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Machine Learning Engineer Quantization Jobs in Minnesota

NxT Level

Principal AI / ML Engineer

Minneapolis, MN · On-site

We're looking for a Principal Machine Learning Engineer to build AI features for the family. Qualifications: * A confident craftsperson who possesses problem-solving tools and can discuss multiple ...

Deloitte

AI Solutions Architect

Minneapolis, MN

$65.75 - $86.75/hr

Certifications in artificial intelligence, machine learning, or cloud platforms, such as AWS Certified Machine Learning - Specialty, Google Cloud Professional Machine Learning Engineer, Microsoft ...

UnitedHealth Group

Data Scientist

Minnetonka, MN · On-site

$60K - $107K/yr

... ML Engineering Team, responsible for designing and deploying advanced Machine Learning and ... Experience optimizing model inference using quantization, distillation, or distributed GPU compute

New

UnitedHealth Group

Data Scientist

Minnetonka, MN · Remote

$60K - $107K/yr

... ML Engineering Team, responsible for designing and deploying advanced Machine Learning and ... Experience optimizing model inference using quantization, distillation, or distributed GPU compute

New

Deloitte

Senior AI Engineer - SFL Scientific

Minneapolis, MN

$109K - $149K/yr

Work You'll Do As a Senior AI Engineer, you'll work cross-functionally with data scientists, machine learning engineers, project managers, and industry experts to develop robust AI infrastructure and ...

Thomson Reuters

Lead Research Engineer

Eagan, MN · On-site

$104K - $137K/yr

We hire engineers and specialists across a variety of AI research areas to drive the company ... Experience integrating Machine Learning solutions into production-grade software with a sound ...

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Machine Learning Engineer Quantization information

What are some common challenges Machine Learning Engineers face when implementing quantization techniques in production models?

Machine Learning Engineers working on quantization often encounter challenges such as balancing reduced model size and computational efficiency with maintaining acceptable accuracy levels. Adapting quantization methods to different hardware platforms can also require significant testing and optimization. Additionally, engineers must frequently address compatibility issues with existing deployment pipelines and ensure that quantization-aware training is properly integrated to minimize performance degradation. Collaboration with hardware and software teams is essential to streamline deployment and achieve optimal results.

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

To thrive as a Machine Learning Engineer Quantization, you need a solid background in machine learning, deep learning, and computer science, typically supported by a degree in a related field. Familiarity with quantization techniques, frameworks such as TensorFlow Lite or PyTorch, and experience with hardware accelerators are crucial. Strong problem-solving skills, attention to detail, and effective collaboration set top performers apart. These capabilities are vital for efficiently deploying high-performing models on resource-constrained devices and ensuring scalable, real-world AI solutions.

What does a Machine Learning Engineer Quantization do?

A Machine Learning Engineer specializing in quantization focuses on optimizing machine learning models by reducing their size and computational requirements without significantly sacrificing accuracy. This involves converting model parameters and computations from high-precision formats (like 32-bit floating point) to lower-precision formats (such as 8-bit integers). Quantization enables faster inference, lower memory usage, and allows models to run efficiently on edge devices and mobile platforms. These engineers work closely with data scientists and hardware teams to implement, test, and validate quantized models in production environments.

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

AspectMachine Learning Engineer QuantizationData Scientist
Required CredentialsBachelor's or master's in CS, ML, or related; certifications in ML or AIBachelor's or master's in statistics, CS, or related; certifications in data analysis or statistics
Work EnvironmentDeveloping optimized ML models, deploying quantized models for efficiencyAnalyzing data, building predictive models, interpreting results
Industry UsageTech companies, AI hardware firms, embedded systemsFinance, healthcare, marketing, research institutions

Machine Learning Engineer Quantization focuses on optimizing ML models for deployment efficiency, often working closely with hardware and software teams. Data Scientists analyze data and build models for insights. While both roles require ML knowledge, quantization engineers specialize in model compression techniques, whereas data scientists focus on data analysis and interpretation.

What are popular job titles related to Machine Learning Engineer Quantization jobs in Minnesota? For Machine Learning Engineer Quantization jobs in Minnesota, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Quantization jobs in Minnesota look for? The top searched job categories for Machine Learning Engineer Quantization jobs in Minnesota are:
What cities in Minnesota are hiring for Machine Learning Engineer Quantization jobs? Cities in Minnesota with the most Machine Learning Engineer Quantization job openings:
Machine Learning Engineer Quantization jobs near you
Principal AI / ML Engineer

Principal AI / ML Engineer

NxT Level

Minneapolis, MN • On-site

Full-time

Medical, Retirement, PTO

Re-posted 17 days ago

Job description

Our client is a digital consumer product. They're building social features using LLMs and Machine Learning to add to user experience. We're looking for a Principal Machine Learning Engineer to build AI features for the family.
Qualifications:
  • A confident craftsperson who possesses problem-solving tools and can discuss multiple approaches while preferring the best approach given the constraints.
  • A scientific artist and artistic scientist who can navigate complexity and bring clarity to intricate problems while appreciating simplicity as the highest sophistication.
  • A pragmatic optimizer who identifies the need for change, articulates it, and drives incremental improvements toward ambitious goals.
  • An advocate for team collaboration and open communication.
  • Possesses comprehensive knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch) and advanced NLP techniques.
  • Significant experience in AI/ML engineering with a strong data science background.
  • Demonstrated ability in designing and training LLMs.
  • Expertise in Databricks/Apache Spark for large-scale data processing in AI/ML applications.
  • Proficiency with AWS Bedrock and Comprehend.
  • Demonstrated leadership in AI/ML solutions in a commercial environment.
  • Proficiency in programming languages such as R, Python, and Java, with a focus on data exploration and processing.
  • Proven ability to lead and inspire teams of senior engineers and data scientists.
  • Effective individual contributor within a collaborative team environment.
  • Strong analytical, problem-solving, and communication skills.

Responsibilities:
  • Collaborate with the Data team to transform Data Lakehouse potential into actionable insights and services that enhance user experiences.
  • Shape AI/ML strategies, particularly in LLMs, to drive business outcomes and establish data science principles.
  • Implement technologies in AWS and Databricks for handling and processing large datasets for advanced language model training.
  • Lead data science practices, including model selection and training methodology.
  • Build, train, manage, and host LLMs using Data Lakehouse data.
  • Optimize AI system performance through testing and tuning, leveraging data science and Databricks methodologies.
  • Ensure ethical standards and data privacy compliance in AI solutions.
  • Provide specialized AI/ML expertise to guide the development and optimization of AI features.
  • Stay updated on the latest AI/ML and data science trends, especially in LLMs and AWS, Databricks/Apache Spark technology.

Benefits:
  • Full Medical Coverage: OFW covers 100% of premiums for employees and their families.
  • 401k: Up to a 4% match with immediate vesting.
  • 12 weeks of paid leave for all new parents.
  • Learning & Development stipend for employees.
  • Supportive and flexible working environment, allowing remote work from anywhere.

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