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Hourly Embedded Machine Learning Jobs in Washington

Geico

Director, Machine Learning Engineering

Bethesda, MD · On-site

$150K - $300K/yr

Multiple factors are taken into consideration to arrive at the final hourly rate/ annual salary to be offered to the selected candidate. Factors include, but are not limited to, the scope and ...

MORSE Corp

Data Scientist- TS/SCI

Arlington, VA · On-site

You will be part of teams performing Test and Evaluation (T&E) of AI and machine learning models ... embedded systems. * Support the transition of the developed algorithms to software using one or ...

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Hourly Embedded Machine Learning information

What is an hourly embedded machine learning engineer?

An Hourly Embedded Machine Learning engineer is a professional who specializes in developing and deploying machine learning models on embedded systems, such as microcontrollers, IoT devices, or edge devices, and is compensated on an hourly basis rather than a salaried or project-based arrangement. These engineers work to optimize algorithms so they can run efficiently on devices with limited computing power, memory, and energy resources. Their responsibilities often include model selection, quantization, optimization, and integration of machine learning pipelines into hardware. Hiring on an hourly basis allows for flexibility in project scope and duration, making it ideal for companies with specific, time-limited needs. They often collaborate with hardware engineers, data scientists, and software developers to create intelligent embedded solutions.

How does an hourly embedded machine learning professional typically collaborate with hardware and software teams during a project?

As an Hourly Embedded Machine Learning professional, you will often work closely with both hardware and software engineering teams to ensure that machine learning models are efficiently integrated into embedded systems. This typically involves frequent communication to align on hardware constraints, such as memory and processing power, and to optimize algorithms for real-time performance. You may also participate in joint debugging sessions and code reviews to address integration issues and streamline deployment. Collaboration is key, as successful projects depend on the seamless interaction between machine learning solutions and the embedded hardware platform.

What are the key skills and qualifications needed to thrive as an hourly embedded machine learning engineer, and why are they important?

To thrive as an Hourly Embedded Machine Learning Engineer, you need a solid background in embedded systems, machine learning algorithms, and programming languages like C/C++ and Python, often supported by a degree in computer engineering or a related field. Familiarity with tools such as TensorFlow Lite, embedded Linux, microcontroller development environments, and model optimization frameworks is typically required. Strong problem-solving skills, adaptability, and effective communication help you address complex technical challenges and collaborate with cross-functional teams. These skills are crucial for designing efficient, real-time ML solutions that operate reliably on resource-constrained embedded devices.

What is the difference between Hourly Embedded Machine Learning vs Hourly Data Scientist?

AspectHourly Embedded Machine LearningHourly Data Scientist
CredentialsKnowledge of embedded systems, programming, ML algorithmsDegree in Data Science, Statistics, or related field
Work EnvironmentEmbedded hardware, IoT devices, real-time systemsData analysis, modeling, visualization in office or cloud
Industry UsageConsumer electronics, automotive, IoT devicesFinance, healthcare, marketing, research

Hourly Embedded Machine Learning specialists focus on integrating ML models into embedded systems and hardware, often working with IoT devices and real-time constraints. In contrast, Hourly Data Scientists analyze large datasets to develop predictive models primarily in cloud or office environments. While both roles require programming skills, embedded ML emphasizes hardware integration, whereas data science centers on data analysis and visualization.

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

The most popular types of Embedded Machine Learning jobs in Washington are:

What are popular job titles related to Hourly Embedded Machine Learning jobs in Washington?

For Hourly Embedded Machine Learning jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Hourly Embedded Machine Learning jobs in Washington look for?

The top searched job categories for Hourly Embedded Machine Learning jobs in Washington are:

What cities in Washington are hiring for Hourly Embedded Machine Learning jobs?

Cities in Washington with the most Hourly Embedded Machine Learning job openings:

Hourly Embedded Machine Learning jobs near you
Infographic showing various Hourly Embedded Machine Learning job openings in Washington as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 21% Part Time, and 2% Contract. Highlights an 84% Physical, 2% Hybrid, and 14% Remote job distribution.

