li>
 Proficiency in
 
 Python
 
 </li>
 <li>
 Hands-on experience with
 
 OCR or document/image processing systems
 
 </li>
 <li>
 Experience with
 
 Computer Vision models
 
 (CNNs, ResNet, CRNN, etc.)
 </li>
 <li>
 Experience with
 
 NLP techniques
 
 (BERT, NER, text extraction, etc.)
 </li>
 <li>
 Solid understanding of:
 </li>
 <li>
 Model training, loss functions, and epochs
 </li>
 <li>
 Model evaluation and performance tuning
 </li>
 </ul>
 
 
 
 
 
 Tech Stack
 
 
 <ul>
 <li>
 Python, PyTorch
 </li>
 <li>
 OCR tools (e.g., Tesseract, EasyOCR, or similar)
 </li>
 <li>
 Models: ResNet, CRNN, BERT, NER
 </li>
 <li>
 Database: MySQL
 </li>
 </ul>
 
 
 
 
 #ML #Machinelearning #OCR #DocumentAI #DocumentProcessing #ComputerVision #ImageProcessing #TextExtraction #DataExtraction
 
</div>

Machine Learning Engineer

Apple

Would you like to be a part of Apple&rsquo;s AI and Machine Learning org, where we encourage and create groundbreaking technology for multi-modal models with strong agent and reasoning capabilities? The Data and Machine Learning Innovation (DMLI) team is seeking a passionate Machine Learning Engineer to explore new methods, challenge existing metrics and protocols, and develop new insightful practices for real-world ML challenges. As a team member, you will work on some of the most ambitious technical challenges in the field. Your role will involve collaborating closely with our team of machine learning researchers, engineers, and data scientists. Together, you will spearhead groundbreaking research initiatives and develop transformative products designed to build for billions of users worldwide. 
 
Description 
 
As a Machine Learning (ML) Engineer, you will be entrusted with the critical role of innovating and applying innovative research in foundation models to with a particular focus on audio data. This includes working across the full ML pipeline-from pre-training on large-scale unlabeled audio corpora to post-training evaluation and fine-tuning with task-specific datasets. The solutions you develop will have a significant impact on future Apple software and hardware products, as well as the broader ML ecosystem. 
 
Your responsibilities will extend to designing and developing a comprehensive multi-modal data generation and curation framework for foundation models at Apple. You will also contribute to building robust model evaluation pipelines that support continuous improvement and performance assessment. In addition, the role involves analyzing multi-modal data to better understand its influence on model behavior and outcomes. Furthermore, you will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues. 
 
YOUR WORK MAY SPAN VARIOUS APPLICATIONS, INCLUDING: 
 
Designing self-supervised and semi-supervised representation learning pipelines, and fine-tuning strategies for tasks like speech recognition and speaker identification. 
 
Applying data selection techniques such as novelty detection and active learning across multi modalities to improve data efficiency and reduce distributional gaps. 
 
Modeling data distributions using ML/statistical methods to uncover patterns, reduce redundancy, and handle out-of-distribution challenges. 
 
Rapidly learning new methods and domains as needed, and guiding product teams in selecting effective ML solutions. 
 
Preferred Qualifications 
 
Deep understanding of multi-modal foundation models. 
 
Staying up-to-date with emerging trends in generative AI and multi-modal LLMs. 
 
The ability to formulate machine learning problems, design, experiment, implement, and communicate solutions effectively with multi-functional teams. 
 
Demonstrated publication records in relevant conferences (e.g., CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, etc.). 
 
Track records of adopting ML to solve cross-disciplinary problems. 
 
Minimum Qualifications 
 
Deep technical skills in one or more machine learning areas, such as computer vision, audio, combinatorial optimization, causality analysis, natural language processing, and deep learning. 
 
Strong software development skills with proficiency in Python; hands-on experience working with deep learning toolkits like PyTorch, TensorFlow, or JAX (one of). 
 
5+ years of experience developing and evaluating ML applications, demonstrating a passion for understanding and improving model/data quality.&lsquo; 
 
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant . 
 
Pay &amp; Benefits 
 
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $147,400 and $272,100, and your base pay will depend on your skills, qualifications, experience, and location. 
 
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits. 
 
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

AIML - Sr Machine Learning Engineer - Data and ML Innovation

Richard, Wayne & Roberts

<div>
 
 A leading organization is seeking a
 
 Machine Learning Engineer
 
 to design, build, and productionize advanced analytics solutions across machine learning, deep learning, and generative AI. This role will focus on developing scalable pipelines, optimizing model performance, and deploying solutions in production environments using modern data platforms such as Databricks and PySpark.
 
