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Ai Applications Engineer Jobs in Minnesota (NOW HIRING)

NextSilicon

HPC Applications Engineer

Minneapolis, MN · On-site +1

We are looking for an experienced Applications Engineer to join the Applications team, with ... Hands-on experience with one or more AI/ML frameworks, such as pytorch Responsibilities * Profile ...

MAS HVAC, Inc

Senior Applications Engineer

Maple Grove, MN · On-site

$90 - $130/hr

... for AI infrastructure to scale, our culture remains people-first by design.It is core to our ... Position Summary:The Senior Applications Engineer will support the design, application, and ...

Posted today

C4 Technical Services

AI/ML Engineer

Minneapolis, MN · On-site

This role will focus on healthcare and enterprise AI applications, including LLM-powered assistants ... AI/ML Engineering & MLOps Experience developing, evaluating, deploying, and monitoring ML/LLM ...

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Jobgether

Java Gen AI Engineer

Finland, MN · On-site

$81 - $127/hr

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Java Gen AI Engineer based in Finland. This role offers an ...

Blank Metal Inc

AI Engineer

Minneapolis, MN · On-site

$108K - $146K/yr

Our team of seasoned engineers, designers, and strategists work across industries to create AI ... You understand how to integrate LLM APIs and AI services into robust applications, architect ...

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Ai Applications Engineer information

What is an AI applications engineer?

AI Applications Engineers are professionals who design, develop, and integrate artificial intelligence (AI) solutions into software applications to solve real-world problems. They work closely with data scientists, software engineers, and business stakeholders to build and deploy machine learning models, automate processes, and enhance user experiences. Their responsibilities often include selecting appropriate AI technologies, writing code, testing models, and optimizing performance. AI Applications Engineers play a key role in translating AI research and prototypes into scalable and maintainable products used in industries like healthcare, finance, retail, and more.

What are the key skills and qualifications needed to thrive as an AI applications engineer?

To thrive as an AI Applications Engineer, you need strong programming abilities (Python, Java, or C++), a solid understanding of machine learning algorithms, and a relevant degree in computer science or engineering. Familiarity with AI frameworks (such as TensorFlow or PyTorch), cloud platforms, and data processing tools is typically required, along with certifications in machine learning or AI. Excellent problem-solving, collaboration, and communication skills help you translate business needs into effective AI solutions and work efficiently with cross-functional teams. These skills are critical for building scalable, reliable AI systems that deliver tangible value to organizations.

How does an AI applications engineer typically collaborate with data scientists and software developers on project teams?

As an AI Applications Engineer, you will often serve as a bridge between data scientists, who build and optimize machine learning models, and software developers, who integrate these models into production systems. Collaboration usually involves translating model requirements into scalable application features, ensuring model outputs align with user needs, and troubleshooting technical challenges that arise during deployment. Regular meetings, code reviews, and shared documentation are common practices to keep everyone aligned and ensure seamless integration. This cross-functional teamwork enhances both the technical robustness and usability of AI-powered applications.

What is the difference between Ai Applications Engineer vs Data Scientist?

AspectAi Applications EngineerData Scientist
Required CredentialsBachelor's in CS, Engineering, or related; knowledge of AI/ML toolsBachelor's or higher in CS, Statistics, or related; strong analytical skills
Work EnvironmentDevelops AI solutions, collaborates with engineering teamsAnalyzes data, builds models, interprets results
Employer & Industry UsageTech companies, AI startups, R&D departmentsFinance, healthcare, tech, research institutions

While both roles involve AI and data, Ai Applications Engineers focus on developing and deploying AI solutions in engineering contexts, whereas Data Scientists analyze data to extract insights. The roles often overlap but differ mainly in their primary focus and application environment.

What does an AI applications engineer do?

An AI applications engineer designs, develops, and implements artificial intelligence solutions to solve specific business problems. They work with machine learning models, data processing, and programming tools like Python or TensorFlow, often collaborating with data scientists and software developers to deploy AI systems effectively.

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Ai Applications Engineer jobs near you

HPC Applications Engineer

NextSilicon

Minneapolis, MN • On-site, Remote

Full-time

Re-posted 7 days ago

Job description

Description
NextSilicon is reimagining high-performance computing. Our accelerated compute solutions leverage intelligent adaptive algorithms to vastly accelerate supercomputers, driving them forward into a new generation. Our new software-defined hardware architecture enables HPC to fulfill its promise of breakthroughs in all fields of advanced research.
At NextSilicon, everything we do is guided by three core values:
  • Professionalism: We strive for exceptional results through professionalism and unwavering dedication to quality and performance.
  • Unity: Collaboration is key to success. That's why we foster a work environment where every employee can feel valued and heard.
  • Impact: We're passionate about developing technologies that make a meaningful impact on industries, communities, and individuals worldwide.

We are looking for an experienced Applications Engineer to join the Applications team, with demonstrated ability in moving HPC applications onto new platforms and in evaluating performance on node and at scale.
Location: Hybrid in either our Austin, TX or Minneapolis, MN offices (or willing to relocate) preferred but Remote considered for exceptional candidates.
As part of a software-defined hardware company, you will play a pivotal role in driving new software features based on your analysis of customer-defined applications and measuring the resulting performance improvements. This role stands on the edge of computer science/architecture and scientific applications which span a wide range of fields, including but not limited to graph algorithms, sparse computations, weather prediction, seismic imaging, genomics, molecular dynamics, quantum chemistry, and computational fluid dynamics. If you have a passion for science, a knack for solving complex problems, and thrive in a bleeding-edge multidisciplinary environment, we want to hear from you!
Requirements
  • US citizenship and eligibility to visit US government research facilities
  • B.S. degree in a hard science, engineering, computer science, or a related field; M.S. or Ph.D. strongly preferred

  • Hands-on experience with development applications in one or more HPC domains, particularly scientific applications that run at rack or system scale
  • High level of proficiency in one of C/C++/Fortran and familiarity with the others
  • Extensive experience with node level and distributed parallel programming models and a working understanding of OpenMP, MPI in particular
  • Ability to measure application-level performance and profile HPC applications at the node level and at scale
  • Willingness to travel as necessary
  • Ability to work remotely and independently in a fast-paced environment with minimal direct supervision

Desired Skills:
  • Expertise in competitive performance analysis is strongly preferred
  • CUDA or similar GPU kernel languages
  • Hands-on experience with one or more AI/ML frameworks, such as pytorch

Responsibilities
  • Profile and identify bottlenecks of a wide range of HPC applications on different architectures
  • Develop creative algorithmic and software solutions to solve bottlenecks and accelerate applications on a novel dataflow architecture
  • Translate application requirements into software features and hardware requirements
  • Develop performance models for future hardware architectures

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