Principal Machine Learning Engineer to serve as a hands-on technical leader for machine learning, predictive modeling, scoring, decisioning, and applied AI initiatives. This role will primarily focus on building, validat…
Engineering & IT · Philadelphia, PA
Machine Learning Engineer jobs in Philadelphia, PA
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
140
Open roles today
$47–$119/hr
Typical pay range
$159k
Median, full-time
10
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Open machine learning engineer roles
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We are looking for an Integration Services Engineer to support the design, stability, and ongoing improvement of enterprise data integration solutions in Fort Washington, Pennsylvania. This Long-term Contract position is…
$71.3k - $140.6k
Position Summary Our Deloitte AI & Engineering team works to transform technology platforms, drive innovation, and help make... ...request handlingExperience with DockerExperience supporting machine learning workflowsExp…
our ability to change the world for the better. Read further to learn how you could help make great things possible not only in your... ...primary duties of the Overhead Contact System Electrification Engineer include de…
$94.4k - $141.6k
Switching, Digital Power, Automation, Services and Power Monitoring engineered- to-order equipment used to distribute, monitor and control a... ..., global opportunities, the Schneider Career Hub, and learning platforms…
difference in our ability to change the world for the better. Read further to learn how you could help make great things possible not only in your community, but around the world. In the role of Civil Engineer, we'll cou…
subsystems that is part of a high-performance semiconductor assembly machine under development. He / She will take a leading role in... ...Experience requirements• Minimum 5 years of precision mechanical engineering desi…
$95k - $125k
DescriptionKforce has a client in Philadelphia, PA that is seeking a Senior Integration Engineer - Boomi to design, develop, and support enterprise integration solutions that enable secure, scalable data exchange across…
our ability to change the world for the better. Read further to learn how you could help make great things possible not only in your... ...opportunities? Our growing aviation practice is searching for Aviation Engineers,…
$215.1k - $384.4k
operational, financial, and experience outcomes.The Forward Deployed Engineer is a product-minded, hands-on engineer who works with strategic... ...role. It is a product-embedded engineering role that learns from real cu…
We are seeking a Senior Data Engineer to design, develop, and maintain scalable data pipelines and data ingestion processes using modern Big Data technologies. The ideal candidate will have strong experience in data aggr…
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What machine learning engineers earn in Philadelphia
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $47–$65 | $97k–$135k |
| Mid level | $65–$88 | $135k–$184k |
| Senior | $86–$119 | $178k–$248k |
Adjusted for the Philadelphia market from national ranges.
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What employers ask for
The skills these listings keep naming
Python and software engineeringPyTorch or TensorFlowML fundamentals and evaluationModel serving and APIsMLOps (tracking, registries, CI)Docker and KubernetesData pipelines and feature storesLLM fine-tuning and RAG (a plus)Monitoring and drift detection
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Interview questions worth rehearsing
With the thing the interviewer is actually listening for
Walk me through taking a model from prototype to production.
Cover data pipelines, training reproducibility, serving, and monitoring. Emphasize that the model is a small part of the system — that framing is the job.
How do you monitor a model in production?
Discuss input drift, prediction distributions, delayed labels, and business metrics — plus what triggers retraining. Mention that silent degradation is the default failure mode.
How would you reduce inference latency or cost for a large model?
Options include distillation, quantization, batching, caching, and smaller models. Frame it as measuring first, then choosing the cheapest acceptable quality tradeoff.
Tell me about a time a model failed in production. What happened?
A real story about skew, drift, or a data bug — with detection and prevention — is far more convincing than claiming smooth deployments.
How do you evaluate a model beyond accuracy?
Talk about the metric matching the business cost of errors, slicing by segment, and offline-online gaps. Naming a case where accuracy misled is a strong touch.
When would you fine-tune an LLM versus use retrieval or prompting?
Start cheap: prompting, then RAG for knowledge, fine-tuning for behavior and format. Cost and maintenance burden should drive the answer.
How do you make training reproducible?
Version code, data, and config; track experiments; pin environments. This is engineering discipline applied to ML, which is exactly the role.
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Resume tips that move the needle
For machine learning engineers specifically — generic advice costs you here
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Center bullets on production systems: models served, request volume, latency, and the business metric they moved.
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Show software engineering credentials explicitly — testing, CI, code review — since MLE hiring filters hardest on engineering rigor.
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Name the MLOps tooling you have run (MLflow, SageMaker, Kubeflow, vector databases) as these are common screens.
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Include LLM work with specifics — fine-tuning, RAG, evaluation — if you have it; vague 'GenAI experience' claims read poorly.
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Distinguish your role on shared projects: built the serving layer, owned the pipeline, or trained the model.
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Where this role goes
Typical progression
01
ML Engineer
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Senior ML Engineer
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Staff ML Engineer
04
ML Platform Lead
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