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Remote Machine Learning Jobs in Philadelphia, PA

Junior Data Engineer (Philadelphia, PA)

Philadelphia, PA ยท On-site +1

$62K - $75K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

You will absorb data engineering work currently split between the Senior Machine Learning Engineer ... A remote-first workplace * A flexible work environment with the ability to plan your work week ...

Translate engineering requirements into structured CAD data suitable for AI learning and validation ... CNC machining. * Casting and forging. * Assembly modeling. * CAD editing and feature tree ...

Adjunct Faculty- Data Science

PA ยท On-site +1

  • Medical

  • Life

  • Retirement

  • PTO

Adjunct Faculty Remote Employment: Remote Optional Job Number: 515 Department: Data Science ... Machine learning * Data visualization * Data manipulation Qualifications Preferred applicants will ...

Principal, Data Engineering (Remote)

Philadelphia, PA ยท On-site +1

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Knowledge of machine learning operations (MLOps) and model deployment. * Strong problem-solving and analytical abilities. * Excellent communication skills for collaborating with stakeholders.

Showing results 41-60

Remote Machine Learning information

See Philadelphia, PA salary details

$25.7K

$43K

$88.8K

How much do remote machine learning jobs pay per year?

As of Aug 19, 2026, the average yearly pay for remote machine learning in Philadelphia, PA is $42,971.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,800.00 and $46,400.00 per year, depending on experience, location, and employer.

What is a remote machine learning job?

A remote machine learning job involves working with algorithms, data, and models to develop predictive systems or automate tasks, all while working from a location outside of a traditional office setting. Professionals in this role use techniques from statistics and computer science to analyze data, train machine learning models, and deploy solutions for real-world applications. Remote machine learning jobs can span various industries, including technology, healthcare, finance, and e-commerce. These roles typically require strong programming skills, knowledge of machine learning frameworks, and the ability to communicate findings effectively with team members or stakeholders. Working remotely offers flexibility, but also requires discipline and self-motivation to succeed.

What are some effective strategies for collaborating with team members while working remotely as a machine learning engineer?

Collaboration in a remote Machine Learning role often relies on clear communication through digital tools such as Slack, Zoom, and project management platforms like Jira or Asana. Regular check-ins and stand-up meetings help keep everyone aligned on project goals and timelines. Sharing code and models via version control systems (like Git) and using collaborative notebooks (such as JupyterHub or Google Colab) are also common practices. Building strong documentation habits and proactively seeking feedback can help ensure smooth teamwork and project success, even across different time zones.

What is the difference between Remote Machine Learning vs Data Scientist?

AspectRemote Machine LearningData Scientist
Required CredentialsBachelor's/Master's in CS, ML certificationsBachelor's/Master's in CS, Statistics, or related field
Work EnvironmentRemote, collaborative teams, tech companiesRemote or on-site, diverse industries, analytics focus
Industry UsageTech, AI startups, researchFinance, healthcare, e-commerce, tech
Search & Comparison IntentOften compared for technical roles in AI/MLBroader data analysis roles, but overlapping skills

Remote Machine Learning specialists focus on developing algorithms and models primarily in tech environments, often requiring advanced programming and ML knowledge. Data Scientists analyze data to extract insights, sometimes utilizing ML techniques. While both roles share skills and credentials, Remote Machine Learning emphasizes model development, whereas Data Scientists focus on data analysis and interpretation.

What are the most commonly searched types of Machine Learning jobs in Philadelphia, PA?

The most popular types of Machine Learning jobs in Philadelphia, PA are:

What are popular job titles related to Remote Machine Learning jobs in Philadelphia, PA?

For Remote Machine Learning jobs in Philadelphia, PA, the most frequently searched job titles are:

What job categories do people searching Remote Machine Learning jobs in Philadelphia, PA look for?

The top searched job categories for Remote Machine Learning jobs in Philadelphia, PA are:

What cities near Philadelphia, PA are hiring for Remote Machine Learning jobs?

Cities near Philadelphia, PA with the most Remote Machine Learning job openings:

Infographic showing various Remote Machine Learning job openings in Philadelphia, PA as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 22% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $42,971 per year, or $20.7 per hour.

