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Remote Research Computing Jobs in California (NOW HIRING)

DigitalOcean

Financial Reporting Manager

San Francisco, CA · Remote

$112K - $141K/yr

... cloud computing and AI. You'll be part of a high-performing team that ensures completeness and ... Conduct peer disclosure research with consideration for U.S. GAAP requirements as well as peer ...

Adidev Technologies Inc

Data Science

San Francisco, CA · Remote

S. in Computer Science, Computational Physics, Operations Research, Geospatial Sciences, Remote ... Distributed computing tools and cloud technology (AWS) QUALIFICATIONS * Degree in Data Science ...

Reflection

Trade Compliance Counsel

San Francisco, CA · On-site +1

Our team of AI researchers and company builders come from DeepMind, OpenAI, Google Brain, Meta ... collaborations, remote access, restricted-party screening, and the transfer of controlled ...

Mantari

Software Engineer

Torrance, CA · On-site +1

Translate novel research prototypes into production-grade code You'll work closely with Mantari ... Experience with numerical weather prediction, remote-sensing data, or geospatial intelligence

Ambitionary

Quantitative Modeler

San Francisco, CA · On-site +1

... Job Location Remote Time Commitment Less than 20 hours per week, but gradually growing Type of ... You will do your own research, while working alongside our financial industry expertise, and apply ...

Leidos

Software Engineer

San Diego, CA · On-site +1

$69K - $125K/yr

Our San Diego research and engineering team tackles some of the most complex challenges in national defense using advanced signal processing, ocean remote sensing, and high-performance computing . We ...

Orbofi

Conducting research on the latest advancements in AI/ML and incorporating those findings into ... Experience with distributed computing and parallel processing frameworks, such as Apache Spark and ...

Together AI

Solutions Architect

San Francisco, CA · On-site +1

$180/hr

Deliver high-value feedback to our Product, Engineering, and Research teams, ensuring our platform ... performance computing (HPC) environments. * Strong understanding of training, fine-tuning and ...

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Remote Research Computing information

What are the key skills and qualifications needed to thrive in Remote Research Computing, and why are they important?

To excel in Remote Research Computing, you need a strong background in computer science, data analysis, and scientific research methods, often supported by an advanced degree in a relevant field. Familiarity with high-performance computing (HPC) systems, cloud platforms, programming languages (such as Python or R), and experience with collaborative tools are typically required. Excellent problem-solving, communication, and self-management skills help professionals work effectively across virtual teams and complex projects. These skills and qualities are vital to ensure efficient research workflows, accurate data handling, and successful collaboration in a remote, technology-driven environment.

What is remote research computing?

Remote research computing refers to the use of computing resources, such as high-performance computers or cloud-based systems, that can be accessed from anywhere via the internet. This enables researchers to perform complex data analysis, simulations, or computational experiments without being physically present at the location of the hardware. It is especially useful for collaborative projects, large datasets, and situations where local computing power is insufficient. Researchers use secure connections and specialized software to interact with these remote resources efficiently.

What are some common challenges faced by professionals working in Remote Research Computing, and how can they be addressed?

Professionals in Remote Research Computing often encounter challenges such as ensuring secure and efficient data transfer, collaborating with geographically dispersed teams, and troubleshooting computational issues without in-person support. To address these challenges, it's important to become proficient with remote collaboration tools, maintain clear communication with team members, and stay updated on best practices for data security and cloud resource management. Many organizations also provide virtual onboarding and regular training sessions to help remote research computing staff adapt to evolving technologies and workflows.
What are the most commonly searched types of Research Computing jobs in California? The most popular types of Research Computing jobs in California are:
What are popular job titles related to Remote Research Computing jobs in California? For Remote Research Computing jobs in California, the most frequently searched job titles are:
What job categories do people searching Remote Research Computing jobs in California look for? The top searched job categories for Remote Research Computing jobs in California are:
What cities in California are hiring for Remote Research Computing jobs? Cities in California with the most Remote Research Computing job openings:
Remote Research Computing jobs near you

Senior Machine Learning Engineer

Career Renew

Los Angeles, CA • Remote

$165K - $225K/yr

Full-time

Re-posted 21 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.