ZipRecruiter

Log In

1

Pytorch Huggingface Jobs (NOW HIRING)

Roku

Senior Machine Learning Engineer

San Jose, CA · On-site

$229K - $360K/yr

Ray, PyTorch, HuggingFace, AWS Sagemaker * AI literacy and curiosity. You have either tried Gen AI in your previous work or outside of work, or are curious about Gen AI and have explored it. * MS in ...

Diverse Lynx

AI/ML Technical Architect

Manhattan, NY

$180K - $210K/yr

Strong hands-on understanding of ML development frameworks and ecosystems (e.g., Python + standard ML/DL libraries like scikit-learn, TensorFlow, PyTorch, HuggingFace) * Proven architecture ...

Yum! Brands

Associate Manager Machine Learning

Irvine, CA · Hybrid

$134K - $158K/yr

Strong programming skills in Python and ML tooling (e.g., PyTorch, HuggingFace, ONNX, MLflow). * Experience optimizing model latency and integrating ML with backend infrastructure. Preferred ...

AMSYS Talent

LLM Infrastructure Engineer

Houston, TX · On-site

$97K - $127K/yr

Build and deploy LLM inference services using HuggingFace Transformers and PyTorch * Optimize GPU workloads and CUDA memory usage * Implement streaming inference APIs for real-time model responses

Virtue AI

AI Product Testing Engineer

San Francisco, CA · On-site

$120K - $200K/yr

Familiarity with LLM libraries like PyTorch, HuggingFace, or agent development kits. * Enthusiasm for thriving in a fast-paced startup environment . Why Join Virtue AI * Competitive base salary ...

next page

Showing results 1-20

All JobsPytorch Huggingface Jobs

Pytorch Huggingface information

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

To thrive as a PyTorch Hugging Face Engineer, you need a strong background in deep learning, Python programming, and experience with machine learning frameworks, supported by a relevant degree such as computer science or engineering. Familiarity with PyTorch, Hugging Face Transformers library, version control systems like Git, and often cloud platforms (e.g., AWS, GCP) is essential, with certifications in machine learning or cloud technologies being advantageous. Strong problem-solving skills, collaboration, and clear communication help you effectively design, implement, and optimize NLP models in cross-functional teams. These skills ensure you can build state-of-the-art AI solutions efficiently, troubleshoot complex challenges, and deliver impactful results in the fast-evolving field of natural language processing.

What is the difference between Pytorch Huggingface vs Machine Learning Engineer?

AspectPytorch HuggingfaceMachine Learning Engineer
CredentialsProficiency in Python, deep learning frameworks, familiarity with NLP librariesDegree in CS, data science, or related field; experience with ML models
Work EnvironmentResearch labs, AI startups, tech companies focusing on NLP and deep learningTech companies, consulting firms, R&D departments across industries
UsageDeveloping NLP models, fine-tuning transformers, deploying AI solutionsDesigning, building, and deploying ML models across various domains

While Pytorch Huggingface specializes in NLP model development using transformer architectures, Machine Learning Engineers work across diverse ML applications. Pytorch Huggingface skills are often part of a Machine Learning Engineer's toolkit, but the roles differ in scope and focus.

What are Pytorch Huggingface developers?

PyTorch Hugging Face developers are professionals who specialize in building and deploying machine learning and natural language processing (NLP) models using PyTorch, an open-source deep learning framework, and the Hugging Face library, which provides a wide range of pre-trained models and tools for NLP tasks. These developers create, fine-tune, and implement models for tasks like text classification, question answering, and language generation. Their expertise includes working with model architectures such as BERT, GPT, and others, as well as integrating models into applications or research projects.

How do PyTorch Huggingface engineers typically collaborate with data scientists and researchers in a project setting?

PyTorch Huggingface engineers often work closely with data scientists and researchers to implement, fine-tune, and deploy state-of-the-art machine learning models. Collaboration involves regular discussions to understand project objectives, translating research ideas into efficient code, and iterating on model performance. Engineers are responsible for optimizing model pipelines, integrating new features, and ensuring compatibility with the Huggingface ecosystem. Effective communication and teamwork are essential, as projects usually require frequent feedback loops and joint problem-solving sessions.
More about Pytorch Huggingface jobs
What cities are hiring for Pytorch Huggingface jobs? Cities with the most Pytorch Huggingface job openings:
What states have the most Pytorch Huggingface jobs? States with the most job openings for Pytorch Huggingface jobs include:
What job categories do people searching Pytorch Huggingface jobs look for? The top searched job categories for Pytorch Huggingface jobs are:
Pytorch Huggingface jobs near you
Machine Learning Engineer - Geospatial (TS/SCI)

