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Junior Machine Learning Compiler Engineer Jobs in Ohio

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Tata Consultancy Services

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Junior Machine Learning Compiler Engineer information

What are typical projects and responsibilities for a Junior Machine Learning Compiler Engineer in a collaborative team setting?

As a Junior Machine Learning Compiler Engineer, you can expect to work on projects that focus on optimizing machine learning models for performance and deployment across various hardware platforms. Typical responsibilities include assisting in developing and debugging compiler passes, implementing optimizations, and contributing to code reviews. You'll frequently collaborate with senior engineers, data scientists, and hardware specialists to ensure that models are efficiently translated and executed. This role offers valuable learning opportunities through hands-on coding, exposure to state-of-the-art ML frameworks, and regular team meetings for knowledge sharing and mentorship.

What does a Junior Machine Learning Compiler Engineer do?

A Junior Machine Learning Compiler Engineer helps design, develop, and optimize compilers for machine learning models. Their work involves translating high-level machine learning code into efficient low-level code that can run on various hardware platforms, such as CPUs, GPUs, or specialized AI chips. They often collaborate with software engineers and data scientists to ensure that machine learning workloads run efficiently and correctly. This role typically involves programming, debugging, and performance tuning, often using languages like C++, Python, and specialized frameworks.

What are the key skills and qualifications needed to thrive as a Junior Machine Learning Compiler Engineer, and why are they important?

To thrive as a Junior Machine Learning Compiler Engineer, you need a solid background in computer science fundamentals, programming (especially C++ and Python), and foundational knowledge of machine learning and compiler theory. Familiarity with frameworks and tools such as LLVM, TensorFlow, MLIR, and version control systems is typically required, along with a relevant bachelor’s or master’s degree. Strong problem-solving abilities, attention to detail, and effective teamwork and communication skills set standout candidates apart. These skills and qualities are crucial for efficiently optimizing machine learning models for various hardware targets and collaborating on innovative compiler solutions.

What is the difference between Junior Machine Learning Compiler Engineer vs Data Scientist?

AspectJunior Machine Learning Compiler EngineerData Scientist
Required CredentialsBachelor's in Computer Science, Software Engineering, or related field; knowledge of compiler design and ML frameworksBachelor's or higher in Data Science, Statistics, Computer Science, or related field; strong analytical skills
Work EnvironmentSoftware development teams, focusing on compiler optimization for ML modelsData analysis teams, focusing on data interpretation and model development
Employer & Industry UsageTech companies, AI startups, hardware firmsTech firms, finance, healthcare, research institutions

The Junior Machine Learning Compiler Engineer primarily focuses on developing and optimizing compilers for machine learning models, requiring programming and compiler knowledge. In contrast, a Data Scientist analyzes data, builds models, and provides insights. Both roles are essential in AI and tech industries but differ in technical focus and daily tasks.

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Junior Machine Learning Compiler Engineer jobs near you
Artificial Intelligence / Machine Learning Engineer

Artificial Intelligence / Machine Learning Engineer

Riverside Research

Fairborn, OH • Remote

$130K - $150K/yr

Full-time

Posted 18 days ago

Job description

Riverside Overview

Riverside Research is an independent National Security Nonprofit dedicated to research and development in the national interest. We provide high-end technical services, research and development, and prototype solutions to some of the country’s most challenging technical problems. All Riverside Research opportunities require U.S. Citizenship.

Position Overview

Riverside Research is seeking an Artificial Intelligence / Machine Learning Engineer to support existing contracts to prototype and develop automation solutions to NASIC’s most difficult Scientific & Technical Intelligence problems. As a highly valued and sought-after Riverside Research employee, you will be part of a highly skilled and integrated team that analyzes intelligence data to discover opportunities for automated solutions.

Must live in or relocate to a commutable distance to Wright Patterson Airforce Base and surrounding areas.

