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Remote Scientific Computing Jobs in Georgia (NOW HIRING)

Career Renew

Senior Machine Learning Engineer

Atlanta, GA · Remote

$165K - $225K/yr

... remote role for US/Canada based candidates. Salary range: 165-225K USD yearly plus benefits plus ... scientific computing environments a plus Strong mathematical foundation in linear algebra ...

Kaleris

Software Engineer

Alpharetta, GA · On-site +1

... computing, Redis/Resque, JSON and other queueing systems to integrate our applications with ... Bachelor's degree from an accredited university in computer science or related * Excellent verbal ...

Kaleris

Software Engineer

Alpharetta, GA · On-site +1

... computing, Redis/Resque, JSON and other queueing systems to integrate our applications with ... Bachelor's degree from an accredited university in computer science or related * Excellent verbal ...

TIER4 GROUP

Production Support Engineer

Atlanta, GA · On-site +1

$40.50 - $52.75/hr

... remote Job Summary: We are seeking a dedicated and detail-oriented Production Support Analyst ... Qualifications: · Bachelor's degree in Computer Science, Information Technology, or a related ...

Aflac Incorporated

Global Sec Assurance Consult

Columbus, GA · On-site +1

$108K - $135K/yr

If the role is remote, there may be occasions that you are requested to come to the office based on ... Knowledge of cloud computing technologies and security best practices Education & Experience ...

New

Trimble

SQL Support Specialist

Atlanta, GA · On-site +1

Experience using remote desktop takeover tools like Bomgar, Zoom, or WebEx for technical support ... Cloud Computing, Azure, AWS, Crystal Reports, Database Performance Tuning, Technical Support ...

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Remote Scientific Computing information

What is the difference between Remote Scientific Computing vs Remote Data Analysis?

AspectRemote Scientific ComputingRemote Data Analysis
Required CredentialsTypically requires degrees in science, engineering, or computer science; knowledge of programming and simulation toolsOften requires degrees in statistics, data science, or related fields; proficiency in data manipulation and visualization
Work EnvironmentResearch labs, academic institutions, or corporate R&D; often involves high-performance computingBusiness, finance, healthcare sectors; primarily involves analyzing datasets and generating reports
Employer & Industry UsageResearch institutions, tech companies, scientific organizationsFinancial firms, healthcare providers, marketing agencies

Remote Scientific Computing focuses on developing and running simulations, models, and scientific computations, often requiring specialized software and high-performance hardware. Remote Data Analysis centers on interpreting datasets, creating visualizations, and deriving insights, typically using statistical tools. While both roles involve data and programming, their core tasks and industries differ significantly.

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

Senior Machine Learning Engineer

Career Renew

Atlanta, GA • Remote

$165K - $225K/yr

Full-time

Posted 20 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.