machine learning researcher

Decompute

<div>
 <p>
  <strong>
   Company Description
  </strong>
 </p>
 <p>
 </p>
 <p>
  Decompute is a pioneer in the development of AI models optimized for local environments, requiring as little as 1 GB of VRAM. Known for our BlackBird platform, which enables the creation of on-device AI agents, we specialize in areas such as finance, legal, coding, and creative tasks like voice and image generation. Our breakthrough multimodal AI models are renowned for outperforming cloud-dependent competitors, even while operating entirely offline on consumer-grade devices. With a model footprint as small as 350M parameters, Decompute&rsquo;s solutions are efficient, scalable, and tailored for CPUs and GPUs. Join us in revolutionizing on-device AI as we empower enterprises with next-generation advancements in technology.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Role Description
  </strong>
 </p>
 <p>
 </p>
 <p>
  This is a full-time, on-site role located in Cupertino, CA, for an AI Researcher. The AI Researcher will lead cutting-edge research in artificial intelligence, focusing on the development and optimization of advanced models for on-device use. Daily responsibilities include conducting in-depth research, designing algorithms, implementing machine learning solutions, analyzing datasets, and collaborating with cross-functional teams to innovate and refine AI systems.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Qualifications
  </strong>
 </p>
 <p>
 </p>
 <ul>
  <li>
   Strong foundation in ML and CS, including model architectures, programming and algorithm design
  </li>
 </ul>
 <p>
  Experience training, evaluating, and improving advanced models in production or research settings
 </p>
 <ul>
  <li>
   Experience in Research and implementation of AI-focused technologies
  </li>
  <li>
   Proficiency in Statistics for data analysis and model evaluation
  </li>
  <li>
   Strong problem-solving capabilities and aptitude for innovation
  </li>
  <li>
   Good communication skills for collaboration and presentation of research insights
  </li>
  <li>
   Experience with on-device AI or resource-efficient machine learning is a plus
  </li>
  <li>
   Ph.D. or Master's degree in Computer Science, Artificial Intelligence, or related field preferred
  </li>
 </ul>
</div>


