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Pytorch Huggingface Jobs in Texas (NOW HIRING)

Select Minds LLC

Sr Data Scientist GenAI

Dallas, TX · On-site +1

$150K - $210K/yr

... PyTorch, TensorFlow, HuggingFace etc. - Proven track record building transformer/NLP / LLM models; experience with fine-tuning, prompt engineering. - Solid experience with information retrieval ...

PNC Bank

Software Engineer Senior- AI engineer

Dallas, TX · On-site

$86K - $172K/yr

Strong programming in Python, with frameworks like PyTorch/TensorFlow, and libraries such as HuggingFace Transformers. Experience with vector databases, embeddings, and RAG architectures Familiarity ...

PNC Bank

Software Engineer Senior-Ai Engineer

Dallas, TX · On-site

$121K - $159K/yr

Strong programming in Python, with frameworks like PyTorch/TensorFlow, and libraries such as HuggingFace Transformers. Experience with vector databases, embeddings, and RAG architectures Familiarity ...

Toyota

Sr.Security ML / AI Engineer

Plano, TX · On-site

$109K - $150K/yr

PyTorch proficiency for model training and custom architectures * Experience building evaluation ... HuggingFace ecosystem experience - model hub, tokenizers, datasets library, PEFT * Experience with ...

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Pytorch Huggingface information

What is a PyTorch Huggingface engineer?

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.

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

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.

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.

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 popular job titles related to Pytorch Huggingface jobs in Texas?

For Pytorch Huggingface jobs in Texas, the most frequently searched job titles are:

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The top searched job categories for Pytorch Huggingface jobs in Texas are:

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Cities in Texas with the most Pytorch Huggingface job openings:

Pytorch Huggingface jobs near you

Senior Field Application Engineer - AI

AMD (Advanced Micro Devices)

Austin, TX • On-site

Other

Posted 4 days ago

Advanced Micro Devices rating

8.6

Company rating: 8.6 out of 10

Based on 13 frontline employees who took The Breakroom Quiz

27th of 159 rated electronics manufacturers

Job description

WHAT YOU DO AT AMD CHANGES EVERYTHING
At AMD, our mission is to build great products that accelerate next-generation computing experiences-from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you'll discover the real differentiator is our culture. We push the limits of innovation to solve the world's most important challenges-striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career.
THE ROLE:
We are seeking a Field Application Engineer to join the AI and HPC Center of Excellence. The role broadly involves:
  • working with customers and partners in support of RFP-driven requests. Providing hands-on support to customers and partners to enable their AI workloads to run on AMD 'Instinct' datacenter GPUs
  • broader engineering investigations to understand both performance and characteristic performance across popular and customer-specific training and inference workloads. Understand competitive positioning
  • creating a body of technical documentation of AI performance on AMD hardware to support the Field Application team, partners, and customers

This is a 'hands-on' technical role, and we are looking for an individual with an established background in AI who is already familiar in executing and tuning training and inference workloads. In addition to the technical aspect you will also need to be able to create and deliver presentations and training both remotely and in person to our customers and partners.
AMD continues to ramp its in-house AI expertise, and as such this role provides an opportunity for the candidate to grow within the role while creating a significant impact on the wider business, with significant visibility to executive level within AMD.
The role is based within the United States and will require some travel to customers and conferences both nationally and internationally.
THE PERSON:
Does this sound familiar? We'd love to talk!
  • Track record working within AI. You are currently in a role undertaking AI inference or training as a key part of your function.
  • Demonstrable hands-on expertise working with popular AI frameworks.
  • Strong positive can-do attitude willing to do what is necessary and lead others in the wider FAE team by example. Available to help colleagues.
  • Skilled in independently prioritizing opportunities to deliver results on time.
  • Excellent written and oral communication skills in English, with the ability to collaborate effectively with both management and engineering teams.
  • Open to travel both domestic and international, approximately 10-20% over a year.

KEY RESPONSIBILITIES:
  • Support winning new AI business. Enabling customers to execute their AI workloads on AMD Instinct GPUs, AMD Pensando Pollara AI NICs, and EPYC CPUs. Supporting partners in RFP responses by testing requested workloads.
  • Build and nurture deep technical relationships with engineers, architects, and leaders at key customer accounts, and serve as a trusted advisor through application- and system/MLops-focused POCs, presentations, and training.
  • Engineering: execute popular and customer-driven AI inference and training workloads, generate results and create a characteristic understanding of AI performance on AMD hardware. Understand how system and software choices affect performance. Compare performance to our competition.
  • Run training and inference performance investigations using common frameworks (Pytorch, Tensorflow, JAX) and using MLperf, Hugging Face etc.
  • Build a body of documentation for internal and external dissemination: AMD-internal guides, whitepapers, tuning guides, training collateral.
  • Proactive engagement across AMD teams: GPU Business Unit, Engineering, Architecture, Platform, Software, and Product Development teams providing feedback and leadership from the field on requirements. Gathering missing functionality and working with Engineering to resolve and test.
  • Assist in creating Total Cost of Ownership models to aid pricing with bid desk.
  • Technically owning and resolving customer and partner issues. Submitting JIRA tickets and driving resolution.

PREFERRED EXPERIENCE:
  • Demonstrated experience with training and inference workloads on GPUs.
  • Executing applications in common frameworks (Pytorch, Tensorflow, Jax).
  • Any experience with popular AI repositories (e.g. HuggingFace, MLperf) and understanding derived performance/functionality differences between them.
  • MLops: familiarity with KVM, Kubernetes, Openstack, Openshift.
  • Understanding system level hardware design and its impact on performance.
  • Understand how the software stack affects performance: frameworks, precision, compilers, libraries, and other accompanying middleware applications.
  • Customer-facing experience. Able to write technical documents and communicate at an appropriate level depending on the audience.
  • Some Linux administration; understanding setup for HPC/AI middleware.

Nice to Haves:
  • Hands-on AI experience within automotive, finance, healthcare, defense verticals.
  • Programming experience with any of HIP, CUDA, Python, C/C++, Fortran, OpenACC, OpenMP, pSTL.
  • Understanding impact of inter-node network choices on performance at scale. Creating performance projections for applications.
  • Deep Neural Networks and their design for different Machine Learning cases.
  • Any experience understanding/inspecting/writing assembly.
  • Understanding of memory and cache hierarchy and methods to query performance/latency at each level. Inspecting and dataflow down to the register-level.
  • Datacenter deployment and management experience.
  • Government level security clearance.

ACADEMIC CREDENTIALS:
  • Bachelors' Degree in a technical field (Computer Science, Electrical Engineering, Physics, Mathematics) preferred

This role is not eligible for visa sponsorship.
LOCATION:
Austin, TX and Remote
#LI-RF1
#LI- REMOTE
Benefits offered are described: AMD benefits at a glance.
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants' needs under the respective laws throughout all stages of the recruitment and selection process.
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD's "Responsible AI Policy" is available here.
This posting is for an existing vacancy.

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