Responsibilities

AI/ML algorithm development for prototype applications in remote sensing:

  • Develop prototype AI/ML algorithms and associated software tools using Python and the Python/TensorFlow API
  • Train AI/ML models and tune their hyperparameters for a given dataset and algorithm objectives
  • Visualize hyperparameter optimization spaces with Tensor board for selection of optimal parameters for a given parametric (functional) TensorFlow model

AI/ML dataset generation, curation, and management:

  • Provide customized solutions to data quality control that ensure accurate functional mappings for AI/ML algorithms on complex remote sensing datasets
  • Develop databases / data lakes / data warehouses for organizing both structured and unstructured datasets

AI/ML algorithm R&D:

  • Apply machine learning and general computer vision best practices and methods to analyze and exploit large, complex remote sensing datasets from a variety of remote sensing phenomenology
  • Keep up with the SoTA practices for AI/ML, perform relevant R&D, and implement new and innovative ideas in machine learning and high-performance computing to solve long-standing remote sensing “big-data” exploitation problems

Software development, documentation, and coding best practices:

  • Contribute and adhere to the AI team’s standards for reviewing and unit-testing code, lead or participate in team-wide code reviews, and adhere to standardized documentation practices
  • Utilize Python PEP8 standards

Qualifications

  • Must have minimum of Secret with able to obtain and maintain a TS/SCI clearance.
  • 5 years and a Bachelor’s Degree in either Electrical Engineering, Mathematics, Statistics, Physics, Computer Science, or related field of study
  • Must demonstrate proficiency in Python-based end-to-end AI/ML model development lifecycle using a recent deep learning platform (TensorFlow preferred)
  • Awareness of version control, branches, merge conflict resolution, and git in general
  • Proficient in collaborative Office 365 tools such as MS Word, Excel, and PowerPoint
  • Ability to work closely with subject-matter experts to develop tools, algorithms, and datasets needed for developing relevant and useful AI/ML prototype algorithms
  • Self-driven, strong analytic, inferencing, critical thinking, and creative problem-solving skills
  • Communicates highly technical results and methods clearly and succinctly

Desired Qualifications:

  • Advanced degree (MS/PhD) in Data Science, Mathematics, Statistics, Computer Science, a Physical Science or Engineering with 10 years of experience is strongly desired
  • Active TS/SCI Security Clearance
  • Experience with DoD intelligence production processes and workflows
  • 3+ years operational experience in radar signal processing analysis, overhead imagery analysis, orbital mechanics, and/or electronic warfare data analysis
  • 2+ years experience using data visualization tools and libraries in Python
  • Visualizations/Web Development Skills (e.g., Tableau, MEAN stack - MongoDB, ExpressJS, AngularJS, NodeJS)
  • Experience with large (1 GB +) image data and formats such as HDF5, JSON, GEOTIFF, TFRecords, etc.
  • Experience in development of distributed, web-based systems, service-oriented architectures, front-end user interfaces, and back-end databases are a plus
  • Experience with interpretability of deep learning computer vision models including visualization and reasoning about model latent spaces and activation maps to assess model effectiveness / weaknesses
  • Familiarity in differences of supervised learning vs. unsupervised learning techniques

Global Comp

$130,000 - $150,000 This represents the typical compensation range for this position based on experience, location and other factors.

Closing Statement

Riverside Research Institute is a not-for-profit, technology-oriented defense company, where service to our customers and support of our staff is our overall mission. Riverside is an affirmative action-equal opportunity employer and complies with all applicable federal, state, and local laws regarding recruitment and hiring. Riverside offers comprehensive compensation and benefit packages to our employees. Riverside bases its employment decisions solely on technical experience, qualifications and other job-related criteria related to our organizational purpose as a not-for-profit company, and without regard to race, color, religion, age, sex marital status, sexual orientation, national origin, physical or mental disability, veteran’s status or any other status legally protected by applicable federal, state, and local law.

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