AI Researcher

University of North Carolina at Chapel Hill

<div><div><div>Posting Details
</div><p></p><div><div><div><div><b>Department</b></div> <div>School of Data Sci and Society - 397100</div>
</div><br>
<div><div><b>Posting Open Date</b></div> <div>04/13/2026</div>
</div><br>
<div><div><b>Application Deadline</b></div> <div>06/15/2026</div>
</div><br>
<div><div><b>Open Until Filled</b></div> <div>No</div>
</div><br>
<div><div><b>Position Type</b></div> <div>Postdoctoral Scholar</div>
</div><br>
<div><div><b>Position Title</b></div> <div>Postdoctoral Research Associate</div>
</div><br>
<div><div><b>Vacancy ID</b></div> <div>PDS004832</div>
</div><br>
<div><div><b>Full-time/Part-time</b></div> <div>Full-Time Temporary</div>
</div><br>
<div><div><b>Hours per week</b></div> <div>40</div>
</div><br>
<div><div><b>FTE</b></div> <div>1</div>
</div><br>
<div><div><b>Work Location</b></div> <div>Chapel Hill, NC</div>
</div><br>
<div><div><b>Position Location</b></div> <div>North Carolina, US</div>
</div><br>
<div><div><b>Hiring Range</b></div> <div>$62,000 - $65,000, dependent on experience</div>
</div><br>
<div><div><b>Proposed Start Date</b></div> <div>08/03/2026</div>
</div><br>
<div><div><b>Estimated Duration of Appointment</b></div> <div>12 Months</div>
</div><br>
</div></div></div><div><div>Position Information
</div><p></p><div><div><div><div><b>Be a Tar Heel!</b></div>
<div><div>A global higher education leader in innovative teaching, research and public service, the <b>University of North Carolina at Chapel Hill</b> consistently ranks as <b>one of the nation&rsquo;s top public universities</b> and is among is the top ten research universities in the nation for federal research expenditures as well as for federally funded social and behavioral sciences research and development.</div><div>
Here at Carolina, our highly skilled postdocs play a vital role in our research enterprise and towards our overall commitment to research excellence. Across many disciplines, postdocs contribute to the intellectual vitality of the University. They provide innovative ideas and perspectives, foster a stimulating research environment and advance knowledge within their fields. Postdocs are crucial members of our scientific research workforce, contributors to our research outputs and an important reason why Carolina is one of the leading public research institutions in the country.</div><div>
UNC-Chapel Hill offers postdocs comprehensive <b>medical and vision coverage</b>, paid leave, and <b>benefits and services</b> that support professional development and a healthy work/life balance. Chapel Hill regularly ranks as one of the best college towns and best places to live in the United States, a reputation guided by the diverse social, cultural, recreation and professional opportunities that span the campus and community.</div></div>
</div><br>
<div><div><b>Primary Purpose of Organizational Unit</b></div>
<div><div>The School of Data and Information Sciences (SDIS) was founded in 2026 by joining the School of Information and Library Science (1931) and the School of Data Science and Society (2022). SDIS brings together longstanding excellence in information and library science with leadership in data science, including computational and statistical methods, and societal applications.<br>
</div><div>Home to a top ranked library and information science program, SDIS is recognized for strong teaching, research and service and is the home to the Carolina Health Informatics Program (CHIP) and the Center for Information, Technology and Public Life (CITAP). The School teaches and trains undergraduate and graduate students in data science, health informatics, information science and library science, as well as offers certificates for adult learners.<br>
</div><div>SDIS conducts research and scholarship across the data and information life cycle, with an emphasis on human-centric, ethical and applied approaches. Committed to data, information and AI literacy for all Tar Heels, SDIS faculty are tackling our communities&rsquo; most pressing needs and our world&rsquo;s most intractable challenges.<br>
</div></div>
</div><br>
<div><div><b>Position Summary</b></div>
<div>The Zikry Lab at UNC-Chapel Hill is seeking a highly motivated Postdoctoral Research Fellow to join our group at the intersection of statistical methodology, machine learning, and biomedical data science. Our research develops rigorous and interpretable methods for high-dimensional biomedical data, with applications spanning cancer genomics, neuroscience, and precision medicine. The postdoctoral fellow will join a collaborative, interdisciplinary environment within the School of Data and Information Sciences, with opportunities to work with researchers across the UNC scientific community in units such as the Computational Medicine Program and the Lineberger Comprehensive Cancer Center.<br>
<br>
The postdoctoral fellow will play a central role in advancing new statistical and machine learning methodology. Areas of focus include (but are not limited to):<br>
<ul><li>Clustering and unsupervised learning</li><li>Dimension reduction and manifold learning</li><li>High-dimensional inference and feature selection</li><li>Generative modeling and digital twins</li><li>Reliability and interpretability of ML methods</li></ul><br>
The postdoctoral researcher will be responsible for teaching at most one class per semester (an existing 100-level data science course with lecture and evaluation material provided). Based on teaching needs, this may be changed to one class per academic year.</div>
</div><br>
<div><div><b>Minimum Education and Experience Requirements</b></div>
<div>Ph.D. (or foreign equivalent) in statistics, mathematics, biostatistics, bioinformatics, computational biology, or a related field of study.</div>
</div><br>
<div><div><b>Required Qualifications, Competencies, and Experience</b></div>
<div>N/A</div>
</div><br>
<div><div><b>Preferred Qualifications, Competencies, and Experience</b></div>
<div>Experience in statistical modeling and biomedical data analysis is a plus. Evidence of research productivity (publications, presentations, software) is preferred.</div>
</div><br>
<div><div><b>Special Physical/Mental Requirements
</b></div><div>N/A</div></div></div></div></div></div><p></p>

