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Machine Learning Engineer Quantization Jobs in Pennsylvania

Motional

Staff Machine Learning Engineer

Pittsburgh, PA · On-site +1

As a Staff Machine Learning Engineer, you will serve as a technical leader defining the roadmap and architecture for the machine learning systems that power our data discovery and model improvement ...

Motional

Staff Machine Learning Engineer

Pittsburgh, PA · On-site +1

As a Staff Machine Learning Engineer, you will serve as a technical leader defining the roadmap and architecture for the machine learning systems that power our data discovery and model improvement ...

Motional

Staff Machine Learning Engineer

Pittsburgh, PA · On-site

As a Staff Machine Learning Engineer, you will serve as a technical leader defining the roadmap and architecture for the machine learning systems that power our data discovery and model improvement ...

Cmu

Machine Learning Engineer - Autonomy Lab

Pittsburgh, PA

$99K - $131K/yr

As a machine learning engineer in the AI for Autonomy Lab, you willidentify, shape, apply, conduct, and lead engineering research that matches critical U.S. government needs. The AI for Autonomy Lab ...

Govini

Senior Machine Learning Engineer

Pittsburgh, PA · On-site

$114K - $150K/yr

Senior Machine Learning Engineer Pittsburgh, Pennsylvania, United States Company Description Govini transforms Defense Acquisition from an outdated manual process to a software-driven strategic ...

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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 Pennsylvania? For Machine Learning Engineer Quantization jobs in Pennsylvania, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Quantization jobs in Pennsylvania look for? The top searched job categories for Machine Learning Engineer Quantization jobs in Pennsylvania are:
What cities in Pennsylvania are hiring for Machine Learning Engineer Quantization jobs? Cities in Pennsylvania with the most Machine Learning Engineer Quantization job openings:
Machine Learning Engineer Quantization jobs near you
Machine Learning Engineer, Specialist

Machine Learning Engineer, Specialist

Vangard, Inc.

Malvern, PA

Full-time

Posted 18 days ago

Job description

Supports and performs the development and programming of machine learning integrated software algorithms to structure, analyze, and leverage data in a production environment.

Core Responsibilities

  • Leverages data pipeline designs and supports the development of data pipelines to support model development. Proficient with software tools that develop data pipelines in a distributed computing environment (PySprak, GlueETL).

  • Supports integration of model pipelines in a production environment. Develops understanding of SDLC for model production.

  • Reviews pipeline designs, makes data model design changes as needed. Documents and reviews design changes with data science teams.

  • Supports data discovery & automated ingestion for model development. Performs detailed analysis of raw data sources for data quality, applies business context, and model development needs.

  • Engages with internal stakeholders to understand and probe business processes in order to develop hypotheses. Brings structure to requests and translates requirements into an analytic approach. Participates in and influences ongoing business planning and departmental prioritization activities.

  • Runs model monitoring scripts, follows process for alerts to management as needed. Addresses issues found in data pipelines from model monitoring alerts.

  • Participates in special projects and performs other duties as assigned.

Qualifications

  • Undergraduate degree or equivalent experience; a graduate degree is preferred.

  • Minimum of 5 years of relevant work experience.

  • At least 3 years of hands-on experience designing ETL pipelines using AWS services (e.g., Glue, SageMaker).

  • Proficiency in programming languages, particularly Python (including PySpark, PySQL) and familiarity with machine learning libraries and frameworks.

  • Strong understanding of cloud technologies, including AWS and Azure, and experience with NoSQL databases.

  • Familiarity with Feature Store usage, LLMs, GenAI, RAG, Prompt Engineering, and Model Evaluation.

  • Experience with API design and development is a plus.

  • Solid understanding of software engineering principles, including design patterns, testing, security, and version control.

  • Knowledge of Machine Learning Development Lifecycle (MDLC) best practices and protocols.

  • Understanding of solution architecture for building end-to-end machine learning data pipelines.

Special Factors

Sponsorship

Vanguard is not offering visa sponsorship for this position.

About Vanguard

At Vanguard, we don't just have a mission-we're on a mission.

To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.

How We Work

Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.

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