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Nvidia Machine Learning Jobs in Atlanta, GA (NOW HIRING)

Career Renew

Senior Machine Learning Engineer

Atlanta, GA · Remote

$165K - $225K/yr

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a ... NVIDIA DGX Spark. Understanding of FDA regulatory requirements for AI/ML in medical devices ...

Avani Technology Solutions, Inc.

AWS Architect

Atlanta, GA · On-site

$62.50 - $82/hr

... of Machine Learning Platforms including Public Cloud Offering • Experience with software ... with Nvidia-docker, GPU specific technologies Please share your resumes to natraj.b ...

Smarsh

Lead Data Scientist

Atlanta, GA

Experience with natural language processing toolkits like NLTK, spaCy, Nvidia NeMo * Knowledge of microservices architecture and continuous delivery concepts in machine learning and related ...

Smarsh

Lead Data Scientist

Atlanta, GA · On-site

Experience with natural language processing toolkits like NLTK, spaCy, Nvidia NeMo * Knowledge of microservices architecture and continuous delivery concepts in machine learning and related ...

Smarsh

Lead Data Scientist

Atlanta, GA · Remote

Experience with natural language processing toolkits like NLTK, spaCy, Nvidia NeMo * Knowledge of microservices architecture and continuous delivery concepts in machine learning and related ...

Honeywell

Sr Advanced AI Platform Engineer

Atlanta, GA · On-site

$117K - $155K/yr

You will work at the intersection of data engineering, machine learning operations, and edge AI ... Experience with edge AI deployment on NVIDIA Jetson or similar embedded GPU platforms. * Experience ...

Honeywell

Sr Advanced AI Platform Engineer

Atlanta, GA

$117K - $155K/yr

You will work at the intersection of data engineering, machine learning operations, and edge AI ... Experience with edge AI deployment on NVIDIA Jetson or similar embedded GPU platforms. * Experience ...

Deloitte

Oracle Gen AI Manager

Atlanta, GA

... machine learning initiatives * Identify high-value AI use cases and guide teams on prompt ... Experience with LangChain, LangGraph, NVIDIA NIM, or Hugging Face * Experience leading AI or ERP ...

Honeywell

Sr Advanced AI Platform Engineer

Atlanta, GA · On-site

$117K - $155K/yr

You will work at the intersection of data engineering, machine learning operations, and edge AI ... Experience with edge AI deployment on NVIDIA Jetson or similar embedded GPU platforms. * Experience ...

h2o.ai

Senior Sales Engineer, Healthcare

Atlanta, GA · On-site

$100K - $138K/yr

H2O.ai partners include NVIDIA, Dell Technologies, Deloitte, Ernst & Young (EY), Snowflake, AWS ... In this role, you'll combine deep technical expertise in Machine Learning and Generative AI with ...

h2o.ai

Senior Sales Engineer, Healthcare

Atlanta, GA · On-site

$100K - $138K/yr

H2O.ai partners include NVIDIA, Dell Technologies, Deloitte, Ernst & Young (EY), Snowflake, AWS ... In this role, you'll combine deep technical expertise in Machine Learning and Generative AI with ...

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All JobsNvidia Machine Learning JobsNvidia Machine Learning Jobs in Atlanta, GA

Nvidia Machine Learning information

See Atlanta, GA salary details

$24.5K

$41K

$84.6K

How much do nvidia machine learning jobs pay per year?

As of Jul 1, 2026, the average yearly pay for nvidia machine learning in Atlanta, GA is $40,951.00, according to ZipRecruiter salary data. Most workers in this role earn between $31,300.00 and $44,200.00 per year, depending on experience, location, and employer.

How much do NVIDIA machine learning engineers make?

NVIDIA machine learning engineers typically earn between $100,000 and $160,000 annually, depending on experience, location, and skill level. Senior roles or those with specialized expertise in deep learning and GPU programming can earn higher salaries, often exceeding $180,000. Compensation may also include bonuses and stock options in competitive tech environments.

What is a Nvidia Machine Learning job?

A Nvidia Machine Learning job involves developing and optimizing AI models, deep learning frameworks, and GPU-accelerated applications. Engineers in this role work on cutting-edge research, building scalable ML solutions, and improving performance on Nvidia hardware like GPUs and AI accelerators. They collaborate with software and hardware teams to enhance AI capabilities across industries such as gaming, healthcare, and autonomous systems. Strong coding skills in Python, C++, and experience with ML frameworks like TensorFlow or PyTorch are often required.

What are the key skills and qualifications needed to thrive in the Nvidia Machine Learning position, and why are they important?

To thrive in an Nvidia Machine Learning role, a deep understanding of machine learning algorithms, proficiency in programming languages like Python or C++, and a solid background in mathematics or computer science are essential. Experience with Nvidia's CUDA, TensorRT, cuDNN, and familiarity with modern deep learning frameworks such as TensorFlow or PyTorch are highly valued, as are relevant certifications in AI or data science. Strong problem-solving skills, teamwork, and effective communication distinguish top candidates in collaborative, fast-paced environments. These skills are crucial for developing and optimizing AI solutions that leverage Nvidia’s advanced hardware and software platforms.

Does NVIDIA do machine learning?

Nvidia offers extensive tools and platforms for machine learning, including GPUs optimized for training and deploying models. Many machine learning engineers and researchers use Nvidia hardware and software frameworks like CUDA and cuDNN to accelerate AI development. The company also provides AI-focused products and solutions for various industries.

Is it hard to get hired at NVIDIA?