Postdoctoral Research Associate

Apple

<p>Are you excited to tackle some of the most ambitious technical challenges in Apple Intelligence? Be involved in collaborating closely with our machine learning researchers, engineers, and data scientists? Together, you will orchestrate groundbreaking research initiatives and develop transformative products designed to build a significant impact for billions of users worldwide!<br>
<br>
As part of Apple's AI and Machine Learning org, we encourage and create groundbreaking technology for large-scale ML systems, computer vision, natural language processing, and multi-modal understanding and generation. The Data and Machine Learning Innovation (DMLI) team is looking for Machine Learning Engineer to explore new methods, challenge existing metrics or protocols, and develop new insightful practices that will change how we understand data and overcome real-world ML challenges.<br>
<br>
<b>Description</b><br>
<br>
As a Machine Learning (ML) Engineer, you will be entrusted with the critical role of innovating and applying state of the art research in ML to tackle complex data problems. The solutions you develop will significantly impact Apple Intelligence, future Apple products and the broader ML development ecosystem.<br>
<br>
You will work with a multidisciplinary team to actively participate in Apple Intelligence&rsquo;s data-model co-design and co-develop practice. Your responsibilities will extend to the design and development of a comprehensive data generation and curation framework for Apple Intelligence foundation models at Apple. You will also be responsible to build robust model evaluation pipelines, integral to the continuous improvement and assessment of Apple Intelligence foundation models.<br>
<br>
Furthermore, you will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues.<br>
<br>
Your work may span a variety of directions, including but not limited to:<br>
<br>
Develop and implement techniques for creating high-quality synthetic datasets across a variety of domains, including vision, text, and audio data.<br>
<br>
Innovate and experiment with new approaches for synthetic data generation to improve the diversity, realism, and representativeness of datasets.<br>
<br>
Collaborate with multi-functional teams to understand data requirements and ensure that synthetic datasets are optimized for training foundation models.<br>
<br>
Crafting and implementing semi-supervised, self-supervised representation learning techniques for growing the power of both limited labeled data and large-scale unlabeled data.<br>
<br>
Develop pipelines and tools to automate synthetic data generation for large-scale AI experiments.<br>
<br>
Stay updated with the latest research and industry trends in synthetic data generation, foundational model training, and large-scale data engineering.<br>
<br>
<b>Preferred Qualifications</b><br>
<br>
3+ years of experience with developing and evaluating ML applications, and demonstrated experience in understanding and improving data quality.<br>
<br>
Strong publication record in relevant conferences (e.g. CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, , etc)<br>
<br>
Strong problem-solving and communication skills.<br>
<br>
Ph.D/MS degree in Machine Learning, Natural Language Processing, Computer Vision, Data Science, Statistics or related areas<br>
<br>
<b>Minimum Qualifications</b><br>
<br>
Demonstrated expertise in computer vision, natural language processing, and machine learning with a passion for data-centric machine learning.<br>
<br>
Deep understanding in multi-modal foundation models.<br>
<br>
Staying on top of emerging trends in generative AI and multi-modal LLM.<br>
<br>
Strong programming skills and hands-on experience using the following languages or deep learning frameworks: Python, PyTorch, or Jax.<br>
<br>
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant .<br>
<br>
<b>Pay &amp; Benefits</b><br>
<br>
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $147,400 and $272,100, and your base pay will depend on your skills, qualifications, experience, and location.<br>
<br>
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits.<br>
<br>
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.</p>

