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Trainee Machine Learning Engineer Jobs in Pennsylvania

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 ...

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Trainee Machine Learning Engineer information

What is a $900000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers or AI research directors, often requiring advanced skills, extensive experience, and expertise in areas like deep learning, data science, and programming with tools like Python and TensorFlow. These positions usually involve leadership responsibilities, strategic planning, and significant contributions to AI development projects.

What engineers make $500,000?

Senior engineers in fields such as software, data engineering, and specialized roles like machine learning engineers can earn $500,000 or more annually, especially with extensive experience, advanced skills, and in high-demand industries. Compensation often includes base salary, bonuses, and stock options, particularly at large tech companies or startups with significant funding.

Can I get an AI job with no experience?

Entering a trainee machine learning engineer role typically requires some foundational knowledge of programming, statistics, and machine learning concepts. While prior experience is not always mandatory, demonstrating relevant skills through projects, certifications, or coursework can improve your chances of securing such a position.

Can I learn ML in 3 months?

A Trainee Machine Learning Engineer can acquire foundational knowledge in three months by focusing on core concepts such as algorithms, programming in Python, and data handling. However, mastering advanced topics and gaining practical experience typically requires longer, ongoing learning and project work.

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

AspectTrainee Machine Learning EngineerJunior Data Scientist
Required CredentialsBasic programming, introductory ML knowledge, possibly a degree in CS or related fieldDegree in Data Science, Statistics, or related field; some programming experience
Work EnvironmentInternship or entry-level role in tech or AI companies, labs, or startupsEntry-level position in data teams across various industries
Employer & Industry UsageTech companies, AI startups, research labsFinance, healthcare, e-commerce, and tech firms

While both roles are entry-level and involve working with data, a Trainee Machine Learning Engineer focuses more on developing and deploying machine learning models, whereas a Junior Data Scientist emphasizes data analysis, visualization, and insights. The roles often overlap, but the Trainee ML Engineer is more specialized in ML algorithms and model deployment.

What are the most commonly searched types of Machine Learning Engineer jobs in Pennsylvania? The most popular types of Machine Learning Engineer jobs in Pennsylvania are:
What are popular job titles related to Trainee Machine Learning Engineer jobs in Pennsylvania? For Trainee Machine Learning Engineer jobs in Pennsylvania, the most frequently searched job titles are:
What job categories do people searching Trainee Machine Learning Engineer jobs in Pennsylvania look for? The top searched job categories for Trainee Machine Learning Engineer jobs in Pennsylvania are:
What cities in Pennsylvania are hiring for Trainee Machine Learning Engineer jobs? Cities in Pennsylvania with the most Trainee Machine Learning Engineer job openings:
Machine Learning Engineer, Specialist

Machine Learning Engineer, Specialist

Vanguard Group

Malvern, PA • On-site

Full-time

Re-posted 16 days ago


Vanguard rating

8.7

Company rating: 8.7 out of 10

Based on 62 frontline employees who took The Breakroom Quiz

17th of 148 rated financial services


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.

What Vanguard employees say

Pay

Benefits

Hours and flexibility

Workplace

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