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Deepseek Jobs (NOW HIRING)

AMD

CVP of Applied AI FDE

San Jose, CA · On-site

Enable customer success by deeply optimizing open-sourced models (Llama 3, DeepSeek, Mixtral) and proprietary models for our specific hardware topology, utilizing tools like vLLM and TensorRT-LLM ...

SambaNova

Senior AI Systems Performance Engineer

San Jose, CA · On-site

$122K - $167K/yr

You'll work hands-on with some of the most advanced models in the world - such as DeepSeek R1, GPT OSS, and other frontier architectures - to push the limits of throughput, latency, and efficiency.

AMD

Principal AI Performance Modeling Architect

... 4, Reasoning models (Deepseek-R1), and multi-modal architectures • Collaborate with internal and external stakeholders/ML researchers to disseminate results and iterate at rapid pace.

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Deepseek information

What is a deepseek engineer?

Deepseek engineers are professionals who specialize in developing and implementing advanced artificial intelligence (AI) and machine learning solutions, often focusing on natural language processing and search technologies. They typically work for Deepseek, a company known for its work in large language models and AI-driven search capabilities. Their responsibilities include designing, training, and optimizing AI models, as well as integrating these solutions into products and services. Deepseek engineers often collaborate with data scientists and product teams to ensure the technology meets user needs and industry standards.

What are the key skills and qualifications needed to thrive as a deepseek engineer?

To thrive as a Deep Learning Engineer, you need a strong background in computer science, mathematics, and machine learning, often supported by a relevant degree and experience with neural networks. Proficiency in Python, TensorFlow, PyTorch, and familiarity with cloud computing platforms and GPU acceleration is typically required. Strong problem-solving skills, creativity, and effective communication help you design novel models and collaborate with multidisciplinary teams. These skills enable the development and deployment of advanced AI solutions that drive innovation and real-world impact.

What are the typical collaboration patterns for deepseek engineers, and how do they work with cross-functional teams?

Deep Learning Research Engineers at Deepseek frequently collaborate with data scientists, software engineers, and product managers to develop and optimize machine learning models. They participate in regular team meetings to align on project goals, share research findings, and integrate new algorithms into production systems. Effective communication is crucial, as these engineers often translate complex research concepts into actionable tasks for engineering teams. This collaborative environment fosters innovation and ensures that research outcomes can be successfully deployed within real-world applications.

What is the difference between Deepseek vs Data Analyst?

AspectDeepseekData Analyst
Required CredentialsTypically requires a background in computer science, data science, or related fields; certifications in data analysis or machine learning are commonUsually requires a degree in statistics, mathematics, or related fields; certifications like Microsoft Excel, Tableau, or SQL are beneficial
Work EnvironmentPrimarily technical, involving data processing, algorithm development, and machine learning model trainingPrimarily analytical, involving data interpretation, reporting, and visualization
Employer & Industry UsageUsed in tech companies, AI firms, and research institutions focusing on machine learning and AI solutionsUsed across various industries including finance, marketing, healthcare, and consulting for data-driven decision making

Deepseek focuses on developing AI and machine learning models, requiring technical expertise in algorithms and programming. Data Analysts interpret and visualize data to support business decisions. While both roles work with data, Deepseek is more technical and research-oriented, whereas Data Analysts focus on insights and reporting.

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Deepseek jobs near you
Infographic showing various Deepseek job openings in the United States as of August 2026, with employment types broken down into 25% Internship, 25% Full Time, 25% Part Time, and 25% Temporary. Highlights an 75% In-person, and 25% Remote job distribution.

Senior AI Systems Performance Engineer

SambaNova

San Jose, CA

Full-time

Re-posted 27 days ago

Job description

About the role

We are seeking a talented and driven ML performance engineer to optimize and scale state-of-the-art foundation models on SambaNova's reconfigurable dataflow platform. You'll work hands-on with some of the most advanced models in the world - such as DeepSeek R1, GPT OSS, and other frontier architectures - to push the limits of throughput, latency, and efficiency. In this role, you'll bridge the gap between deep learning and systems performance, collaborating across compiler, runtime, and hardware layers to deliver world-record performance for large-scale AI inference.

Responsibilities
  • Bring up and optimize cutting-edge foundation models (e.g., DeepSeek, Llama, Qwen, and others) on the SambaNova platform through the SambaNova software stack.
  • Profile and enhance model performance across compiler, runtime, and hardware layers to achieve SOTA throughput and latency.
  • Collaborate with machine learning, compiler, runtime, and hardware teams to deliver co-designed, high-performance AI applications.
  • Integrate the latest advances in model architecture, quantization, scheduling, and memory optimization from both academia and industry.
  • Develop robust, scalable, and efficient end-to-end inference solutions aligned with customer needs.
  • Identify performance bottlenecks and propose dataflow or scheduling optimizations for both single-node and distributed systems.
Basic Qualifications
  • Bachelor's or higher degree in computer science, electrical engineering, or a related field (e.g., applied mathematics, physics, or statistics).
  • 3+ years of experience in one or more of the following areas:
  • Deep learning model development and performance optimization
  • Compiler, runtime, or kernel-level optimization
  • Software-hardware co-design or systems performance tuning
  • Proficiency in Python or C++, with strong foundations in algorithms, data structures, and numerical computing.
  • Experience with at least one major ML framework - PyTorch, TensorFlow, or JAX.
  • Demonstrated ability to analyze and optimize performance in real-world ML pipelines.
Preferred Qualifications
  • Hands-on experience with LLM or multimodal model training and inference.
  • Background in large-scale distributed training, continuous batching, and high-throughput inference systems.
  • Familiarity with quantization, graph optimization, kernel fusion, and model partitioning.
  • Experience with frameworks such as DeepSpeed, Megatron, vLLM, or TensorRT.
  • Strong GPU programming skills (CUDA, Triton, or OpenCL); experience with cuDNN, cuBLAS, or similar libraries is a plus.
  • Knowledge of memory hierarchy optimization, caching, and scheduling for large-scale model execution.
  • Publication record or open-source contributions in ML systems or performance optimization is a plus.

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