AIML - Machine Learning Engineer, Data and ML Innovation

<p>Are you excited to tackle some of the most ambitious technical challenges in Apple Intelligence? Be involved in collaborating closely with our machine learning researchers, engineers, and data scientists?<br>
<br>
Together, you will orchestrate groundbreaking research initiatives and develop transformative products designed to build a significant impact for billions of users worldwide!<br>
<br>
As part of Apple&rsquo;s AI and Machine Learning org, we encourage and create groundbreaking technology for large-scale ML systems, computer vision, natural language processing, and multi-modal understanding and generation.<br>
<br>
The Data and Machine Learning Innovation (DMLI) team is looking for Machine Learning Engineer to explore new methods, challenge existing metrics or protocols, and develop new insightful practices that will change how we understand data and overcome real-world ML challenges.<br>
<br>
<b>Description</b><br>
<br>
As a Machine Learning (ML) Engineer, you will be entrusted with the critical role of innovating and applying state of the art research in ML to tackle complex data problems.<br>
<br>
The solutions you develop will significantly impact Apple Intelligence, future Apple products and the broader ML development ecosystem.<br>
<br>
You will work with a multidisciplinary team to actively participate in Apple Intelligence&rsquo;s data-model co-design and co-develop practice.<br>
<br>
Your responsibilities will extend to the design and development of a comprehensive data generation and curation framework for Apple Intelligence foundation models at Apple.<br>
<br>
You will also be responsible to build robust model evaluation pipelines, integral to the continuous improvement and assessment of Apple Intelligence foundation models.<br>
<br>
Furthermore, you will have the opportunity to showcase your groundbreaking research work by publishing and presenting at premier academic venues.<br>
<br>
&quot;,&quot;responsibilities&quot;:&quot;Develop and implement techniques for creating high-quality synthetic datasets across a variety of domains, including vision, text, and audio data.<br>
<br>
Innovate and experiment with new approaches for synthetic data generation to improve the diversity, realism, and representativeness of datasets.<br>
<br>
Collaborate with multi-functional teams to understand data requirements and ensure that synthetic datasets are optimized for training foundation models.<br>
<br>
Crafting and implementing semi-supervised, self-supervised representation learning techniques for growing the power of both limited labeled data and large-scale unlabeled data.<br>
<br>
Develop pipelines and tools to automate synthetic data generation for large-scale AI experiments.<br>
<br>
Stay updated with the latest research and industry trends in synthetic data generation, foundational model training, and large-scale data engineering.<br>
<br>
<b>Preferred Qualifications</b><br>
<br>
3+ years of experience with developing and evaluating ML applications, and demonstrated experience in understanding and improving data quality.<br>
<br>
Strong publication record in relevant conferences (e.g. CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, etc).<br>
<br>
Strong problem-solving and communication skills.<br>
<br>
Experience of multimodal large language model or image/video generation model training is a plus.<br>
<br>
Ph.D/MS degree in Machine Learning, Natural Language Processing, Computer Vision, Data Science, Statistics or related areas.<br>
<br>
<b>Minimum Qualifications</b><br>
<br>
Demonstrated expertise in computer vision, natural language processing, and machine learning with a passion for data-centric machine learning.<br>
<br>
Deep understanding in multi-modal foundation models.<br>
<br>
Staying on top of emerging trends in generative AI and multi-modal LLM.<br>
<br>
Strong programming skills and hands-on experience using the following languages or deep learning frameworks: Python, PyTorch, or Jax.<br>
<br>
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant .<br>
<br>
<b>Pay &amp; Benefits</b><br>
<br>
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $171,600 and $302,200, and your base pay will depend on your skills, qualifications, experience, and location.<br>
<br>
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits.<br>
<br>
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.</p>