Principal Data Engineer - Safety Analytics (Global Medical Safety)

Jj

Titusville, NJ โ€ข On-site, Remote

Full-time

Retirement, PTO

Re-posted 15 days ago


Job description

At Johnson & Johnson,we believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented, treated, and cured,where treatments are smarter and less invasive, andsolutions are personal.Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity.Learn more at jnj.com

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Job Function:

Data Analytics & Computational Sciences

Job Sub Function:

Data Engineering

Job Category:

Scientific/Technology

All Job Posting Locations:

Horsham, Pennsylvania, United States of America, Titusville, New Jersey, United States of America

Job Description:

About Innovative Medicine

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.
Learn more at https://www.jnj.com/innovative-medicine

Prefered Location:

Horsham, PA or Titusville, NJ. Remote work will considered on a case by case basis.

Role Overview

We are seeking a Principal Data Engineer to provide technical leadership within Global Medical Safety (GMS), supporting the Safety Analytics organization. This role is focused on building and enabling modern safety analytics tools using AI, Machine Learning, and GenAI, underpinned by robust, compliant, and scalable data engineering on Google Cloud Platform (GCP).

The Principal Data Engineer is responsible for end-to-end ownership of safety analytics data engineering, spanning data intake, data quality and continuity, pipeline and architecture design, automation, performance optimization, and compliance. The role enables advanced analytical, machine learning, and predictive capabilities for pharmacovigilance and serves as a technical data engineering leader within Global Medical Safety.

This is a Principal-level individual contributor role with broad technical influence, working closely with safety scientists, analytics teams, data scientists, IT, and platform partners to deliver trusted, production-grade analytics capabilities for safety decision-making.

Key Responsibilities

Safety Analytics & Pharmacovigilance Enablement

  • Design and maintain production-grade data pipelines and curated datasets that directly support pharmacovigilance activities, including safety monitoring, analytics, and regulatory reporting.

  • Ensure data engineering solutions produce reproducible, explainable, and trusted analytics outputs suitable for safety decision support and inspection readiness.

  • Enable AI/ML and GenAI workflows for safety analytics, including:

  • Feature engineering and feature store enablement
  • Embeddings, vectorized representations, and semantic retrieval
  • Retrieval-Augmented Generation (RAG) patterns for safety analytics tools

End-to-End Data Architecture & Lifecycle Ownership

  • Own the end-to-end data lifecycle for safety analytics, from source system intake through transformation, serving, and downstream analytical consumption, ensuring data continuity, traceability, and integrity.

  • Lead architectural decisions across ingestion, transformation, storage, and serving layers on GCP (e.g., BigQuery, Dataform, object storage).

  • Design, implement, and automate scalable, reusable data pipelines and architectures to support evolving safety analytics needs.

Data Quality, Governance & Compliance

  • Establish and enforce data quality, validation, lineage, and observability standards for safety analytics datasets.

  • Define and implement data governance practices, including data contracts, schema versioning, access control, stewardship, and lifecycle management.

  • Ensure safety analytics data and systems meet Global Medical Safety requirements for reliability, auditability, and regulatory use.

GxP Validation & Regulatory Readiness

  • Apply GxP validation expertise to data pipelines, analytics services, and supporting infrastructure.

  • Partner with quality and compliance teams to implement CSV/CSA-aligned controls, audit trails, documentation, and organizational change.

  • Balance delivery velocity and innovation with the rigor required for regulated pharmacovigilance systems.

Services, APIs & Microservices

  • Design and build APIs and microservices-based architectures to operationalize safety analytics and ML capabilities (e.g., feature serving, retrieval services, analytics backends).

  • Deploy and operate services on GCP (e.g., Cloud Run, GKE) with a strong focus on security, scalability, and observability.

  • Enforce contract-first integration patterns between producing and consuming systems to ensure reliability and safe evolution.

Infrastructure, CI/CD & Cost Optimization

  • Provision and manage cloud infrastructure using Terraform (Infrastructure as Code) on GCP.