 
 
 Key Responsibilities:
 
 
 <ul>
 <li>
 Partner with business stakeholders to define and deliver ML/AI solutions
 </li>
 <li>
 Build, scale, and optimize model training and serving pipelines
 </li>
 <li>
 Improve model performance through feature engineering, tuning, and observability
 </li>
 <li>
 Deploy and integrate ML models into production with a focus on reliability and scalability
 </li>
 <li>
 Collaborate with data engineers and data scientists to translate business needs into technical solutions
 </li>
 <li>
 Drive best practices in architecture, code quality, and CI/CD pipelines
 </li>
 </ul>
 
 
 Qualifications:
 
 
 <ul>
 <li>
 7+ years of experience in machine learning engineering, data science, or software engineering
 </li>
 <li>
 Strong programming skills in Python, SQL, or Scala
 </li>
 <li>
 Experience with Databricks, PySpark, and cloud platforms (AWS, Azure, etc.)
 </li>
 <li>
 Hands-on experience with feature engineering, model optimization, and scalable data pipelines
 </li>
 <li>
 Bachelor&rsquo;s degree in a quantitative field (Master&rsquo;s preferred)
 </li>
 </ul>
 
 
 
 
 Location: Arlington, VA - Relocation assistance within the US
 
 
 No sponsorship now or in the future
 
 
 No 3rd party (C2C) - This is a direct hire with benefits opportunity
 
 
 LI- #HP-1
 
</div>

Openkyber

<div>Job Title: AI / ML Engineer 
Location: Dallas, TX or Memphis, TN Hybrid 
Duration: 6+ Months
Project Role : AI / ML Engineer
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution and developing AI applications using LLMs, RAG (Retrieval-Augmented Generation), and particularly agentic AI solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills :
<ul><li>Advanced knowledge of Python specifically for machine learning and data science.</li>
<li>Experience with LLM frameworks such as LangChain, LlamaIndex, Semantic Kernel, or AutoGen.</li>
<li>Proficiency in cloud services related to ML/AI (e.g., Azure, OpenAI, Hugging Face, AWS, Google Cloud Platform).</li>
<li>Extensive experience in AI/ML development, particularly in NLP, and machine learning domains.</li>
<li>Hands-on experience in developing AI applications using LLMs, RAG (Retrieval-Augmented Generation), and particularly agentic AI solutions is essential</li>
<li>Proficiency in designing and maintaining AI/ML solutions that are cost-effective, accurate, and scalable.</li>
<li>Exceptional problem-solving skills and the ability to think analytically.</li>
<li>Strong communication and interpersonal skills, with the aptitude to navigate complex project requirements with clients and stakeholders.</li>
</ul>Nice to have: Certification as an Azure AI Engineer Associate or equivalent credentials is highly advantageous.
Experience with ML/DL frameworks like TensorFlow, Keras, PyTorch, and scikit-learn.
Experience with C# or Java, node.js.
With Regards, OpenKyber 
7525 Mitchell Road, Suite 106, Eden Prairie, MN 55344 
Email ID - hirings@openkyber.com
For applications and inquiries, contact: hirings@openkyber.com</div>

OCI AI Services Engineer

Are you excited to tackle some of the most ambitious technical challenges in Apple Intelligence? Be involved in collaborating closely with our machine learning researchers, engineers, and data scientists? 
 
Together, you will orchestrate groundbreaking research initiatives and develop transformative products designed to build a significant impact for billions of users worldwide! 
 
As part of Apple&rsquo;s AI and Machine Learning org, we encourage and create groundbreaking technology for large-scale ML systems, computer vision, natural language processing, and multi-modal understanding and generation. 
 
The Data and Machine Learning Innovation (DMLI) team is looking for Machine Learning Engineer to explore new methods, challenge existing metrics or protocols, and develop new insightful practices that will change how we understand data and overcome real-world ML challenges. 
 
Description 
 
As a Machine Learning (ML) Engineer, you will be entrusted with the critical role of innovating and applying state of the art research in ML to tackle complex data problems. 
 
The solutions you develop will significantly impact Apple Intelligence, future Apple products and the broader ML development ecosystem. 
 
You will work with a multidisciplinary team to actively participate in Apple Intelligence&rsquo;s data-model co-design and co-develop practice. 
 
Your responsibilities will extend to the design and development of a comprehensive data generation and curation framework for Apple Intelligence foundation models at Apple. 
 
You will also be responsible to build robust model evaluation pipelines, integral to the continuous improvement and assessment of Apple Intelligence foundation models. 
 
Furthermore, you will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues. 
 
&quot;,&quot;responsibilities&quot;:&quot;Develop and implement techniques for creating high-quality synthetic datasets across a variety of domains, including vision, text, and audio data. 
 
Innovate and experiment with new approaches for synthetic data generation to improve the diversity, realism, and representativeness of datasets. 
 
Collaborate with multi-functional teams to understand data requirements and ensure that synthetic datasets are optimized for training foundation models. 
 