AIML - Sr Machine Learning Engineer, Data and ML Innovation

Oak Ridge National Laboratory

<div><p>Requisition Id 15490</p><div><b>
Overview:
</b></div><div>The Multimodal Sensor Analytics group in the Electrification and Energy Infrastructure Division (EEID) is seeking a postdoctoral researcher with proven expertise in computed tomography (CT) reconstruction, including sparse-view and limited-angle algorithms, and the application of advanced machine learning (ML) and computational imaging methods to scientific and industrial imaging data. You will directly contribute to developing and deploying algorithms for multi-modal tomography (X-ray, neutron, and electron), advancing methods for non-destructive evaluation (NDE) and scientific imaging. This position emphasizes bridging cutting-edge ML/AI with real-world imaging systems in collaboration with experimental scientists, leveraging facilities such as the Manufacturing Demonstration Facility (MDF). This role offers a unique opportunity to drive impactful research on sparse scientific imaging while building a strong research profile in computational imaging and ML for CT.</div><div><br>
</div><div><b>Major Duties/Responsibilities:
</b></div><ul><li>Lead research on sparse-view and limited-angle CT algorithms for scientific and industrial applications.</li><li>
Develop and apply ML/AI-driven computational imaging methods (e.g., deep learning, implicit neural representations, diffusion models) for CT reconstruction, enhancement, and defect detection.</li><li>
Advance algorithms for multi-modal tomography (X-ray, neutron, electron).</li><li>
Collaborate with experimentalists to validate methods on real-world systems.</li><li>
Publish in high-impact journals and present at leading national and international conferences.</li><li>
Ensure compliance with environment, safety, health, and quality program requirements.</li><li>
Uphold strong values and ethics in collaborative research.</li><li>
Deliver ORNL&rsquo;s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace &ndash; in how we treat one another, work together, and measure success.</li></ul><div><br>
</div><div><b>Basic Qualifications:</b></div><ul><li>
Ph.D. in electrical engineering, computer science, or related discipline completed within the last five years.</li><li>
Demonstrated expertise in computed tomography (CT), with experience in sparse-view and/or limited-angle reconstruction and published research in this area.</li><li>
Experience applying ML/AI algorithms to scientific imaging data (e.g., CT, microscopy, multi-modal datasets).</li><li>
A strong record of productive and creative research, demonstrated by peer-reviewed publications and conference presentations.</li></ul><div></div><div><b><br>
Preferred Qualifications:</b></div><ul><li>
Hands-on experience with ML/AI frameworks (PyTorch, TensorFlow) for tomographic reconstruction and analysis.</li><li>
Familiarity with physics-informed, plug-and-play (PnP), or generative models for imaging.</li><li>
Prior experience with multi-modal tomography (X-ray, neutron, electron).</li><li>
Knowledge of high-performance computing or cloud environments for large-scale data.</li><li>
Strong collaboration skills and ability to work in interdisciplinary teams.</li></ul><div><b><br>
</b></div><div><b>Special Requirements:
</b></div><div>Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will be up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding.</div><div><br>
</div><div>Please submit three letters of reference when applying to this position. You may upload these directly to your application or have them sent to Postdocrecruitment@ornl.gov with the position title and number referenced in the subject line.</div><div></div><div><br>
Instructions to upload documents to your candidate profile:</div><ul><li>
Login to your account via jobs.ornl.gov</li><li>
View Profile</li><li>
Under the My Documents section, select Add a Document</li></ul><div></div><div><b><br>
Security, Credentialing, and Eligibility Requirements:</b></div><div>
For employment at Oak Ridge National Laboratory (ORNL), a Real ID compliant form of identification will be required. Additionally, ORNL is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as mandated by Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which requires a favorable post-employment background investigation.</div><div></div><div><br>
To obtain this credential, new employees must successfully complete and pass a Federal Tier 1 background check investigation. This investigation includes a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last year. This includes marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.</div><div></div><div><br>
For foreign national candidates:</div><div>
If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) risk determination to maintain employment. Once you meet the three-year residency requirement, you will be required to obtain a PIV credential to maintain employment.</div><div><br>
</div><div><b>About ORNL:</b></div><div>
As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation&rsquo;s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top choice for employment. These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation.</div><div></div><div><br>
ORNL offers competitive pay and benefits programs to attract and retain individuals who demonstrate exceptional work behaviors. The laboratory provides a range of employee benefits, including medical and retirement plans and flexible work hours, to support the well-being of you and your family. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also available for added convenience.</div><div></div><div><br>
Other benefits include the following: Prescription Drug Plan, Dental Plan, Vision Plan, 401(k) Retirement Plan, Contributory Pension Plan, Life Insurance, Disability Benefits, Generous Vacation and Holidays, Parental Leave, Legal Insurance with Identity Theft Protection, Employee Assistance Plan, Flexible Spending Accounts, Health Savings Accounts, Wellness Programs, Educational Assistance, Relocation Assistance, and Employee Discounts.</div><div></div><div><br>
If you have difficulty using the online application system or need an accommodation to apply due to a disability, please email: ORNLRecruiting@ornl.gov or call 1.866.963.9545.</div><p></p><div><br>
 #LI-DC1</div><p>
This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.</p><p>
We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.</p><p><br>
If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov.</p><p><br>
ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT-Battelle is an E-Verify employer.</p></div>

