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Remote Ml Researcher Jobs in Texas (NOW HIRING)

Tamus

Postdoctoral Research Associate

El Paso, TX · On-site +1

Responsibilities: * Develop, implement, and validate remote-sensing-enabled models for ... Design and implement AI(ML/DL)-based predictive frameworks to assess water availability, drought ...

SpyCloud

Senior Data Scientist, Applied ML

Austin, TX · On-site +1

$154K - $200K/yr

This role sits deliberately at the intersection of research and deployment, not on one side of it ... In addition to our engaging workspace in South Austin, flexible and remote-friendly work options ...

Tamus

Postdoctoral Research Associate

Temple, TX · On-site +1

... remote sensing, and geospatial modeling. The ideal candidate will work and collaborate closely ... The researcher will develop profit-risk-environment tradeoff products, build reusable R/Python ...

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Remote Ml Researcher information

What is a remote ML researcher?

Remote ML Researchers are professionals who specialize in machine learning (ML) and conduct their research while working outside of a traditional office environment, often from home or other remote locations. They design, implement, and evaluate algorithms, models, and systems to solve complex data problems, collaborating virtually with teams and stakeholders. Their work can span industries like technology, healthcare, finance, and more, and typically involves tasks such as data analysis, model development, and publishing research findings. Remote ML Researchers rely heavily on digital communication tools and cloud-based platforms to share ideas, code, and results.

What are the key skills and qualifications needed to thrive as a remote ML researcher, and why are they important?

To thrive as a Remote ML Researcher, you need a strong background in machine learning theory, programming (Python, TensorFlow, PyTorch), and a relevant degree such as computer science or statistics. Familiarity with cloud computing platforms, distributed systems, version control (Git), and experience publishing in peer-reviewed journals are commonly required. Excellent problem-solving skills, self-motivation, and effective written communication are crucial for collaborating remotely and sharing findings. These skills ensure high-quality research output, successful collaboration in distributed teams, and ongoing advancement in the fast-evolving field of machine learning.

What are some common challenges faced by remote ML researchers and how can they overcome them?

Remote ML researchers often encounter challenges such as limited real-time collaboration, managing large datasets remotely, and ensuring consistent communication with their teams. Overcoming these obstacles typically involves leveraging collaboration tools (like Slack, GitHub, and Zoom), setting up efficient remote access to computational resources, and establishing regular check-ins with colleagues. Building a proactive communication routine and sharing progress updates can help maintain team alignment and foster a sense of connection, even when working from different locations.

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Remote Ml Researcher jobs near you

SME - Nuclear Power Plant Technology & Cybersecurity (AI/ML)

Def-Logix, Inc.

San Antonio, TX • Remote

$53 - $70.75/hr

Part-time

Posted 4 days ago

Job description

YOUR ROLE

As the NPP technology and cybersecurity SME, you would be the team's authority on how nuclear plant systems work and how NRC cybersecurity requirements apply to them. Specifically, you would help:

  • Assess existing literature and technical basis on AI/ML use in the nuclear industry and identify representative use cases. The NRC has clarified it wants deeper analysis of a smaller number of use cases rather than a broad survey.
  • Identify unique cybersecurity considerations: new attack vectors, vulnerabilities, and mitigation strategies for AI/ML in critical digital assets.
  • Evaluate those considerations against Regulatory Guide 5.71, identifying where controls could be enhanced and where gaps exist.
  • Contribute to a draft evaluation framework covering criteria for secure AI/ML use, applicable security controls, and any control tailoring. The framework should account for guidance in RG 5.71 and requirements in 10 CFR 73.110 and RG 5.96
  • Contribute to interim letter reports and the final technical letter report.

Scope notes from the NRC's Q&A: AI/ML embedded in COTS digital I&C products is in scope. Lessons from other safety-critical sectors, particularly industrial control systems and critical infrastructure, are encouraged. No hands-on testing or evaluation of AI/ML implementations is expected; this is a literature-based and analytical effort.

MINIMUM QUALIFICATIONS
  • Demonstrated experience performing work related to NPP cybersecurity, including regulation, research, evaluation, or implementation of NPP cybersecurity programs
  • Demonstrated knowledge of NPP systems and the application of cybersecurity programs to those systems
  • Detailed knowledge of NRC NPP cybersecurity regulations and guidance (10 CFR 73.54, RG 5.71; familiarity with the proposed Part 53 / 73.110 framework is valuable)
  • Hands-on experience physically working inside a nuclear power plant, not only classroom or desk-based exposure
  • Direct experience identifying digital assets subject to 10 CFR 73.54
  • Direct experience mapping those assets against RG 5.71 appendix controls
  • A track record of producing findings packages delivered to regulatory staff
  • This experience can come from a consulting firm, a utility, or a lab - what matters is having actually applied these controls in a real plant
TIME COMMITMENT & LOGISTICS
  • Period of performance: immediately through 26 August 2027
  • Level of effort: Part-time
  • Location: Remote with one (1) possible travel to customer site
  • Clearance/access: No clearance or plant site access is required 
ABOUT THE PROJECT

Client: U.S. Nuclear Regulatory Commission (NRC), Office of Nuclear Regulatory Research

The NRC is funding research to establish a technical basis for addressing cybersecurity concerns raised by AI/ML technologies in commercial nuclear power plants, both currently operating reactors and new/advanced designs. The findings are intended to inform regulatory guidance that is risk-informed, technology-inclusive, and performance-based. 


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