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Tensorflow Pytorch Jobs in Utah (NOW HIRING)

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Tensorflow Pytorch information

What are the key skills and qualifications needed to thrive as a Deep Learning Engineer specializing in TensorFlow and PyTorch, and why are they important?

To thrive as a Deep Learning Engineer with a focus on TensorFlow and PyTorch, you need a strong background in computer science, mathematics, and machine learning, typically supported by a relevant degree. Proficiency in programming languages like Python, experience with TensorFlow and PyTorch frameworks, and familiarity with cloud platforms or GPU computing are essential. Analytical thinking, problem-solving, and effective communication are standout soft skills for collaborating with teams and interpreting model results. These skills are crucial for developing, deploying, and optimizing AI models that drive innovation and solve complex real-world problems.

What are TensorFlow and PyTorch?

TensorFlow and PyTorch are two of the most popular open-source deep learning frameworks used by researchers and developers to build, train, and deploy machine learning models. TensorFlow, developed by Google, offers robust support for production environments and has a large ecosystem. PyTorch, developed by Facebook, is known for its flexibility, ease of use, and dynamic computational graph, making it popular in academia and research. Both frameworks support a wide range of neural network architectures and are used extensively for tasks such as computer vision, natural language processing, and reinforcement learning.

What is the difference between Tensorflow Pytorch vs Data Scientist?

AspectTensorflow PytorchData Scientist
Required SkillsDeep learning frameworks, Python, machine learningData analysis, statistical skills, Python/R, machine learning
Work EnvironmentAI/ML development, research, software engineeringData analysis, reporting, business insights
Industry UsageAI/ML projects, research labs, tech companiesBusiness, finance, healthcare, tech

Tensorflow and Pytorch are deep learning frameworks used primarily by AI/ML developers, while Data Scientists utilize these tools for data analysis and modeling. Although their skill sets overlap, Tensorflow Pytorch focus on model development, whereas Data Scientists apply these models to derive insights and inform decisions.

How do TensorFlow/PyTorch engineers typically collaborate with data scientists and other team members in a production environment?

TensorFlow and PyTorch engineers often work closely with data scientists to transform experimental machine learning models into efficient, scalable production solutions. Collaboration involves frequent code reviews, shared development environments, and regular meetings to align model requirements with deployment constraints. Engineers also coordinate with DevOps teams to ensure smooth integration and monitoring of models in production. Strong communication skills and a willingness to iterate on solutions are essential for bridging the gap between research and real-world application.
What are popular job titles related to Tensorflow Pytorch jobs in Utah? For Tensorflow Pytorch jobs in Utah, the most frequently searched job titles are:
What cities in Utah are hiring for Tensorflow Pytorch jobs? Cities in Utah with the most Tensorflow Pytorch job openings:

Data Scientist - Applied AI Scientist

Enterprise Technology Operations

Midvale, UT • Hybrid

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 11 days ago


Job description

Zions Bancorporation's Enterprise Technology and Operations (ETO) team is transforming what it means to work for a financial institution. With a commitment to technology and innovation, we have been providing our community, clients and colleagues the best experience possible for over 150 years. Help us transform our workforce of the future, today.

Zions Bancorporation's Innovation Lab is seeking a creative and driven Data Scientist (Applied AI Scientist) who bridges the gap between rigorous statistical research and production-grade software engineering. This role is at the heart of our innovation engine. You will not only uncover deep data insights and design advanced AI algorithms, but you will also architect the robust, scalable code required to bring those concepts to life.

As a key member of the Innovation Lab, you will work in a fast-paced, experimental environment, turning ambiguous business challenges into tangible, data-driven prototypes. We need a scientist who treats machine learning as an engineering discipline, someone who understands the "why" behind the math, and the "how" of robust software implementation.

Visa Sponsorship:
This Data Scientist position is currently NOT eligible for employment visa sponsorship (e.g., H-1B visa). This includes, for example, situations where a candidate may have temporary work authorization while enrolled in school or upon graduation (e.g., CPT, OPT) but would need H-1B visa sponsorship within a few years of employment in order to maintain employment eligibility.

