$220k - $300k
Job Description Job Description Senior AI/ML Engineer Title: Senior AI/ML Engineer Reports to: VP of Engineering, Operations Location: Remote (MUST BE LOCATED IN CT/ET) Primary stack: Python, Kotlin, TypeScript, AWS AI f…
Engineering & IT · Pittsburgh, PA
Machine Learning Engineer jobs in Pittsburgh, PA
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
254
Open roles today
$42–$107/hr
Typical pay range
$143k
Median, full-time
2
Fresh in this list
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01
Open machine learning engineer roles
12 shown of 254 · sorted by freshness
and replace cars. Could you be the full-time onsite PI Field Engineer in Pittsburgh, PA, US we’re looking for? Your future role... ...from our investment in your development, through award-winning learning, Progress towa…
$79k - $158k
provide a work home for the elite in our industry. As a Master Field Engineer, you are the ultimate technical authority and carry full... ...Dependent Care Spending Account Tuition Assistance To learn more about our bene…
industry-leading efficiencies, our best-in-class team leverages methodologies, governance and systems that are unparalleled in the engineering space. Are you looking for an opportunity to join a diverse group of professi…
Software Engineer (Python) - Local to Pittsburgh, Dallas, or Cleveland Position Description This role is located in our client office... ...vacation, holidays, and sick time . Paid parental leave . Learning opportunities…
Job Description Job Description Position Title: Sr Machine Learning Engineer Location: PA - Pittsburgh, TX - Dallas, OH - Cleveland Work Status : Onsite 5 days a week Duration : Contract to Hire Years Of Experience Requi…
benefits and an opportunity to work on cutting edge technology in a dynamic team! Acutronic is looking for a Junior Service Engineer who will be responsible for troubleshooting, testing, inspection, installation and repa…
Processing expert to join our team and help us build cutting-edge machine learning technology that will replace complex, time-consuming, manual... ...Collaborate closely with fellow taxonomists, software engineers, data…
$141k - $249k
innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: You will... - Collaborate closely with autonomy and algorithm engineers to scale safe self-driving systems usin…
$45 - $48 per hour
Job Title: Product Introduction Field Modification Engineer Location: West Mifflin, PA Employment Type: 6-12 month contract with opportunity for permanent hire Industry: Transportation Manufacturing Compensation: $45-$48…
Ffh4Plrh7-9iVzzEsUNLvToh65WXvGOVb5uuUdPlXt_c1GT1mdJeeqNxp4IEJo5Pu6 dDwtPgjQgMrsQ$). Scope of Position: As an Associate Field Engineer, you will be crucial to ALC’s business through the field commissioning of building aut…
$35 - $50 per hour
Seeking a hands-on Field Service Engineer with strong mechanical and electrical aptitude. The ideal candidate will have solid experience... ...Conduct preventive maintenance and corrective repairs on CNC machines and ind…
02
What machine learning engineers earn in Pittsburgh
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $42–$58 | $87k–$121k |
| Mid level | $58–$79 | $121k–$165k |
| Senior | $77–$107 | $160k–$223k |
Adjusted for the Pittsburgh market from national ranges.
03
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
04
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.
05
Resume tips that move the needle
For machine learning engineers specifically — generic advice costs you here
01
Center bullets on production systems: models served, request volume, latency, and the business metric they moved.
02
Show software engineering credentials explicitly — testing, CI, code review — since MLE hiring filters hardest on engineering rigor.
03
Name the MLOps tooling you have run (MLflow, SageMaker, Kubeflow, vector databases) as these are common screens.
04
Include LLM work with specifics — fine-tuning, RAG, evaluation — if you have it; vague 'GenAI experience' claims read poorly.
05
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
02
Senior ML Engineer
03
Staff ML Engineer
04
ML Platform Lead
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