data to drive business solutions. May develop processes and machine learning based tools to monitor and analyze model performance and... ...Education: Bachelor’s degree in Computer Science, Statistics, Engineering, Data…
Engineering & IT · Tampa, FL
Machine Learning Engineer jobs in Tampa, FL
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
168
Open roles today
$43–$110/hr
Typical pay range
$146k
Median, full-time
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Open machine learning engineer roles
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seeking a highly skilled Data Scientist to develop advanced machine learning, predictive analytics, and artificial intelligence solutions... ...patterns, and operational risks. Perform data preparation, feature engineeri…
Black Cape Title: Python Data Engineer (Entry - Mid Level) Location: Tampa, FL Onsite: Expected to go onsite (into a SCIF... ...full-stack web applications, advanced data analysis tools, and machine learning capabilities…
logistics (demand forecasting, resource optimization) Utilize time-series analysis (e.g., ARIMA, Exponential Smoothing) and machine learning models (Random Forest, Gradient Boosting, XGBoost) Develop inventory optimizati…
JOB TITLE: SERVICE SALES ENGINEER DEPARTMENT: SALES LOCATION: ATS Waypoint; Tampa, Florida JOB SUMMARY: A Service Sales Engineer... .... SKILLS AND ABILITIES: • Demonstrated ability to learn technical, complex systems •…
Strong hands-on experience in Python for data engineering and application development. Extensive experience with AWS cloud services, including S3, EMR, Glue, Lambda, IAM, EC2, ECS/EKS, CloudWatch, and Redshift. Strong ex…
**Job Title: Field Engineer / Superintendent****Job Location:** Tampa **Company Overview:** Join our dynamic team at Matcon, where we are committed to delivering excellence in construction and engineering projects. We ar…
GARNEY CONSTRUCTION A Project Engineer position is available at Garney. This position will act as the backbone of the project management team and keep the job on track. Great attention to detail and the ability to organi…
Job Description Job Description WWC Global, an operating firm of Command Holdings, is seeking a Machine Learning (ML) Engineer to serve on a potential contract supporting USSOCOM's mission to transform the SOF Enterprise…
analyzes complex data sets while planning, executing, and managing machine learning (ML) projects with cloud-native platforms and advanced ML... ...to conduct large-scale data processing, and performs data engineering, d…
analytics projects including Big Data, artificial intelligence/ machine learning, and other applications to advance current platform... ...communicator, support technical exchanges with scientists and engineers to expres…
$35 - $50 per hour
FIELD SERVICE ENGINEER Seeking a Field Service Engineer who thrives in fast-paced industrial environments and is comfortable traveling... .... MUST HAVE EXPERIENCE WORKING ON DRYERS, BLENDERS, EXTRUSION MACHINES, AND CON…
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What machine learning engineers earn in Tampa
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $43–$60 | $89k–$124k |
| Mid level | $60–$81 | $124k–$168k |
| Senior | $78–$110 | $163k–$228k |
Adjusted for the Tampa 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
02
Senior ML Engineer
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Staff ML Engineer
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ML Platform Lead
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