AI Research Scientist, SysML

Meta

$130K — $180K *
Consumer Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree in Computer Science, Computer Engineering, or related field.
  • PhD in Electrical Engineering, Computer Science, or equivalent.
  • Experience in developing AI systems, algorithms, or hardware acceleration using C/C++ or Python.
  • Knowledge of training/inference processes for large scale AI models, including CV and LLMs.
  • Proven contributions to open source projects or coding competitions.

Responsibilities

  • Identify and address multi-discipline ML acceleration challenges.
  • Collaborate on co-design issues between hardware and software with research teams.
  • Innovate and integrate novel ML accelerator solutions and system architectures.
  • Develop model compression and scalability methods such as pruning and distillation.
  • Optimize AI models for performance under latency and power limits.
  • Communicate data-driven recommendations and influence partner adoption of solutions.
  • Stay engaged with the ML research community through conferences and publications.

Benefits

  • Join a cutting-edge team working on AR and VR technologies.
  • Opportunity to influence advancements in AI acceleration.
  • Collaborative environment across various disciplines.
  • Access to the latest tools and resources for research.
  • Potential for impact through patents and peer-reviewed publications.
Full Job Description
Meta is seeking a Research Scientist to join our Research & Development teams. This role involves working on AI models, hardware acceleration and software systems related topics. The position will involve taking these skills and applying them to solve for some of the most crucial & exciting problems that exist in Reality Labs. The primary objective will be to develop novel solutions that enable compute and power efficient training and on-device inference of vision and language models for use cases in AR, VR and edge devices. We are hiring in multiple locations.

Responsibilities

Identify and solve multi-discipline ML acceleration problems involving algorithms, network design, hardware architecture and AR/VR use cases. These may involve novel approaches not yet established in the industry
• Work across hardware and software, to solve co-design problems with other Research scientists working in this area
• Codesign and invent novel ML accelerator and system architecture solutions, and facilitate the integration of algorithms and software to utilize these enhancements
• Develop state-of-the-art model compression and scalability techniques using Numerics, pruning, distillation etc
• Optimize models on hardware accelerators to achieve the best performance given various real time latency and power constraints
• Influence partners to adopt recommended solutions through data-driven analysis and clear communication of trade-offs
• Define use cases, and develop methodology & benchmarks to evaluate different approaches
• Apply in-depth knowledge of how the ML acceleration interacts with the other systems around it
• Attend conferences, interpret papers, and stay updated with latest research advancements in the field of ML acceleration; contribute to patents and/or publications in peer-reviewed conferences and journals

Minimum Qualifications
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• PhD in Electrical Engineering, Computer Science, or equivalent experience
• Experience developing AI-System infrastructure, AI algorithms or AI hardware acceleration in C/C++ or Python

Preferred Qualifications
• Experience or knowledge of training/inference of Large scale AI models - CV and/or LLMs
• Experience with PyTorch, TensorFlow or similar machine learning toolsets
• Demonstrated research and engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)
• Experience working and communicating cross-functionally in a team environment
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading workshops, journals or conferences such as ICLR, NeurIPS, CVPR, ACL, ICML, MLSys, ISCA, MICRO, DAC, ASPLOS etc
• Experience or knowledge of on-device algorithm development including hardware-aware ML models and/or optimizing ML compilers for efficient deployment on AI accelerators
• Experience or knowledge of architecting ML hardware accelerators and systems
• Experience evaluating alternative system or algorithm designs by analyzing trade-offs in performance, power, and latency to recommend a solution

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