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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.
119
Open roles today
$48–$122/hr
Typical pay range
$162k
Median, full-time
10
Fresh in this list
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01
Open machine learning engineer roles
12 shown of 119 · sorted by freshness
Managing Director, Data Engineering & AIWho You'll Work WithAs a Managing Director in Slalom's Data & AI practice, you will lead the... ...organizations build the data foundations required to enable machine learning, gen…
wherever the destination - our designs lead you there. Our “One Infrastructure” group provides comprehensive planning, design, and engineering services for all phases of roadway, bridge, transit & rail, airport, and port…
$122.76k - $220.97k
: ANA United StatesWork Type: HybridDate Posted: 2026-08-28Arcadis is the world's leading company delivering sustainable design, engineering, and consultancy solutions for natural and built assets.We are more than 34,000…
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. HDR is looking for a Trenchless Engineer to join…
$113.93k - $193.69k
: ANA United StatesWork Type: HybridDate Posted: 2026-08-19Arcadis is the world's leading company delivering sustainable design, engineering, and consultancy solutions for natural and built assets.We are more than 36,000…
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... ...monumental growth. We are seeking a qualified Senior Mechanical Engineer to help…
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.HDR is looking for Professional Engineers for var…
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... ...monumental growth. We are seeking a qualified Senior Electrical Engineer to help…
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. HDR is looking for Senior Dam Safety Engineers a…
Seeking an experienced Oracle OBIEE / OAS Data Engineer with strong expertise in Oracle Analytics Server, Oracle Database, SQL/PL/SQL, ETL, and Data Warehousing. The ideal candidate will be responsible for developing and…
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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