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Neural Architecture Search Jobs (NOW HIRING)

Track and evaluate emerging research in neural architecture search, machine learning systems and quantization methods, and determine what translates to measurable improvements in our systems Your ...

On-Device Research Engineer

San Jose, CA · On-site

$120K - $300K/yr

Familiarity with neural architecture search and hardware-aware NAS * Background shipping voice-first or far-field audio products * Contributions to open-source compression toolchains (TFLite, ONNX ...

On-Device Research Engineer

San Jose, CA · On-site

$120K - $300K/yr

Familiarity with neural architecture search and hardware-aware NAS * Background shipping voice-first or far-field audio products * Contributions to open-source compression toolchains (TFLite, ONNX ...

Track and evaluate emerging research in neural architecture search, machine learning systems and quantization methods, and determine what translates to measurable improvements in our systems Your ...

Track and evaluate emerging research in neural architecture search, machine learning systems and quantization methods, and determine what translates to measurable improvements in our systems Your ...

AI / Embedded ML Engineer

Saratoga, CA · On-site

$145K - $190K/yr

... neural architecture search or AutoML for edge targets • Familiarity with Rust for embedded or systems programming • Prior work on products in wearables, robotics, industrial sensing, or IoT ...

AI / Embedded ML Engineer

Saratoga, CA · On-site

$145K - $190K/yr

... neural architecture search or AutoML for edge targets • Familiarity with Rust for embedded or systems programming • Prior work on products in wearables, robotics, industrial sensing, or IoT ...

Matterport - Senior ML Ops Engineer

Sunnyvale, CA · On-site

$122K - $168K/yr

Implement and apply model optimization techniques such as quantization, pruning, distillation, and neural architecture search to improve inference speed and reduce resource consumption. Develop and ...

AI / Embedded ML Engineer

Saratoga, CA · On-site

$145K - $190K/yr

... neural architecture search or AutoML for edge targets • Familiarity with Rust for embedded or systems programming • Prior work on products in wearables, robotics, industrial sensing, or IoT ...

... neural network pruning/knowledge distillation/quantization/architecture search, sub-quadratic attention optimization, efficient architecture design and on-device ML • Experience with sparse mixture ...

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Neural Architecture Search information

What is neural architecture search?

Neural Architecture Search (NAS) is an automated process for designing artificial neural network architectures. Instead of manually creating network structures, NAS uses algorithms to search for the optimal architecture that performs best for a specific task, like image classification or natural language processing. This approach can help discover novel and efficient models that may outperform human-designed architectures. NAS often leverages machine learning techniques such as reinforcement learning or evolutionary algorithms to explore a wide range of possible designs.

What are the key skills and qualifications needed to thrive as a neural architecture search engineer?

To thrive as a Neural Architecture Search (NAS) Engineer, you need a solid background in machine learning, deep learning, and optimization algorithms, typically supported by an advanced degree in computer science or related fields. Familiarity with frameworks like TensorFlow, PyTorch, and specialized NAS libraries, as well as experience with cloud computing platforms, is crucial. Strong analytical thinking, creativity, and collaborative communication help in designing innovative models and working across research and engineering teams. These skills are essential to efficiently develop and deploy high-performing neural networks tailored for specific tasks and resource constraints.

What are some common challenges faced by professionals working in neural architecture search roles?

Professionals in Neural Architecture Search roles often face challenges related to the computational demands of searching vast model spaces, as NAS can require significant hardware resources and time. Balancing the trade-off between model performance and efficiency is also crucial, as the best-performing architectures may not always be the most practical for deployment. Additionally, staying updated with evolving NAS algorithms and effectively collaborating with machine learning engineers and data scientists to integrate findings into production models are key aspects of the role.

What is the difference between Neural Architecture Search vs Data Scientist?

AspectNeural Architecture SearchData Scientist
Required credentialsAdvanced knowledge in machine learning, deep learning, programming, and algorithmsDegree in data science, statistics, computer science, or related fields
Work environmentResearch labs, AI development teams, tech companies focusing on AI/MLBusiness environments, analytics teams, consulting firms, tech companies
Industry usageAI research, model optimization, automated machine learningData analysis, predictive modeling, business insights
Common search intentAutomating neural network design, optimizing architecturesAnalyzing data, building predictive models, deriving insights

Neural Architecture Search focuses on automating the design of neural network architectures using machine learning techniques, while Data Scientists analyze data and build models to extract insights. Both roles require strong technical skills but serve different purposes within AI development and data analysis.

Infographic showing various Neural Architecture Search job openings in the United States as of August 2026, with employment types broken down into 1% Locum Tenens, 76% Full Time, 18% Part Time, and 5% Contract. Highlights an 93% Physical, 2% Hybrid, and 5% Remote job distribution.

Senior AI Systems and Algorithms Engineer

Jobtailor

California, MO • On-site

$180 - $260/hr

Other

Posted 9 days ago


Job description

  • Advance the state of the art in foundation model development, training, and deployment
  • Design scalable systems for preparing high-quality multimodal datasets for frontier foundation model training
  • Develop algorithms and systems that improve the scalability, efficiency, and cost of large-scale pre-training and post-training
  • Advance techniques that improve inference performance, reduce deployment cost, and enable efficient serving across cloud and edge platforms
  • Develop reusable infrastructure and contribute new model support to NVIDIA's open-source GenAI training platform
  • Collaborate with research, product, and infrastructure teams to design new algorithms and optimize existing systems
  • Contribute to NVIDIA's open-source AI stack, including Megatron-LM, Megatron Bridge, and NeMo-RL
Requirements
  • MS or Ph.D in Computer Science, AI, Applied Mathematics, or a related field (or equivalent experience)
  • 5+ years of relevant industry experience
  • Strong foundation in machine learning, deep learning, and optimization
  • Excellent software engineering skills, including Python and PyTorch
  • Experience building high-performance software for large-scale AI systems
  • Strong analytical, debugging, and performance optimization skills
  • Experience with distributed training at scale, such as Megatron-LM, Megatron Bridge, FSDP, and TP/PP/CP/DP
  • Experience with optimizer research and efficient sparse or long-context attention
  • Experience with supervised fine-tuning, reinforcement learning for LLMs, PPO, GRPO, asynchronous RL, or NeMo-RL
  • Experience with model compression, quantization, pruning, knowledge distillation, neural architecture search, or diffusion/non-autoregressive language models
  • Experience contributing to open-source AI frameworks such as Megatron-LM, Megatron Bridge, NeMo-RL, or Hugging Face Transformers
  • Experience with GPU performance optimization, distributed systems, latency/throughput analysis, and profiling of large-scale AI workloads
Core Competencies

Demonstrates expertise in foundation model development, including machine learning and deep learning, with a strong focus on optimizing large-scale AI systems. Proficient in Python and PyTorch, with experience in distributed training and contributing to open-source AI frameworks.

Highest-signal resume keywords
  • Foundation Model Development
  • Machine Learning
  • Deep Learning
  • Python Programming
  • PyTorch Framework
ATS Optimization KeywordsHard Skills
  • Machine Learning
  • Deep Learning
  • Optimization
  • Distributed Training
  • Model Compression
  • Quantization
  • Pruning
  • Neural Architecture Search
  • Reinforcement Learning
  • Performance Optimization
Soft Skills
  • Analytical Skills
  • Debugging Skills
Certifications & Qualifications
  • MS or Ph.D in Computer Science
  • AI
  • Applied Mathematics
Industry Keywords
  • AI Systems
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