  • Build and maintain CI/CD pipelines (e.g., Jenkins) for data pipelines, analytics services, feature pipelines, and ML data assets.

  • Continuously optimize the performance and cost efficiency of data and analytics infrastructure while maintaining compliance and reliability standards.

Technical Leadership & Stakeholder Engagement

  • Serve as a technical authority and data engineering leader for Safety Analytics within Global Medical Safety.

  • Review and influence designs across pipelines, services, feature stores, and AI/ML integrations to maintain a high technical bar. Collaborate closely with safety scientists, epidemiologists, biostatisticians, analytics teams, IT, and platform partners to translate safety needs into scalable technical solutions.

  • Communicate complex technical concepts and tradeoffs clearly to both technical and non-technical stakeholders.

  • Enable and upskill teams through mentorship, guidance, and knowledge sharing on modern data, cloud, and AI technologies.

Qualifications

  • Master's degree in Computer Science, Engineering, or a related field (or equivalent experience) is required.

  • 5+ years of experience in data engineering or analytics engineering with increasing responsibilities.

  • Proficient programming skills in Python and SQL.

  • Deep understanding of data architecture for analytics and ML (e.g., batch/streaming, modeling, performance optimization).

  • Proven ability to translate complex problems into clear, concise, and testable programming code/tools.

  • Experience implementing data contracts, data validation, schema versioning, and governance practices, as well as a solid understanding of leading cloud concepts (GCP preferred).

  • Experience designing and operating APIs and microservices-based architectures.

  • Excellent written and verbal communication, customer service, interpersonal, and teamwork skills to foster a collaborative team environment.

  • Solid understanding of SDLC and Agile methodologies, alongside basic project management skills.

  • Experience building production workloads on Google Cloud Platform (GCP) is preferred.

  • Experience provisioning infrastructure using Terraform (Infrastructure as Code) and building CI/CD pipelines (e.g., Jenkins) is preferred.

  • Experience in pharmaceuticals, life sciences, healthcare, or a related regulated domain is preferred.

  • GCP certification is preferred.

  • Experience enabling AI/ML and GenAI workflows (e.g., feature engineering, RAG patterns, semantic retrieval) for analytical applications is preferred.

Johnson & Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by federal, state or local law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.


Johnson & Johnson is committed to providing an interview process that is inclusive of our applicants' needs. If you are an individual with a disability and would like to request an accommodation, external applicants please
contact us via https://www.jnj.com/contact-us/careers , internal employees contact AskGS to be directed to your
accommodation resource.

#JNJTech

#LI-Hybrid

#LI-GR1

Required Skills:

Preferred Skills:

Advanced Analytics, Agility Jumps, Coaching, Critical Thinking, Data Engineering, Data Governance, Data Modeling, Data Privacy Standards, Data Science, Digital Fluency, Execution Focus, Hybrid Clouds, Organizing, Presentation Design, Technical Development, Technical Writing, Technologically Savvy

The anticipated base pay range for this position is :

$102,000.00 - $177,100.00

Additional Description for Pay Transparency:

Subject to the terms of their respective plans, employees are eligible to participate in the Company's
consolidated retirement plan (pension) and savings plan (401(k)).
Subject to the terms of their respective policies and date of hire, employees are eligible for the following time off benefits:
Vacation -120 hours per calendar year
Sick time - 40 hours per calendar year; for employees who reside in the State of Colorado -48 hours per calendar year; for employees who reside in the State of Washington -56 hours per calendar year
Holiday pay, including Floating Holidays -13 days per calendar year
Work, Personal and Family Time - up to 40 hours per calendar year
Parental Leave - 480 hours within one year of the birth/adoption/foster care of a child
Bereavement Leave - 240 hours for an immediate family member: 40 hours for an extended family member per calendar year
Caregiver Leave - 80 hours in a 52-week rolling period10 days
Volunteer Leave - 32 hours per calendar year
Military Spouse Time-Off - 80 hours per calendar year
For additional general information on Company benefits, please go to: - https://www.careers.jnj.com/employee-benefits