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Machine Learning Engineer Opt Jobs in Miami, FL (NOW HIRING)

Machine Learning Engineer

Sunrise, FL ยท On-site

$90K - $110K/yr

Role - Machine Learning Engineer Experience Required -8+ Years We are seeking a Machine Learning Engineer to design, build, and deploy Generative AI solutions powered by Large Language Models (LLMs)

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

About Opendoor At Opendoor our mission is to tilt the world in favor of homeowners and those who aim to become one. Homeownership matters. It's how people build wealth, stability, and community. It ...

Strong coding and engineering skills Responsibilities * Develop and improve / Voice Generation models * Train, fine-tune, and evaluate speech models * Bring research ideas into production systems

We're looking for a Generative AI & Machine Learning Engineer who thrives at the intersection of research and real-world impact. This role is perfect for someone who loves experimenting with new ...

We're looking for a Generative AI & Machine Learning Engineer who thrives at the intersection of research and real-world impact. This role is perfect for someone who loves experimenting with new ...

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Machine Learning Engineer Opt information

See Miami, FL salary details

$30.1K

$123.2K

$185.1K

How much do machine learning engineer opt jobs pay per year?

As of Jul 30, 2026, the average yearly pay for machine learning engineer opt in Miami, FL is $123,160.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,100.00 and $148,200.00 per year, depending on experience, location, and employer.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models into production environments. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, reliable systems that organizations can use to make predictions or automate tasks. Their responsibilities include data preprocessing, choosing appropriate algorithms, model training, and ensuring the model's performance in real-world applications. Machine Learning Engineers often collaborate with data scientists, data engineers, and product teams to deliver intelligent solutions.

What is the difference between Machine Learning Engineer Opt vs Data Scientist?

AspectMachine Learning Engineer OptData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; certifications in ML toolsBachelor's or Master's in CS, Statistics, or related fields; data analysis certifications
Work EnvironmentDevelops, tests, and deploys ML models in production systemsAnalyzes data, builds models, and provides insights for decision-making
Employer & Industry UsageTech companies, AI startups, e-commerce, financeResearch institutions, tech firms, consulting, finance
Common Search & ComparisonOften compared for technical skills and deployment focusCompared for data analysis and business insights

Machine Learning Engineers Opt focus on deploying scalable ML models in production environments, while Data Scientists primarily analyze data and develop models for insights. Both roles require strong technical skills, but their core responsibilities differ in application and deployment.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need a solid background in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch), data processing tools, and cloud platforms, along with relevant certifications, is highly valuable. Strong problem-solving ability, collaboration, and effective communication are standout soft skills in this role. These skills and qualities ensure the successful development, deployment, and integration of machine learning solutions that drive business value.

What are some common challenges Machine Learning Engineers face when deploying models to production environments?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, handling data drift, and integrating models seamlessly with existing systems when deploying to production. Monitoring model performance in real time and retraining models as new data becomes available are also critical tasks. Collaboration with data engineers and DevOps teams is essential to address infrastructure and deployment hurdles while maintaining model accuracy and reliability.
What are popular job titles related to Machine Learning Engineer Opt jobs in Miami, FL? For Machine Learning Engineer Opt jobs in Miami, FL, the most frequently searched job titles are:
What cities near Miami, FL are hiring for Machine Learning Engineer Opt jobs? Cities near Miami, FL with the most Machine Learning Engineer Opt job openings:

Machine Learning Engineer

Northern Base

Sunrise, FL โ€ข On-site

$90K - $110K/yr

Full-time

Posted 7 days ago


Job description

Job Description
Role -ย  Machine Learning Engineer
Experience Required -8+ Years
ย 
We are seeking a Machine Learning Engineer to design, build, and deploy Generative AI solutions powered by Large Language Models (LLMs). In this role, you will work on end-to-end GenAI use cases, from model selection to production-ready systems. Key Responsibilities Develop and productionize GenAI applications using LLMs (open-source and closed-source). Design agentic workflows using LangChain and LangGraph.
ย 
Must Have Technical/Functional Skills:
ย 
We are seeking a Machine Learning Engineer to design, build, and deploy Generative AI solutions powered by Large Language Models (LLMs). In this role, you will work on end-to-end GenAI use cases, from model selection to production-ready systems. Key Responsibilities Develop and productionize GenAI applications using LLMs (open-source and closed-source). Design agentic workflows using LangChain and LangGraph.
โ€ข Implement short-term and long-term memory strategies for LLM-based systems.
โ€ข Optimize prompts, retrieval pipelines, and orchestration logic.
โ€ข Collaborate with product and platform teams to deliver scalable AI solutions.
Required Qualificationsย 
โ€ข Strong experience with LLMs (e.g., OpenAI, Anthropic, Llama, Mistral)
โ€ข Hands-on experience with LangChain and/or LangGraph.
โ€ข Solid understanding of LLM memory architecture and state management.
โ€ข Proficiency in Python and ML engineering best practices.
Nice to Have
โ€ข Experience with GCP services (e.g., Vertex AI, BigQuery, GCS).
โ€ข Experience deploying ML/GenAI systems in production environments.
โ€ข data scientist
โ€ข Can do ML model
ย 
Roles & Responsibilities
ย 
โ€ข Design, develop, and deploy GenAI applications using LLMs.
โ€ข Build and implement agentic workflows using LangChain/LangGraph.
โ€ข Develop ML models and production-ready AI solutions.
โ€ข Implement and manage LLM memory and state management strategies.
โ€ข Optimize prompts, retrieval pipelines, and orchestration workflows.
โ€ข Collaborate with product and platform teams to deliver scalable AI solutions.
โ€ข Deploy, monitor, and maintain AI/ML systems in production environments.
โ€ข Evaluate and integrate open-source and proprietary LLMs.
ย 
Base Salary Range : $90,000 to $110,000 Per Annum