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Machine Learning Contract Remote Jobs in Georgia

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 fully remote role for US/Canada based candidates. Salary range: 165-225K USD yearly plus benefits plus ...

PrizePicks

Machine Learning Platform Engineer

Atlanta, GA · On-site +1

$155K - $185K/yr

Design and build the end-to-end machine learning infrastructure, setup platform for transitioning ... S. and are willing to consider remote candidates. #LI-Remote Working at PrizePicks: The typical ...

FIS

Data Scientist Sr Lead

Atlanta, GA · On-site +1

... remote): Atlanta, GA, Columbus, GA or Jacksonville, FL What you will be doing * Lead the design, development, validation, deployment, and monitoring of advanced analytics, machine learning, and AI ...

Worldpay, Inc.

Data Scientist Sr Lead

Atlanta, GA · On-site +1

... remote): Atlanta, GA, Columbus, GA or Jacksonville, FL What you will be doing * Lead the design, development, validation, deployment, and monitoring of advanced analytics, machine learning, and AI ...

Home Depot

Senior AI Engineer (Remote)

Atlanta, GA · On-site +1

$99K - $136K/yr

Operating at the intersection of Data Science, Machine Learning Engineering, and Software Engineering, this hands-on role translates AI concepts into enterprise-ready products. This role involves ...

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Machine Learning Contract Remote information

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How much do machine learning contract remote jobs pay per hour?

As of Aug 10, 2026, the average hourly pay for machine learning contract remote in Georgia is $21.52, according to ZipRecruiter salary data. Most workers in this role earn between $13.65 and $25.00 per hour, depending on experience, location, and employer.

What are common challenges faced by remote machine learning contractors, and how can they be addressed?

Remote machine learning contractors often face challenges such as managing communication across time zones, accessing necessary data securely, and staying aligned with the client's project expectations. To address these, it’s important to establish clear communication channels, use secure data transfer protocols, and schedule regular check-ins with project stakeholders. Building strong documentation habits and leveraging collaborative tools like version control or shared notebooks can also help ensure smooth workflow and project transparency.

What skills and qualifications are needed to thrive as a machine learning contractor in a remote role?

To thrive as a Machine Learning Contractor working remotely, you need strong proficiency in mathematics, programming (typically Python), and a solid understanding of machine learning algorithms, usually supported by a relevant degree or equivalent experience. Familiarity with tools and frameworks like TensorFlow, PyTorch, scikit-learn, and cloud platforms such as AWS or Azure is essential, as well as experience with version control systems like Git. Excellent self-motivation, time management, and communication skills help you effectively collaborate with distributed teams and manage multiple projects independently. These competencies are crucial for delivering high-quality, scalable solutions and meeting client expectations in a flexible, remote work environment.

What is a machine learning contract remote job?

Machine learning contract remote jobs are temporary work opportunities where professionals use machine learning techniques to solve problems for organizations, but do so remotely, often from home or another location. These roles typically involve building, training, and deploying models, analyzing data, and collaborating with teams virtually. Contracts can vary in length and scope, allowing flexibility for both the employer and the worker. These positions are ideal for individuals seeking project-based work or more flexible schedules, and require strong technical skills and the ability to communicate effectively online.

What is the difference between Machine Learning Contract Remote vs Data Scientist Contract Remote?

AspectMachine Learning Contract RemoteData Scientist Contract Remote
Required CredentialsDegree in Computer Science, Data Science, or related field; experience with ML frameworksDegree in Statistics, Data Science, or related; proficiency in data analysis tools
Work EnvironmentRemote, project-based, often collaborative with ML engineersRemote, analytical, often cross-functional teams
Employer & Industry UsageTech companies, AI startups, research institutionsTech firms, finance, healthcare, consulting
Common Search & ComparisonYesYes

Machine Learning Contract Remote roles focus on developing and deploying ML models, requiring specialized skills in algorithms and frameworks. Data Scientist Contract Remote positions emphasize data analysis, statistical modeling, and insights generation. While both roles often work remotely and share similar credentials, their core responsibilities differ, making this comparison useful for job seekers exploring related opportunities.

What are popular job titles related to Machine Learning Contract Remote jobs in Georgia? For Machine Learning Contract Remote jobs in Georgia, the most frequently searched job titles are:
What job categories do people searching Machine Learning Contract Remote jobs in Georgia look for? The top searched job categories for Machine Learning Contract Remote jobs in Georgia are:
What cities in Georgia are hiring for Machine Learning Contract Remote jobs? Cities in Georgia with the most Machine Learning Contract Remote job openings:
Machine Learning Contract Remote jobs near you

Senior Machine Learning Engineer

Career Renew

Atlanta, GA • Remote

$165K - $225K/yr

Full-time

Re-posted 19 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.