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Deepspeed Jobs (NOW HIRING)

Quevera LLC

Software Engineer II

Herndon, VA · On-site

$100K - $137K/yr

Experience with distributed training frameworks such as DeepSpeed, FSDP, or Megatron. * Three (3) or more years of experience with computer vision or multimodal AI models. * Understanding of Vision ...

Unconventional AI

AI Systems, Training

Palo Alto, CA · On-site

$123K - $168K/yr

Proven track record of implementing, debugging, and maintaining production-grade training frameworks--such as Megatron-LM, DeepSpeed, Ray, PyTorch Lightning--turning raw compute into a reliable model ...

Unconventional AI

AI Systems, Training

... LM, DeepSpeed, Ray, PyTorch Lightning--turning raw compute into a reliable model-building factory. Preferred : • A forward-looking perspective on co-designing algorithms for unconventional ...

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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.
Software Engineer II

Software Engineer II

Quevera LLC

Herndon, VA • On-site

$100K - $137K/yr

Other

Medical, Dental, Vision, Life, Retirement

Re-posted 11 days ago

Job description


Quevera is seeking a highly skilled Software Engineer II with an active TS/SCI clearance with Polygraph to support mission-critical programs. In this role, you will develop and optimize advanced machine learning solutions supporting multimodal artificial intelligence and computer vision applications for national security missions.
Working alongside applied scientists and engineering teams, you will design scalable machine learning pipelines, fine-tune Vision-Language Models (VLMs), build AWS-based training infrastructure, and develop data processing and evaluation frameworks for large-scale geospatial imagery datasets. You'll leverage modern AI technologies to deliver high-performing, secure, and production-ready machine learning solutions.
As a Software Engineer II, you'll have the opportunity to work with cutting-edge AI technologies, collaborate with industry experts, and contribute to innovative solutions supporting critical national security missions.
Work Schedule:
Work Location: Must be willing to work onsite in a SCIF daily, or as required.
Job Responsibilities:
  • Design and execute fine-tuning pipelines for Vision-Language Models (VLMs) using domain-specific imagery datasets.
  • Develop data preprocessing, training orchestration, and hyperparameter optimization workflows.
  • Build and implement evaluation frameworks for multimodal model performance, including image understanding, visual question answering, and spatial reasoning.
  • Develop scalable distributed training infrastructure using AWS services, including SageMaker and EC2 GPU instances.
  • Engineer data pipelines for curating, annotating, and transforming geospatial imagery into model-ready datasets.
  • Collaborate with applied scientists and solutions architects to optimize model architectures and parameter-efficient fine-tuning strategies, including LoRA and QLoRA.
  • Optimize model inference performance and deployment workflows.
  • Develop secure, scalable machine learning solutions that support mission requirements.

Minimum Requirements:
  • Active TS/SCI clearance with Polygraph required.
  • Current NGA eligibility with active SBU, SECNet, and COE accounts.
  • Five (5) or more years of professional machine learning engineering experience with a focus on deep learning.
  • One (1) or more years of experience fine-tuning large language models (LLMs) or Vision-Language Models (VLMs).
  • Experience with parameter-efficient fine-tuning techniques, including LoRA, QLoRA, and adapters.
  • Familiarity with supervised fine-tuning, instruction tuning, and RLHF/DPO alignment techniques.
  • Four (4) or more years of advanced Python development for machine learning workloads.
  • Strong proficiency with PyTorch and the Hugging Face ecosystem, including Transformers, PEFT, Datasets, and Accelerate.
  • Experience with distributed training frameworks such as DeepSpeed, FSDP, or Megatron.
  • Three (3) or more years of experience with computer vision or multimodal AI models.
  • Understanding of Vision Transformer architectures, including ViT, CLIP, LLaVA, or similar models.
  • Experience processing and augmenting image datasets at scale.
  • Three (3) or more years of experience with AWS machine learning infrastructure, including SageMaker, EC2 GPU instances, and Amazon S3.
  • Experience building machine learning evaluation pipelines, including automated benchmarking, metric computation, and result analysis.
  • Strong software engineering fundamentals, including version control, testing, and CI/CD practices for machine learning workflows.

Desired Skills:
  • Active TS/SCI clearance with Polygraph required.
  • Current NGA eligibility with active SBU, SECNet, and COE accounts.
  • Five (5) or more years of professional machine learning engineering experience with a focus on deep learning.
  • One (1) or more years of experience fine-tuning large language models (LLMs) or Vision-Language Models (VLMs).
  • Experience with parameter-efficient fine-tuning techniques, including LoRA, QLoRA, and adapters.
  • Familiarity with supervised fine-tuning, instruction tuning, and RLHF/DPO alignment techniques.
  • Four (4) or more years of advanced Python development for machine learning workloads.
  • Strong proficiency with PyTorch and the Hugging Face ecosystem, including Transformers, PEFT, Datasets, and Accelerate.
  • Experience with distributed training frameworks such as DeepSpeed, FSDP, or Megatron.
  • Three (3) or more years of experience with computer vision or multimodal AI models.
  • Understanding of Vision Transformer architectures, including ViT, CLIP, LLaVA, or similar models.
  • Experience processing and augmenting image datasets at scale.
  • Three (3) or more years of experience with AWS machine learning infrastructure, including SageMaker, EC2 GPU instances, and Amazon S3.
  • Experience building machine learning evaluation pipelines, including automated benchmarking, metric computation, and result analysis.
  • Strong software engineering fundamentals, including version control, testing, and CI/CD practices for machine learning workflows.

Why Join Quevera?
Award-Winning Culture
Quevera was recognized as a Top Workplace in the Washington, DC/Baltimore region for 2025, marking our fifth consecutive year receiving this distinction based on employee feedback.
Outstanding Benefits
  • We invest in our employees and their families through a highly competitive benefits package, including:
  • 100% employer-paid medical coverage (optional plan)
  • Competitive options for Medical, Dental and Vision insurance
  • Employer-paid short-term and long-term disability coverage
  • Employer-paid life insurance
  • $5,000 annually for education, training, certifications, and professional development
  • Career advancement through our structured IQWay Program
  • Up to 6% 401(k) match
  • Additional 4% profit-sharing contribution

At Quevera, we believe exceptional people deserve exceptional opportunities. We're more than just a workplace-we're a team of innovators, problem-solvers, and industry experts committed to delivering mission-critical solutions while fostering professional growth, collaboration, and technical excellence.
Quevera is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected veteran status, age or any other characteristic protected by law. #LI-AA1

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