Postdoctoral Research Associate - ML & CI for Sparse & Multi-Modal Computed Tomography

GenBio AI

<div>
 Headquartered in Silicon Valley, we are a newly established start-up where a collective of visionary scientists, engineers, and entrepreneurs are dedicated to transforming the landscape of biology and medicine through the power of generative AI. Our team comprises leading minds and innovators in AI and biological science, pushing the boundaries of what is possible. We are dreamers who reimagine a new paradigm for biology and medicine.
 <br/>
 <br/>
 We are committed to decoding biology holistically and enabling the next generation of life-transforming solutions. As the first mover in pan-modal Large Biological Models (LBM), we are pioneering a new era of biomedicine, with our LBM training leading to ground-breaking advancements and a transformative approach to healthcare. Our robust R&amp;D team and leadership in LLMs and generative AI position us at the forefront of this revolutionary field. With headquarters in Silicon Valley, California, and a branch office in Paris and Abu Dhabi, we are poised to make a global impact. Join us as we embark on this journey to redefine the future of biology and medicine through the transformative power of Generative AI.
 <br/>
 <br/>
 <strong>
  Job Description:
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   You will work with the team to conduct cutting-edge research in AI, foundation models, and computational biology. Your primary tasks will include improving existing models and exploring new methodologies to advance our AI capabilities in biology
  </li>
  <li>
   You will collaborate with the team on designing and executing large-scale experiments, analyzing complex datasets, and applying statistical techniques to validate the performance and robustness of AI systems
  </li>
  <li>
   Additionally, you will work closely with AI/machine learning researchers and computational biologists to develop Genbio AI&rsquo;s state-of-the-art biology foundation models and drive the research agenda to generate impact
   <br/>
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Qualification:
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Currently enrolled in a full-time master's or PhD (preferred) program in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field (preferably in the United States)
  </li>
  <li>
   A strong coder with excellent skills in C/C++ and Python
  </li>
  <li>
   Fluent in deep learning frameworks like PyTorch (and/or JAX), Hugging Face (Datasets, Accelerate, Transformers, etc.), Megatron-LM, DeepSpeed, etc
  </li>
  <li>
   Have a solid understanding of GPU, CPU, or other AI accelerator architectures
  </li>
  <li>
   Familiar with LLM (and/or other foundation model) architectures (such as attention mechanisms, state-space models, MoE, etc.) and training infrastructure (e.g., large-scale GPU clusters)
  </li>
  <li>
   Have experience improving ML accuracy using low-precision formats
  </li>
  <li>
   Have 1+ years of relevant industry experience
  </li>
  <li>
   Derive a great deal of satisfaction from every percentage point of performance improvement
  </li>
  <li>
   Have experience writing and optimizing compute kernels using CUDA or similar languages
   <br/>
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Nice to Have:
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Current PhD in Computer Science and Engineering with a specialization in Computer Architecture, Parallel Computing, Compilers, or other systems
  </li>
  <li>
   Co-optimizing computing infrastructure and deep learning frameworks for optimal performance on specific workloads. Identify and resolve performance bottlenecks through profiling and system analysis
  </li>
  <li>
   Experience collaborating with data scientists and machine learning engineers to integrate distributed training capabilities into GenBio AI&rsquo;s model development and deployment frameworks
  </li>
  <li>
   Proficient in Python with experience in GPU-accelerated libraries (e.g., CUDA, cuDNN)
  </li>
  <li>
   Knowledge of performance profiling and optimization tools for HPC and deep learning
   <br/>
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 Join us as we embark on this journey to redefine the future of biology and medicine.
 <br/>
 <br/>
 We are an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. GenBio AI participates in the U.S. Department of Homeland Security&rsquo;s E-Verify program to confirm the employment eligibility of all newly hired employees. For more information on E-Verify, please visit www.e-verify.gov.
 <br/>
 <br/>
 We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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Research Engineer Intern

