Citizen/Permanent Resident US/GC Holder Job Title: Marine Engine Field Service Engineer/Technician Location: Miami, NM... ..., instruct others in proper use of all tooling, equipment and machines after properly certified…
Engineering & IT · Miami, FL
Machine Learning Engineer jobs in Miami, FL
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
126
Open roles today
$48–$122/hr
Typical pay range
$162k
Median, full-time
3
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Open machine learning engineer roles
12 shown of 126 · sorted by freshness
A pioneering AI Startup is seeking an experienced Back-End Engineer to design and build intelligent systems. The role is fully remote and suitable for those with 7+ years in complex SaaS environments. Ideal candidates ar…
$27 - $32 per hour
MedTech is seeking Mechanical Field Service Engineers to support a nationwide medical device... ...hydraulic assemblies on dialysis machines. Complete required rinse, flush, testing... ...outcomes and population health w…
Class of Q1' 2026 supporting the Data Center Critical Facilities Engineer. The training will be on the cutting-edge of technology in a... ..., or HVAC and skilled Mechanical trades? Or desire to learn a new skill or trad…
views are made. We’re also building the learning loops for when views resolve against reality... ...We're a team of ~25 senior-to-principal engineers, designers and AI/ML researchers. More... ...with AI isn't a tradition…
$60k - $75k
annual merit raises, and a 5% salary increase after six months. Learn, advance, and build a career that can take you in multiple... ...like food and beverage. Partner with automation and controls engineers to test, troub…
with the PM and field team. Qualifications: ~3-5 years in the Construction Industry ~ Degree in Construction Management, Engineering, or related field preferred. ~ OSHA 10/30 Certification prefered ~ Proficiency in Micro…
$120k - $150k
Job Description Job Description AI/ML Engineer Experience: 4–7 Years Job Type: Full-Time Location: Remote / Hybrid /... ...for a skilled AI/ML Engineer to design, develop, and deploy machine learning and generative AI so…
Strong understanding of inference, latency, scaling, monitoring, and reliability ~ Strong ML background overall (ML Scientist / ML Engineer trajectory) ~ Strong coding and engineering skills Develop and improve / Voice G…
Data Scientist to lead the development of advanced analytics, machine learning models, and data products that deliver measurable business... ...clients. You will work cross-functionally with product, engineering, marketi…
actionable intelligence. You'll work across operations, engineering, and leadership to build predictive systems that optimize... ...ETA prediction, etc.). They will be well versed in AI & Machine Learning. Having Hands-O…
Hospitality America is looking for a Maintenance Engineer to join our team! The Maintenance Engineer is responsible for the maintenance of the hotel’s building and grounds and the operation of its equipment and mechanica…
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What machine learning engineers earn in Miami
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $48–$66 | $99k–$138k |
| Mid level | $66–$90 | $138k–$187k |
| Senior | $88–$122 | $182k–$253k |
Adjusted for the Miami 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
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.
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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
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ML Platform Lead
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