Artificial Intelligence/Machine Learning (AI/ML) Engineer

Annapolis, MD • On-site

Base-2 Solutions, LLC
Custom Software Development Services • 51 - 200 employees
Learn more about Base-2 Solutions, LLCexternal

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 29 days ago

Job description

  • Requisition ID: 4351
  • Standard Title:
  • Required Security Clearance: Top Secret/SCI with Full Scope Polygraph
  • Location: Annapolis Junction, MD
  • Work Type: On-Site
  • Shift: First
  • Referral Eligibility: Eligible
  • U.S. Citizenship Required? Yes

Position Summary
Base-2 Solutions is seeking an AI/ML Engineer who designs, creates, tests, and productizes AI/ML algorithms to solve business challenges. The AI/ML models they create should be capable of learning and making predictions as defined by the business logic developed to meet customer requirements. The AI/ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics application, interpretation, and presentation. The AI/ML Engineer needs familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models through iterative user and system feedback, the AI/ML Engineer designs and creates scalable solutions for optimal performance. The AI/ML Engineer may be responsible for leading geographically diverse teams and will often serve as a primary POC for AI-related matters, so must have exceptional analytical, problem-solving and communication skills.
Essential Duties and Responsibilities
  • Experience with standard machine language frameworks, e.g. Pytorch, TensorFlow.
  • Select appropriate data sets.
  • Perform statistical analysis.
  • Run machine learning algorithms.
  • Use results to improve models.
  • Train and retrain systems when needed.
  • Experience in working with various ML libraries and packages.
  • Run standard test and evaluation protocols.
  • Provide system integration oversight.
  • Oversee Test and evaluation of AI and ML algorithms through an iterative design process to meet verification and validation requirements.
  • Research and implement a broad range of AI and ML algorithms and tools.
  • Design or Select appropriate data and knowledge representation methods.
  • Recognize software architecture, data modelling, and data structures.
  • Transform and convert data science prototypes into scalable solutions.
  • Verify data and model output quality.
  • Identify differences in data distribution that affect model performance.
Required Qualifications
  • Five (5) years experience in applied machine learning in programs and contracts of similar scope, type, and complexity is required.
  • A Master's or Ph.D. degree in advanced math, artificial intelligence, data science, computer science or deep learning from an accredited college or university.
  • Five (5) additional years of machine learning experience with a relevant Bachelor's degree may be substituted for a Master's degree.
Preferred Qualifications
  • Expert knowledge with multiple programming languages, including: Python Java R.
Required Education and Experience Equivalency
  • Master's or Ph.D. degree in advanced math, artificial intelligence, data science, computer science or deep learning from an accredited college or university with Five (5) years experience in applied machine learning in programs and contracts of similar scope, type, and complexity.
  • Relevant Bachelor's degree with Ten (10) years experience in applied machine learning in programs and contracts of similar scope, type, and complexity.
Required Certifications
  • None specified.
Required Security Clearance
  • Active Top Secret/SCI with Full Scope Polygraph

Pay & Benefit Highlights
Compensation
  • Competitive fixed salary or hourly pay (based on experience, skills, location, and internal equity).
  • Employee referral bonuses up to $10,000 per hired referral.
  • Additional bonus opportunities for exceptional performance and contributions to business development and company growth (role-dependent).
Health
  • 100% company-paid medical premiums for employees and eligible dependents.
  • Choose from multiple plan options with CareFirst, Kaiser, and UnitedHealthcare, including PPO, POS, HMO, and HSA-compatible plans.
  • 100% company-paid dental premiums for employees and eligible dependents.
  • 100% company-paid vision premiums for employees and eligible dependents.
Income Protection
  • 100% company-paid premiums for short-term disability.
  • 100% company-paid premiums for long-term disability.
  • 100% company-paid premiums for accidental death & dismemberment (AD&D).
  • 100% company-paid premiums for life insurance up to $200,000.
Retirement
  • 401(k) with immediate vesting: 4% company match plus a 4% non-elective company contribution (8% total).
  • 401(k) pre-tax and Roth options.
Leave
  • Up to 20 days of flexible paid time off (PTO).
  • 11 paid floating holidays.
Work-Life Balance
  • Flexible work schedules, including flex time and compressed work periods (contract and project-dependent).

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