ZipRecruiter

Log In

1

Machine Learning Engineer Quantization Jobs in Pennsylvania

Instacart

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

Instacart

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

Instacart

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

Instacart

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

Air

Senior Machine Learning Engineer

Pittsburgh, PA

$118K - $156K/yr

... engineers Qualifications * U.S. Citizenship is required * Advanced degree, or bachelor's with at least 3 years of experience, in Data Science, Machine Learning or a related field Required Skills:

Air

Senior Machine Learning Engineer

Pittsburgh, PA · On-site

$118K - $156K/yr

... engineers Qualifications * U.S. Citizenship is required * Advanced degree, or bachelor's with at least 3 years of experience, in Data Science, Machine Learning or a related field Required Skills:

Air

Senior Machine Learning Engineer

Pittsburgh, PA · On-site

$118K - $156K/yr

... engineers Qualifications * U.S. Citizenship is required * Advanced degree, or bachelor's with at least 3 years of experience, in Data Science, Machine Learning or a related field Required Skills:

previous page

Showing results 41-60

All JobsMachine Learning Engineer Quantization JobsMachine Learning Engineer Quantization Jobs in Pennsylvania

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
Infographic showing various Machine Learning Engineer Quantization job openings in Pennsylvania as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Principal Data & Machine Learning Engineer

AKUVO LLC

Malvern, PA • On-site

$158K - $216K/yr

Full-time

Posted 22 days ago

Job description

THE OPPORTUNITY

AKUVO is seeking a Principal Data & Machine Learning Engineer to serve as the senior-most technical owner across AKUVO’s data platform, machine-learning models, and the services behind AKUVO IQ. This is a breadth role: you are equally at home building production applications and APIs, engineering the data lake and infrastructure, and developing and deploying predictive models — the person the team turns to at any layer.

You will lead the technical execution of the data and analytics strategy, own architecture across data engineering and machine learning, internalize critical systems currently held by external partners, and provide technical leadership and mentorship to the engineering team. The role combines hands-on engineering across the full stack with technical leadership and direct ownership of production systems.

LOCATION

Local in Malvern/Philadelphia first, widening to surrounding areas such as New Jersey, New York, Delaware, while continuing to expand geographically in a hybrid/remote capacity based on location.

KEY RESPONSIBILITIES

  • Lead the technical execution of the data and analytics strategy across data engineering and machine learning, and own the architecture for AKUVO’s data lake, ML platform, model pipelines, and the data services behind AKUVO IQ.
  • Work hands-on across the full stack — application and API development, systems and infrastructure, data pipelines, and predictive-model development — stepping directly into whichever layer the team needs.
  • Build, deploy, and maintain predictive models and scores alongside the Senior Data & Machine Learning Engineer, contributing directly to model development as well as the platform beneath it.
  • Internalize critical data and ML systems currently held by external partners through a structured knowledge-transfer and documentation process, building internal depth and reducing concentration risk.
  • Design scalable, reliable, and secure architectures for structured portfolio data, predictive-model data, and separately governed PII and AI-conversation data.
  • Own the operational disciplines for pipelines and production models — monitoring, alerting, incident response, versioning, drift detection, and retraining — so systems can be independently deployed, monitored, and enhanced.
  • Evolve technical practices for architecture, development, testing, CI/CD, observability, documentation, and data quality, and ensure data is accurate, timely, and traceable with clear lineage and governance.
  • Provide technical leadership, mentorship, and development to the engineering team, set technical direction, and coordinate delivery.
  • Partner with Applied AI, the Collections domain, Product, Engineering, Architecture & Innovation, and Compliance to keep data, models, and AI systems integrated, governed, and production-ready.
  • Evaluate technical investments, cost, and resource needs; make pragmatic build-versus-buy decisions; and document and prioritize key risks, dependencies, and technical debt.
  • Communicate architecture, risks, and priorities clearly to executive and cross-functional stakeholders, and advance AI-assisted engineering practices across the team.

SKILLS AND EXPERIENCE

  • 10+ years across software/data engineering and machine learning, with hands-on delivery spanning application development, systems and infrastructure, data platforms, and production ML models.
  • 3+ years providing technical leadership and developing engineers.
  • Full-stack breadth — able to build applications and APIs, engineer data pipelines and infrastructure, and develop, deploy, and maintain ML models; the person the team relies on at any layer.
  • Deep, hands-on experience with cloud data and ML platforms in production (Azure strongly preferred) — data lakes, layered architectures, pipelines, product-serving APIs, and model pipelines.
  • Strong Python and SQL, and modern engineering practices (ETL/ELT, CI/CD, observability, testing, environment management).
  • A track record of internalizing critical systems and knowledge through structured transitions, and of setting and evolving technical practices.
  • Ownership of the production model lifecycle — deployment, versioning, monitoring, drift detection, and retraining.
  • Proven ability to translate business and product priorities into scalable roadmaps and pragmatic build-versus-buy decisions.
  • Strong communication with executive, product, and cross-functional stakeholders, and comfort operating as a hands-on technical leader.
  • Active, sophisticated use of AI within your own engineering and leadership workflow.

PREFERRED QUALIFICATIONS

  • Experience spanning both software/platform engineering and applied ML in the same role — a rare full-stack-plus-modeling breadth.
  • Microsoft Fabric and OneLake, or experience leading a Synapse-to-Fabric migration; Databricks or comparable ML platforms.
  • B2B SaaS, fintech, or financial-services background (2+ years), ideally with collections, lending, or credit-scoring exposure.
  • Experience standing up or maturing model governance, documentation, and compliance practices.
  • Experience with sensitive, PII, or regulated data and separately governed data zones.
  • Azure DevOps and structured delivery processes (Epics → Features → Stories → Tasks).