Getting hired for a machine learning role at NVIDIA can be competitive due to the company's focus on advanced technology and innovation. Candidates typically need strong technical skills in deep learning, programming, and relevant experience, along with a solid educational background. The hiring process often involves multiple interviews and technical assessments to evaluate expertise and problem-solving abilities.

What are some common challenges faced by professionals in Nvidia Machine Learning roles?

One common challenge in Nvidia Machine Learning roles is optimizing models to fully leverage GPU architectures for both performance and efficiency, which requires continuous learning as the technology rapidly evolves. Team members often work on complex, large-scale projects that demand close collaboration across software, hardware, and research divisions. Navigating the fast pace of innovation and contributing effectively to cross-functional teams is essential for success. However, these challenges also make the role exciting and offer excellent opportunities for professional growth and hands-on experience with state-of-the-art AI solutions.

Is ML a high paying job?

Machine Learning roles, including positions like Nvidia Machine Learning engineers, are generally well-paid due to high demand for specialized skills in AI, data analysis, and programming. Salaries vary based on experience, location, and company, but these jobs tend to offer above-average compensation compared to many other tech roles.
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Nvidia Machine Learning jobs near you
Infographic showing various Nvidia Machine Learning job openings in Atlanta, GA as of June 2026, with employment types broken down into 2% As Needed, 51% Full Time, 44% Part Time, and 3% Contract. Highlights an 89% Physical, 5% Hybrid, and 6% Remote job distribution, with an average salary of $40,951 per year, or $19.7 per hour.

Senior Machine Learning Engineer

Career Renew

Atlanta, GA • Remote

$165K - $225K/yr

Full-time

Posted 9 days ago

Job description

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a fully remote role for US/Canada based candidates. Salary range: 165-225K USD yearly plus benefits plus equity.
We are the leading virtual staining company revolutionizing digital pathology adoption worldwide through cutting-edge AI-powered technology. Our solutions deliver diagnostic-quality results in minutes while preserving tissue samples for comprehensive analysis.
Our breakthrough DeepStain™ and ReStain™ technologies enable unlimited virtual staining from a single tissue sample, eliminating the bottlenecks and limitations of traditional chemical staining processes. This innovation supports the critical evolution from research applications to clinical deployment, empowering laboratories to advance their digital pathology capabilities while reducing chemical waste, improving operational efficiency, and expanding diagnostic possibilities.

About the Role

We are seeking an experienced Senior ML Engineer to join our team who owns the representation-learning and generative modeling stack that powers Pictor’s virtual staining. The ideal candidate will have deep expertise in Machine Learning and building generalizable, production-ready models, and evaluations that stand up in clinical workflows.
Design and implement novel computer vision and deep learning algorithms for virtual staining and digital pathology applications
Conduct rigorous experiments to evaluate algorithm performance, validate research hypotheses, and drive iterative improvements
Develop and advance ML models leveraging Vision Transformers, Diffusion Models, GANs, and generative architectures for image-to-image translation tasks
Apply classical and learned image enhancement, denoising, and semantic segmentation techniques to histopathology imaging challenges
Explore image representation in latent space for efficient, high-fidelity virtual staining
Stay current with state-of-the-art research, identifying opportunities to apply novel techniques to PictorLabs’ product roadmap

Collaboration
Collaborate with ML Engineering and software teams to translate research prototypes into production-ready systems meeting latency and throughput requirements
Work with large-scale pathology datasets to train, validate, and fine-tune foundation models and custom architectures
Partner with software engineers, data scientists, and pathology domain experts to integrate research into production systems
Contribute to best practices for data engineering, data governance, and data quality across research and production pipelines
Leverage AI coding and ideation tools to accelerate research velocity and prototype new approaches

Required Qualifications

PhD (preferred) or Master’s degree in Computer Science, Electrical Engineering, or a related field
Deep expertise in computer vision and deep learning, with hands-on experience in one or more of: Vision Transformers, Diffusion Models, GANs, semantic segmentation, or classical image enhancement and denoising
Expert proficiency in Python and PyTorch and other scientific computing environments a plus
Strong mathematical foundation in linear algebra, probability, and optimization
Experience with large-scale model training, distributed computing, or cloud ML infrastructure (AWS, GCP, or Azure)
Knowledge of handling large scale image data, data version controls, model registry, has experience dealing with ML lifecycles
Experience with feature search, data balancing, and data curation pipelines.
Knowledge of software engineering best practices including version control (Git) and CI/CD pipelines
Excellent collaboration and communication skills, with the ability to work effectively in a fast-paced, cross-functional international startup environment
Extensive use of AI tools for coding, optimization, and ideation

Preferred Qualifications

Experience with medical imaging, digital pathology, or whole slide image (WSI) processing
Experience with LoRAs, transformer architecture and state of the art image to image translation models (Flux 2, Z-Image) and the Hugging face ecosystem
Background in generative models and fine-tuning of foundation models
Experience with GPU acceleration and optimization, including CUDA kernel engineering, TensorRT/ONNX export, and inference serving frameworks such as Triton
Experience with hosting computer vision model inference on NVIDIA DGX Spark.
Understanding of FDA regulatory requirements for AI/ML in medical devices
Experience with MLOps tools (MLflow, Kubeflow) and model versioning practices
Develop tools and frameworks to streamline ML research workflows, experimentation, and reproducibility

What We Offer

The opportunity to work on technology that directly improves patient outcomes and transforms clinical diagnostics, alongside a talented team of engineers and researchers pushing the boundaries of AI in healthcare. You will have the freedom to pursue high-impact research while seeing your work deployed at scale in real clinical environments.