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Pytorch Huggingface Jobs in Boston, MA (NOW HIRING)

Microsoft

Research Intern - Self-Improving AI

Cambridge, MA · On-site

$6.7K - $13K/mo

Experience with deep learning and language modeling frameworks including pytorch, huggingface transformers, vLLM. Applied Sciences IC2 - The base pay range for this internship is USD $5,610 - $11,010 ...

XPO

Applied AI Scientist - Hybrid

Boston, MA · On-site

$100K - $120K/yr

PyTorch, HuggingFace) * Strong communication skills, with the ability to explain AI system behavior and tradeoffs to technical and business stakeholders, and to collaborate closely with optimization ...

New

XPO Logistics

Applied AI Scientist - Hybrid

Boston, MA · On-site

$100K - $120K/yr

PyTorch, HuggingFace) * Strong communication skills, with the ability to explain AI system behavior and tradeoffs to technical and business stakeholders, and to collaborate closely with optimization ...

XPO Logistics

Applied AI Scientist - Hybrid

Boston, MA

$100K - $120K/yr

PyTorch, HuggingFace) * Strong communication skills, with the ability to explain AI system behavior and tradeoffs to technical and business stakeholders, and to collaborate closely with optimization ...

S&P Global

Machine Learning Engineer II

Cambridge, MA · On-site

ML: PyTorch, Transformers, HuggingFace, LangChain * Tools/Toolkits: Claude Code, Weights & Biases, OpenSearch, PostgreSQL/PGVector, LiteLLM * Techniques : Agentic Search, Prompt Engineering ...

Klaviyo

Senior Software Engineer, Agent Platform

Boston, MA · On-site

$133K - $175K/yr

Knowledge of ML frameworks (Huggingface, PyTorch, TensorFlow, Keras). We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it ...

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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.

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

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Pytorch Huggingface jobs near you
Infographic showing various Pytorch Huggingface job openings in Boston, MA as of August 2026, with employment types broken down into 1% Internship, 88% Full Time, 9% Part Time, and 2% Contract. Highlights an 79% Physical, 3% Hybrid, and 18% Remote job distribution.

Senior Machine Learning Engineer I, Physical Sciences

Cambridge, MA • On-site

Lila Sciences
See more jobs at Lila Sciencesexternal

$133K - $176K/yr

Full-time

Re-posted 22 days ago

Job description

Your Impact at LILA

This Machine Learning Engineer for the Physical Sciences team focuses on building and operating end-to-end, scalable machine learning workflows that solve a diversity scientific use cases in materials, chemistry and physical sciences. Your work will advance research efforts on state-of-the-art algorithms to build towards scientific superintelligence across today's greatest challenges in physical sciences.

What You'll Be Building

  • Design, implement, and maintain endtoend ML pipelines (data ingestion, feature engineering, training, evaluation, deployment, monitoring).
  • Productionize models and services with robust testing, observability, and documentation in collaboration with cross-functional software teams and build CI/CD workflows and automated evaluations to ensure safe, frequent releases.
  • Collaborate with domain scientists and platform engineers to translate research insights into performant, scalable systems.
  • Contribute to technical design reviews, coding standards, and mentoring of best practices.

What You'll Need to Succeed

  • BS/MS/PhD in Computer Science, Engineering, or a related quantitative field, or equivalent industry experience.
  • Strong Python software engineering fundamentals (testing, packaging, typing); experience with machine learning frameworks (e.g., PyTorch, Huggingface, etc.).
  • Experience deploying ML services to production in cloud-based infrastructure (FastAPI/GRPC, containers, orchestration, cloud infra).
  • Handson experience with model deployment in production systems (LLMs, multimodal models, databases, RAG) with strong debugging and profiling skills.
  • Clear communication and collaboration in crossfunctional settings.

Bonus Points For

  • Exposure to scientific or engineering domains (materials, chemistry, physics) and related data formats/benchmarks.
  • GPU optimization experience (CUDA, Triton, compilation, distributed training).
  • Prior contributions to opensource ML or scientific software.
  • Experience with workflow orchestration, data provenance, or largescale compute environments.

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