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Mobile Machine Learning Jobs in Maryland (NOW HIRING)

Xcelerate Solutions

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Senior GPU Platform Engineer (Linux/CUDA) - TS/SCI

Bethesda, MD · On-site

$134K - $185K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... both mobile and desktop Linux platforms. Continuously assess and enhance power efficiency ... Experience with machine learning and neural network frameworks on GPUs in Linux. * Knowledge of GPU ...

Cymertek

Software Engineer

Annapolis Junction, MD · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... Mobile Application Developer, Embedded Software Engineer, Cloud Software Engineer, DevOps Engineer, QA Engineer, Test Automation Engineer, Game Developer, Data Engineer, Machine Learning Engineer, AI ...

Deloitte

Oracle Gen AI Manager

Baltimore, MD · On-site

... machine learning initiatives * Identify high-value AI use cases and guide teams on prompt ... Do you have the ability to transform an organization through the latest social, mobile, and ...

TEKsystems c/o Allegis Group

Data Engineer

Baltimore, MD · On-site

$113K - $136K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... web, mobile app, full stack or integrations hosted on premises data centers or in the cloud ... machine learning, or scientific computing Utilize a deep understanding of AI/ML/data applications ...

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All JobsMobile Machine Learning JobsMobile Machine Learning Jobs in Maryland

Mobile Machine Learning information

What are the key skills and qualifications needed to thrive as a mobile machine learning engineer, and why are they important?

To thrive as a Mobile Machine Learning Engineer, you need a solid background in computer science, machine learning, and mobile application development, often supported by a relevant degree and experience. Proficiency with ML frameworks (like TensorFlow Lite or Core ML), mobile platforms (Android/iOS), and deployment tools is typically required. Strong problem-solving skills, adaptability, and effective communication set standout professionals apart in this field. These skills are crucial for successfully developing, optimizing, and integrating machine learning models into efficient and user-friendly mobile applications.

What is mobile machine learning?

Mobile machine learning refers to the development and deployment of machine learning models on mobile devices such as smartphones and tablets. It enables apps to perform tasks like image recognition, language translation, and speech processing directly on the device without needing to send data to the cloud. This approach improves privacy, reduces latency, and can work even without an internet connection. Developers use frameworks like TensorFlow Lite, Core ML, and PyTorch Mobile to optimize models for the limited resources of mobile hardware.

What is the difference between Mobile Machine Learning vs Data Scientist?

AspectMobile Machine LearningData Scientist
Required CredentialsBachelor's in CS, ML, or related; experience with mobile platformsBachelor's or higher in CS, Statistics, or related; data analysis skills
Work EnvironmentMobile app development teams, on-device processingData analysis teams, research environments
Industry UsageMobile app companies, tech startupsFinance, healthcare, tech firms
Common Search/ComparisonYesYes

Mobile Machine Learning focuses on developing ML models optimized for mobile devices and integrating them into mobile apps. Data Scientists analyze large datasets to extract insights and build predictive models across various industries. While both roles require programming and ML knowledge, Mobile Machine Learning emphasizes on-device deployment and mobile platform expertise, whereas Data Scientists focus on data analysis and model development for broader applications.

What are some common challenges faced by mobile machine learning engineers when deploying models on mobile devices?

Mobile Machine Learning engineers often encounter challenges related to limited computational resources and memory constraints on mobile devices. Optimizing models for efficient inference without significant loss in accuracy is a key hurdle, as is ensuring compatibility across different devices and operating systems. Additionally, balancing power consumption and real-time performance is critical, so engineers frequently collaborate with mobile app developers and hardware specialists to deliver seamless user experiences while maintaining model integrity.

What are the most commonly searched types of Machine Learning jobs in Maryland?

The most popular types of Machine Learning jobs in Maryland are:

What are popular job titles related to Mobile Machine Learning jobs in Maryland?

For Mobile Machine Learning jobs in Maryland, the most frequently searched job titles are:

What job categories do people searching Mobile Machine Learning jobs in Maryland look for?

The top searched job categories for Mobile Machine Learning jobs in Maryland are:

What cities in Maryland are hiring for Mobile Machine Learning jobs?

Cities in Maryland with the most Mobile Machine Learning job openings:

Mobile Machine Learning jobs near you

Principal Data Scientist, AI Foundations

AIToolboard

Potomac, MD • On-site

$180 - $260/hr

Other

Posted 10 days ago

Job description

Jobs / Principal Data Scientist, AI Foundations

Principal Data Scientist, AI Foundations

Full-time and Part-time

About the Role

Principal Data Scientist, AI FoundationsData is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making.As a Data Scientist at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.Team DescriptionAI Foundations Specialist Models Data Science team builds and ships state of the art scalable architecture, AI/ML solutions for Capital One’s award-winning mobile app. We partner with product, tech and design teams to deliver app features that delight customers with dynamic and personalized experiences, enable them to chat with Capital One’s digital assistant Eno, or search for useful contents. You will be the driving force to experiment, innovate and create next generation experiences powered by the latest emerging generative AI technologies.In this role, you will:

  • Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI powered products that change how customers interact with their money.
  • Leverage a broad stack of technologies — Pytorch, AWS Ultraclusters, Hugging Face, LangChain, Lightning, VectorDBs, and more — to reveal the insights hidden within huge volumes of numeric and textual data.
  • Be the expert in Natural Language Processing (NLP) to harness the power of Large Language Models (LLMs), adapt and finetune them for customer facing applications and features.
  • Build machine learning and NLP models through all phases of development, from design through training, evaluation, and validation; partnering with engineering teams to operationalize them in scalable and resilient production systems that serve 80+ million customers.
  • Flex your interpersonal skills to translate the complexity of your work into tangible business goals.
The Ideal Candidate is:
  • Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it’s about making the right decision for our customers.
  • Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them.
  • Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You’re not afraid to share a new idea.
  • A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You're passionate about talent development for your own team and beyond.
  • Technical. You’re comfortable with advanced ML and DL technologies including language models and are passionate about developing further. You have hands-on experience working with LLMs and solutions using open-source tools and cloud computing platforms.
  • Influential. You are passionate about AI/ML and can bring along a cross functional team in breakthrough innovations. You communicate clearly and effectively to share your findings with non-technical audiences.
  • You are experienced in training language models or large computer vision models as well as have expertise in one or more key subdomains such as: training optimization, self-supervised learning, explainability, RLHF.
  • You have an engineering mindset as shown by a track record of delivering models at scale both in training data and inference volumes. You have experience in delivering libraries, platforms, or solution level code to existing products.
Basic Qualifications:
  • Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date:
  • A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 5 years of experience performing data analytics
  • A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 3 years of experience performing data analytics
  • A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field)
Preferred Qualifications:
  • Master’s Degree in “STEM” field (Science,
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