Machine Learning Engineer - Geospatial (TS/SCI)

LaunchCode

Springfield, VA • On-site

$175K - $250K/yr

Full-time

Posted 16 days ago

Job description

Description
Title: AI/Machine Learning Engineer - Vision Language Models / Multimodal AI (NGA)
Location: Springfield or Herndon, VA (onsite)
Clearance: TS/SCI (CI Poly preferred)
Position Type: Full-Time, Direct Hire
Pay: $175,000 to $250,000 for an SME
Company: The name of our partner organization will be disclosed during the interview process. This is not a direct role with LaunchCode; it is a position through LaunchCode, working with one of our partner companies.
Disclaimer: We are unable to provide work sponsorship for this role
Overview:
We're hiring a AI/Machine Learning Engineer with strong experience in multimodal AI and large-scale model training to support advanced vision-language initiatives in a secure government environment. This role will focus on fine-tuning Vision Language Models (VLMs) on domain-specific geospatial imagery, building scalable AWS training infrastructure, and developing evaluation frameworks for image understanding and spatial reasoning. Ideal candidates will have deep experience with PyTorch, HuggingFace, distributed training, and computer vision, along with the ability to optimize and deploy multimodal models in mission-critical environments.
Huge plus for candidates who have hands-on experience taking multimodal models such as CLIP, LLaVA, Qwen-VL, or similar Vision Language Models and fine-tuning them on classified or mission-specific imagery datasets. The ideal candidate can build the AWS infrastructure needed to train and scale these models, evaluate performance improvements across real-world use cases, and deploy solutions into secure government or air-gapped environments.
Key Responsibilities:
  • Design and execute fine-tuning pipelines for Vision Language Models (VLMs) using domain-specific imagery datasets
  • Handle data preprocessing, training orchestration, and hyperparameter optimization for multimodal models
  • Build evaluation frameworks for image understanding, visual question answering, and spatial reasoning tasks
  • Develop scalable AWS-based ML infrastructure using SageMaker and GPU-enabled EC2 for distributed training
  • Create data pipelines for curating, annotating, and transforming geospatial imagery into model-ready datasets
  • Partner with applied scientists and architects on model architecture improvements, LoRA/QLoRA strategies, and inference optimization

Required Qualifications:
  • Active TS/SCI with CI Poly
  • 5+ years of machine learning engineering experience focused on deep learning
  • 1+ year of hands-on experience fine-tuning foundation models (LLMs or VLMs)
  • Experience with LoRA, QLoRA, adapters, supervised fine-tuning, instruction tuning, and RLHF/DPO
  • 4+ years of advanced Python development for ML workloads
  • Strong PyTorch and HuggingFace experience (Transformers, PEFT, Datasets, Accelerate)
  • Experience with distributed training frameworks such as DeepSpeed, FSDP, or Megatron
  • 3+ years working with computer vision or multimodal models
  • Familiarity with vision transformer architectures (ViT, CLIP, LLaVA, etc.)
  • Experience processing and augmenting image datasets at scale
  • 3+ years with AWS ML infrastructure including SageMaker, EC2 GPU environments, and S3
  • Experience with ML evaluation pipelines, benchmarking, metrics, and result analysis
  • Strong software engineering fundamentals including version control, testing, and CI/CD

Preferred Qualifications:
  • 2+ years working with geospatial or remote sensing imagery
  • Experience with EO or SAR satellite imagery
  • Understanding of geospatial metadata, coordinate systems, and imagery preprocessing
  • Experience with model quantization / inference optimization (vLLM, TensorRT, ONNX)
  • MLOps tooling experience (MLflow, Weights & Biases, SageMaker Experiments)
  • Familiarity with annotation tools and active learning workflows
  • Containerized ML experience with Docker / ECR / ECS / EKS
  • Experience supporting ATO processes and NIST 800-53 compliance
  • Experience deploying in air-gapped/disconnected environments
  • Familiarity with multimodal evaluation benchmarks (MMMU, MMBench, GQA)
  • Publications or contributions in computer vision, multimodal AI, or VLMs
  • Synthetic data generation experience for training augmentation

Apply