Crafting and implementing semi-supervised, self-supervised representation learning techniques for growing the power of both limited labeled data and large-scale unlabeled data. 
 
Develop pipelines and tools to automate synthetic data generation for large-scale AI experiments. 
 
Stay updated with the latest research and industry trends in synthetic data generation, foundational model training, and large-scale data engineering. 
 
Preferred Qualifications 
 
3+ years of experience with developing and evaluating ML applications, and demonstrated experience in understanding and improving data quality. 
 
Strong publication record in relevant conferences (e.g. CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, etc). 
 
Strong problem-solving and communication skills. 
 
Experience of multimodal large language model or image/video generation model training is a plus. 
 
Ph.D/MS degree in Machine Learning, Natural Language Processing, Computer Vision, Data Science, Statistics or related areas. 
 
Minimum Qualifications 
 
Demonstrated expertise in computer vision, natural language processing, and machine learning with a passion for data-centric machine learning. 
 
Deep understanding in multi-modal foundation models. 
 
Staying on top of emerging trends in generative AI and multi-modal LLM. 
 
Strong programming skills and hands-on experience using the following languages or deep learning frameworks: Python, PyTorch, or Jax. 
 
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant . 
 
Pay &amp; Benefits 
 
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $171,600 and $302,200, and your base pay will depend on your skills, qualifications, experience, and location. 
 
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits. 
 
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

AIML - Sr Machine Learning Engineer, Data and ML Innovation

Quantum HR

<div>About Us
Quantum HR is a premier human resources consulting firm dedicated to connecting exceptional talent with leading organizations worldwide. We specialize in providing bespoke recruitment solutions, leveraging deep industry insights and a global network to help companies build high-performing teams by matching them with professionals who truly fit their needs and culture. 
 
About the Role
We are hiring a Machine Learning Engineer to design, build, and deploy advanced ML models powering Digified&rsquo;s digital identity verification and contracting platforms. 
You will work on real-world, high-impact problems in computer vision, biometrics, OCR, fraud detection, and NLP, delivering production-grade ML systems integrated into APIs and enterprise workflows.
This is a hands-on engineering role focused on taking models from research to scalable, secure, and compliant production environments. 
 
Key Responsibilities 
<ul><li>
Develop and optimize ML models for face recognition, liveness detection, OCR, fraud detection, and NLP-based document understanding
</li><li>Build and maintain production-grade ML pipelines and APIs for real-time inference
</li><li>Deploy and scale models using MLOps best practices (CI/CD, versioning, monitoring, retraining)
</li><li>Optimize model performance for latency, accuracy, and reliability in production environments
</li><li>Design and manage high-quality datasets with proper annotation and data governance
</li><li>Monitor model performance (drift, accuracy, false acceptance/rejection rates)
</li><li>Collaborate with backend, mobile, DevOps, and product teams to integrate ML systems
</li><li>Ensure secure, compliant ML practices aligned with regulatory and data privacy standards
</li><li>Conduct research and propose improvements in model architectures and system performance</li></ul> 

Requirements 
<ul><li>
3&ndash;7 years of experience in Machine Learning, Deep Learning, or Computer Vision
</li><li>Strong Python programming skills
</li><li>Experience with PyTorch and/or TensorFlow/Keras
</li><li>Strong background in at least one of: Computer Vision, NLP, or Fraud Detection
</li><li>Experience with ML deployment (FastAPI, Flask, Docker, Kubernetes)
</li><li>Understanding of MLOps principles (model versioning, monitoring, retraining pipelines)
</li><li>Solid foundation in mathematics (linear algebra, probability, statistics)
</li><li>Experience working with production ML systems 
 
Nice to Have 
</li><li>
Experience with face recognition, OCR, or liveness detection systems
</li><li>Exposure to Vision Transformers, Siamese Networks, CRNNs, or LLM-based systems
</li><li>Experience with distributed training or GPU acceleration
</li><li>Familiarity with MLflow or experiment tracking tools
</li><li>Knowledge of identity standards (FIDO, NIST 800-63-3)
</li><li>Background in fintech, regtech, or anti-fraud systems
</li><li>Experience with trust &amp; safety or government-grade systems</li></ul> 

Benefits 
<ul><li>
Work on cutting-edge ML systems in digital identity and fraud prevention
</li><li>High-impact role with production ownership from research to deployment
</li><li>Exposure to enterprise-grade AI systems used in regulated environments
</li><li>Strong engineering culture with cross-functional collaboration
</li><li>Opportunity to work on advanced CV/NLP/biometric systems at scale
</li><li>Career growth in a fast-scaling AI-driven product company</li></ul></div>

ML Engineer

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AIML - Sr Machine Learning Engineer - Data and ML Innovation

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AIML - Sr Machine Learning Engineer, Data and ML Innovation

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