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Deepspeed Jobs (NOW HIRING)

EnCharge AI

LLM Inference Deployment Engineer

$180K - $240K/yr

Strong expertise in LLM inference frameworks (PyTorch, ONNX Runtime, vLLM, TensorRT-LLM, DeepSpeed). * In-depth knowledge of the Python programming language for model integration and performance ...

NVIDIA

Agent RL Infra Engineer

Santa Clara, CA · On-site

... DeepSpeed, FSDP, HF Accelerate) and ML ops skills covering pipeline automation, job orchestration, and GPU cluster management are important here • Proficiency in Python, Go, Rust, or similar • ...

Relace

Machine Learning Engineer

San Francisco, CA · On-site

Familiarity with distributed training frameworks (e.g., PyTorch, JAX, DeepSpeed, or similar). * Experience working with large-scale training or inference infrastructure. * Understanding of memory ...

TSMC

Senior AI Model Fine-Tuning Engineer

Austin, TX · On-site

$103K - $142K/yr

... DeepSpeed). • Experience in evaluating model performance, including using metrics like BLEU, ROUGE, perplexity, and custom evaluation frameworks. • Candidates must be willing and able to work on ...

GE HealthCare

AI Scientist

Bellevue, WA · On-site

Preferred : • Experience and demonstrated capability to handle challenges with vague or abstract problem definition. • Experience with frameworks and tools such as DeepSpeed, HuggingFace ...

Apple

Senior Machine Learning Engineer, AI, SIML

Cupertino, CA · On-site

$154K - $213K/yr

Experience with parallel training libraries such as PyTorch Distributed (torch.distributed), DeepSpeed, or FairScale. Experience building ML models for on-device inference. Publication record at ML ...

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Deepspeed information

What are some common challenges faced by engineers working with DeepSpeed and how can they be addressed?

Engineers working with DeepSpeed often encounter challenges related to optimizing large-scale model training, such as managing memory efficiency and tuning distributed training parameters. Troubleshooting issues like gradient accumulation, parallelism strategies, and ensuring compatibility with different hardware setups can be complex. Collaborating closely with data scientists, DevOps, and research teams is essential for addressing these challenges, as is staying updated with the latest DeepSpeed releases and documentation. Regular participation in code reviews and knowledge-sharing sessions can also help engineers overcome technical hurdles and continuously improve model performance.

What is Deepspeed?

Deepspeed is an open-source deep learning optimization library developed by Microsoft, designed to enable distributed training of large-scale models efficiently. It helps researchers and engineers train models that are too large to fit in the memory of a single GPU by offering features like ZeRO optimization, mixed-precision training, and advanced parallelism techniques. Deepspeed is widely used in the machine learning community for its scalability and performance improvements, making it easier to train state-of-the-art models on vast datasets. The library integrates seamlessly with PyTorch and supports training on multiple GPUs and even across multiple machines.

What is the difference between Deepspeed vs Data Scientist?

AspectDeepspeedData Scientist
Required credentialsKnowledge of machine learning frameworks, programming skills in Python, experience with AI model trainingDegree in Data Science, Statistics, Computer Science, or related fields; strong analytical skills
Work environmentAI research labs, tech companies, cloud computing environmentsBusiness, tech companies, research institutions
Industry usageAI model training, deep learning optimizationData analysis, predictive modeling, business insights

Deepspeed focuses on optimizing large-scale AI model training and deep learning performance, while Data Scientists analyze data to generate insights and build predictive models. Both roles require technical skills but serve different purposes within the AI and data ecosystem.

What are the key skills and qualifications needed to thrive as a DeepSpeed Engineer, and why are they important?

To thrive as a DeepSpeed Engineer, you need a solid background in machine learning, deep learning frameworks (such as PyTorch), and distributed systems, often supported by a degree in computer science or a related field. Proficiency with DeepSpeed, parallel computing libraries, and cloud platforms, along with familiarity with tools like CUDA and NCCL, is typically expected. Strong problem-solving abilities, collaboration, and adaptability are crucial soft skills for optimizing large-scale AI models and working with cross-functional teams. Mastering these skills ensures efficient development and deployment of high-performance, scalable AI solutions in demanding environments.
More about Deepspeed jobs
What cities are hiring for Deepspeed jobs? Cities with the most Deepspeed job openings:
What states have the most Deepspeed jobs? States with the most job openings for Deepspeed jobs include:
What job categories do people searching Deepspeed jobs look for? The top searched job categories for Deepspeed jobs are:
Deepspeed jobs near you
Infographic showing various Deepspeed job openings in the United States as of July 2026, with employment types broken down into 2% Internship, 97% Full Time, and 1% Part Time. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution.
LLM Inference Deployment Engineer

LLM Inference Deployment Engineer

EnCharge AI

Remote

$180K - $240K/yr

Full-time

Re-posted 19 days ago

Job description

EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge's robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today's best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems.
About the Role
EnCharge AI is seeking an LLM Inference Deployment Engineer to optimize, deploy, and scale large language models (LLMs) for high-performance inference on its energy efficient AI accelerators. You will work at the intersection of AI frameworks, model optimization, and runtime execution to ensure efficient model execution and low-latency AI inference.
Responsibilities
  • Deploy and optimize LLMs (GPT, LLaMA, Mistral, Falcon, etc.) post-training from libraries like HuggingFace
  • Utilize inference runtimes such as ONNX Runtime, vLLM for efficient execution.
  • Optimize batching, caching, and tensor parallelism to improve LLM scalability in real-time applications.
  • Develop and maintain high-performance inference pipelines using Docker, Kubernetes, and other inference servers.

Qualifications
  • Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field.
  • Experience in LLM inference deployment, model optimization, and runtime engineering.
  • Strong expertise in LLM inference frameworks (PyTorch, ONNX Runtime, vLLM, TensorRT-LLM, DeepSpeed).
  • In-depth knowledge of the Python programming language for model integration and performance tuning.
  • Strong understanding of high-level model representations and experience implementing framework-level optimizations for Generative AI use cases
  • Experience with containerized AI deployments (Docker, Kubernetes, Triton Inference Server, TensorFlow Serving, TorchServe).
  • Strong knowledge of LLM memory optimization strategies for long-context applications.
  • Experience with real-time LLM applications (chatbots, code generation, retrieval-augmented generation).

EnchargeAI is an equal employment opportunity employer in the United States.
The salary range for this position is $180,000 to $240,000 USD ($175,000 to $245,000 CAD) per year. Actual compensation offered will be determined based on job-related knowledge, skills, and experience.

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