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Machine Learning Biomedical Engineer Jobs in Philadelphia, PA

Merican

AI / Machine Learning Engineer

PA · On-site

AI / Machine Learning Engineer (Contract) Location: Philadelphia, PA or Charlotte, NC Duration: 6 Months Contract Job Summary We are seeking an experienced AI / Machine Learning Engineer to design ...

Bespoke Labs

Machine Learning Engineer

Trenton, NJ · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Wilmington, DE · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

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All JobsMachine Learning Biomedical Engineer JobsMachine Learning Biomedical Engineer Jobs in Philadelphia, PA

Machine Learning Biomedical Engineer information

See Philadelphia, PA salary details

$31.8K

$129.9K

$195.3K

How much do machine learning biomedical engineer jobs pay per year?

As of Jul 18, 2026, the average yearly pay for machine learning biomedical engineer in Philadelphia, PA is $129,939.00, according to ZipRecruiter salary data. Most workers in this role earn between $102,400.00 and $156,400.00 per year, depending on experience, location, and employer.

What is the difference between Machine Learning Biomedical Engineer vs Data Scientist in Biomedical Industry?

AspectMachine Learning Biomedical EngineerData Scientist in Biomedical Industry
Required CredentialsDegree in Biomedical Engineering, Computer Science, or related fields; knowledge of machine learning and biomedical dataDegree in Data Science, Statistics, or related fields; proficiency in data analysis and machine learning
Work EnvironmentResearch labs, healthcare institutions, biotech companiesHealthcare analytics firms, research institutions, biotech companies
Employer & Industry UsageDevelops algorithms for medical devices, diagnostics, and treatment planningAnalyzes biomedical data to inform clinical decisions, research, and product development

Both roles require expertise in machine learning and biomedical data, but Machine Learning Biomedical Engineers focus on developing algorithms for medical applications, while Data Scientists analyze biomedical data to support research and clinical decisions.

What does a Machine Learning Biomedical Engineer do?

A Machine Learning Biomedical Engineer applies machine learning techniques to solve problems in biology and medicine. They develop algorithms and models to analyze complex biomedical data, such as medical images, genetic information, or sensor readings. Their work supports advancements in diagnostics, treatment planning, and personalized medicine. Typically, they collaborate with clinicians, researchers, and other engineers to design systems that improve healthcare outcomes.

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

To thrive as a Machine Learning Biomedical Engineer, you need a strong background in biomedical engineering, data analysis, and machine learning, typically supported by a degree in biomedical engineering, computer science, or a related field. Familiarity with programming languages like Python or R, machine learning frameworks (e.g., TensorFlow, PyTorch), and experience with medical imaging or signal processing tools are commonly required. Critical thinking, problem-solving, and the ability to communicate complex technical concepts to interdisciplinary teams are vital soft skills. These abilities are crucial for developing innovative healthcare solutions, ensuring regulatory compliance, and bridging the gap between technology and medicine.

How does a Machine Learning Biomedical Engineer typically collaborate with clinicians and researchers in a healthcare setting?

Machine Learning Biomedical Engineers often work closely with clinicians and researchers to develop algorithms that solve real-world medical challenges. Collaboration usually involves understanding clinical needs, translating them into technical requirements, and iteratively refining models based on feedback from medical experts. Regular meetings, interdisciplinary project teams, and direct participation in data collection or validation studies are common. This collaborative environment ensures that technical solutions are both innovative and clinically relevant, making communication and adaptability essential skills.
What are popular job titles related to Machine Learning Biomedical Engineer jobs in Philadelphia, PA? For Machine Learning Biomedical Engineer jobs in Philadelphia, PA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Biomedical Engineer jobs in Philadelphia, PA look for? The top searched job categories for Machine Learning Biomedical Engineer jobs in Philadelphia, PA are:
What cities near Philadelphia, PA are hiring for Machine Learning Biomedical Engineer jobs? Cities near Philadelphia, PA with the most Machine Learning Biomedical Engineer job openings:
Machine Learning Biomedical Engineer jobs near you
Infographic showing various Machine Learning Biomedical Engineer job openings in Philadelphia, PA as of July 2026, with employment types broken down into 2% Internship, 1% As Needed, 73% Full Time, 23% Part Time, and 1% Contract. Highlights an 84% Physical, 6% Hybrid, and 10% Remote job distribution, with an average salary of $129,939 per year, or $62.5 per hour.
Machine Learning Engineer, Specialist

Machine Learning Engineer, Specialist

Vanguard Group

Malvern, PA • On-site

Full-time

Re-posted 21 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

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