New York University Abu Dhabi

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 The laboratory of Dr. Djellel Difallah in the Division of Science, New York University Abu Dhabi, seeks a Post-Doctoral Associate or a Associate Research Scientist to join a lab focused on applied machine learning.
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 The successful applicant will participate in research involving human computation, knowledge discovery, machine learning, and data science. The position will provide the opportunity to develop applied research skills in machine learning, interact with an international network of collaborators, and gain post-doctoral research experience.
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  Skills
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 The ideal candidate is self-motivated and can work independently, has a passion for AI and its applications, and is willing to learn new technologies. The candidate should have a PhD in Computer Science or a closely related field. Relevant background and skills include:
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   Strong foundation in one of the following areas: Machine Learning / Information Retrieval / Knowledge Graph Representation / Recommender Systems
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   Graph Theory/Network Science
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   Python, and up-to-date machine learning libraries
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   Excellent written and verbal communication skills
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   Track record of publishing in top tier conferences
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 For consideration, applicants need to submit a cover letter, curriculum vitae, list of publications (if applicable), a 1-page statement of research interests, and three letters of reference. If you have any questions, please email: djellel@nyu.edu
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  About NYUAD
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 NYU Abu Dhabi is a degree-granting research university with a fully integrated liberal arts and science undergraduate program in the Arts, Sciences, Social Sciences, Humanities, and Engineering. NYU Abu Dhabi, NYU New York, and NYU Shanghai, form the backbone of NYU&rsquo;s global network university, an interconnected network of portal campuses and academic centers across six continents that enable seamless international mobility of students and faculty in their pursuit of academic and scholarly activity. This global university represents a transformative shift in higher education, one in which the intellectual and creative endeavors of academia are shaped and examined through an international and multicultural perspective. As a major intellectual hub at the crossroads of the Arab world, NYUAD serves as a center for scholarly thought, advanced research, knowledge creation, and sharing, through its academic, research, and creative activities.
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 EOE/AA/Minorities/Females/Vet/Disabled/Sexual Orientation/Gender Identity Employer
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 UAE Nationals are encouraged to apply.
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  Equal Employment Opportunity Statement
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  For people in the EU, click here for information on your privacy rights under GDPR: www.nyu.edu/it/gdpr
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 NYU is an Equal Opportunity Employer and is committed to a policy of equal treatment and opportunity in every aspect of its recruitment and hiring process without regard to age, alienage, caregiver status, childbirth, citizenship status, color, creed, disability, domestic violence victim status, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision making, sex, sexual orientation, unemployment status, veteran status, or any other legally protected basis. All interested persons are encouraged to apply for vacant positions at all levels.
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  Sustainability Statement
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 NYU aims to be among the greenest urban campuses in the country and carbon neutral by 2040. Learn more at nyu.edu/sustainability
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Associate Research Scientist/ Post-Doctoral Associate in the Division of Science (Computer Science) - Dr. Djellel Difallah

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