Responsibilities:

  • End-to-End AI Design: Design, prototype, and validate ML/AI solutions, translating complex business challenges into mathematical formulations and scalable, production-ready code.
  • Advanced Analytics & EDA: Perform deep exploratory data analysis, statistical testing, and data transformations on diverse datasets (structured and unstructured) to uncover predictive signals and validate hypotheses.
  • Production-Grade Science: Architect and implement modular, extensible, and testable Python codebases for AI experiments. Move beyond Jupyter notebooks by applying clean-code principles (SOLID, DRY) for seamless hand-off to ETO Engineering teams.
  • Agentic & Generative AI: Develop and experiment with applied generative AI and multi-agent architectures using orchestration frameworks (e.g., LangChain, LangGraph), focusing on optimal state management, robust RAG pipelines, and efficient system design.
  • Algorithmic Optimization: Optimize model inference, data processing pipelines, and memory footprints for latency and scalability, applying a strong understanding of data structures and algorithmic complexity.
  • Rigorous Evaluation: Build automated evaluation frameworks to benchmark model performance, mitigate hallucinations, track drift, and ensure algorithmic fairness via A/B testing and statistical rigor.
  • Collaboration & Communication: Act as the technical translator between research-focused ideation and engineering execution. Communicate complex statistical findings and system architectures to both technical and non-technical stakeholders.

Qualifications:

  • The Scientist's Mind: Solid foundation in statistics (Bayesian/Frequentist), linear algebra, hypothesis testing, and the internal mechanics of ML algorithms (e.g., how optimizers work, loss functions, attention mechanisms).
  • The Engineer's Toolbelt: Advanced Python proficiency with a strong focus on Object-Oriented Programming (OOP) and modular design. You must be comfortable writing unit tests (e.g., Pytest) for your data pipelines and models.
  • Framework Depth: Deep expertise with ML libraries (PyTorch, TensorFlow, Scikit-learn, Pandas) and experience implementing custom logic, rather than just calling out-of-the-box models.
  • Generative AI Systems: Hands-on experience with NLP, Large Language Models (LLMs), and Vector Databases, with an understanding of how to evaluate and optimize these systems at scale.
  • Software Maturity: Proficiency with Git/version control, containerization (Docker), API development (FastAPI/Flask), and a working knowledge of how models fit into a CI/CD lifecycle (MLOps).
  • Problem Solving: Exceptional problem-solving skills, comfort with ambiguity, and the ability to own the data science lifecycle from abstract ideation to engineered prototype.
  • Education & Experience: Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field plus 4+ years of hands-on experience in applied machine learning or data science. A Master's degree or PhD is a plus. A combination of education and experience may meet qualifications.

Location:

This position has a hybrid work from home schedule with a minimum of three days per week in the office at the new Zions Technology Center in Midvale, UT.

The Zions Technology Center is a 400,000-square-foot technology campus in Midvale, Utah. Located on the former Sharon Steel Mill superfund site, the sustainably built campus is the company's primary technology and operations center. This modern and environmentally friendly technology center enables Zions to compete for the best technology talent in the state while providing team members with an exceptional work environment with features such as:

  • Electric vehicle charging stations and close proximity to Historic Gardner Village UTA TRAX station.
  • At least 75% of the building is powered by on-site renewable solar energy.
  • Access to outdoor recreation, parks, trails, shareable bikes and locker rooms.
  • Large modern cafe with a healthy and diverse menu.
  • Healthy indoor environment with ample natural light and fresh air.
  • LEED-certified sustainable building that features include the use of low VOC-emitting construction materials.

Benefits:

  • Medical, Dental and Vision Insurance - START DAY ONE!
  • Life and Disability Insurance, Paid Parental Leave and Adoption Assistance
  • Health Savings (HSA), Flexible Spending (FSA) and dependent care accounts
  • Paid Training, Paid Time Off (PTO) and 11 Paid Federal Holidays
  • 401(k) plan with company match, Profit Sharing, competitive compensation in line with work experience
  • Mental health benefits including coaching and therapy sessions
  • Tuition Reimbursement for qualifying employees
  • Employee Ambassador preferred banking products

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