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

Opendoor

Machine Learning Engineer

Miami, FL · On-site

About the Role - Senior and Above You're interviewing for Opendoor's ML team which seeks to automate and refine every decision made in our product. We don't slot into silos; you'll build where you ...

Allvue Systems

Machine Learning Engineer II

Coral Gables, FL · On-site

$92K - $126K/yr

Job Summary The Machine Learning Engineer II supports the discovery, design, and delivery of AI- and automation-enabled solutions that improve operational workflows. The role partners with business ...

Conch Technologies Inc

Sr ML Engineer

Davie, FL · On-site

$95K - $131K/yr

Hybrid Davie, FL Duration: 6+months contract * 8+ years of Machine Learning Engineering or Applied AI experience. * 3+ years in a Lead, Principal, or Senior Technical Leadership role. * Proven ...

New

QUANTUM TECHNOLOGIES LLC

Sr ML Engineer

Davie, FL · On-site

$82/hr

GC-EAD Required Qualifications * 8+ years of Machine Learning Engineering or Applied AI experience. * 3+ years in a Lead, Principal, or Senior Technical Leadership role. * Proven experience ...

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Showing results 21-40

All JobsSr Machine Learning Engineer JobsSr Machine Learning Engineer Jobs in Miami, FL

Sr Machine Learning Engineer information

See Miami, FL salary details

$56.9K

$121K

$175.5K

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

As of Sep 5, 2026, the average yearly pay for sr machine learning engineer in Miami, FL is $121,045.00, according to ZipRecruiter salary data. Most workers in this role earn between $99,900.00 and $137,200.00 per year, depending on experience, location, and employer.

What is a Sr Machine Learning Engineer?

Senior Machine Learning Engineers are experienced professionals who design, develop, and implement machine learning models and systems. They work on complex problems, lead technical projects, and often mentor junior engineers. Their responsibilities include data preprocessing, model selection, algorithm development, and optimizing solutions for scalability and performance. Senior ML Engineers also collaborate closely with data scientists, software engineers, and stakeholders to integrate machine learning into products and services.

What are the key skills and qualifications needed to thrive as a Sr Machine Learning Engineer?

To thrive as a Sr Machine Learning Engineer, you need advanced expertise in machine learning theory, programming (Python, R), data modeling, and a strong background in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, scikit-learn, cloud platforms (AWS, GCP), and relevant certifications (like TensorFlow Developer) is highly beneficial. Strong problem-solving skills, effective communication, and the ability to lead and mentor teams set top candidates apart. These skills ensure the ability to design scalable ML solutions, collaborate effectively, and drive impactful business outcomes.

How does a Sr Machine Learning Engineer typically collaborate with data scientists and software engineers within a project team?

Sr Machine Learning Engineers frequently act as a bridge between data scientists, who focus on model development and experimentation, and software engineers, who handle system integration and production deployment. They translate prototype models into scalable, production-ready solutions, ensuring that models are optimized for real-world performance. Collaboration often involves reviewing code, aligning on data pipeline requirements, and participating in regular team meetings to address technical and business objectives. This cross-functional teamwork is essential for delivering reliable machine learning products.

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

AspectSr Machine Learning EngineerData Scientist
CredentialsBachelor's/Master's in CS, ML, or related fields; experience with ML frameworksBachelor's/Master's/PhD in CS, Statistics, or related fields; strong analytical skills
Work EnvironmentDevelops and deploys ML models, collaborates with engineering teamsAnalyzes data, builds models, interprets data insights for business
Industry UsageTech, finance, healthcare, e-commerceResearch, marketing, finance, tech

While both roles involve working with data and models, Sr Machine Learning Engineers focus on building and deploying scalable ML systems, whereas Data Scientists primarily analyze data and develop insights. The roles often overlap but differ in technical focus and responsibilities.

What are popular job titles related to Sr Machine Learning Engineer jobs in Miami, FL?

For Sr Machine Learning Engineer jobs in Miami, FL, the most frequently searched job titles are:

What job categories do people searching Sr Machine Learning Engineer jobs in Miami, FL look for?

The top searched job categories for Sr Machine Learning Engineer jobs in Miami, FL are:

What cities near Miami, FL are hiring for Sr Machine Learning Engineer jobs?

Cities near Miami, FL with the most Sr Machine Learning Engineer job openings:

Sr Machine Learning Engineer jobs near you
Infographic showing various Sr Machine Learning Engineer job openings in Miami, FL as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $121,045 per year, or $58.2 per hour.

Machine Learning Engineer

Opendoor

Miami, FL • On-site

Full-time

Re-posted 17 days ago

Job description

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's how families put down roots, how neighborhoods strengthen, how the future gets built. We're building the modern system of homeownership giving people the freedom to buy and sell on their own terms. We've built an end-to-end online experience that has already helped thousands of people and we're just getting started.
About the Role - Senior and Above
You're interviewing for Opendoor's ML team which seeks to automate and refine every decision made in our product. We don't slot into silos; you'll build where you have the most impact and the most fun.
These are builder roles across the ML stack. Wherever you land, you'll be doing one of three things:
  • Building models in business-critical contexts like pricing, risk, repairs, and decision optimization. Leverage frontier techniques to extend our capabilities into the unstructured world of real estate.
  • Building the intelligent services that bring structured, precise decision-making into the highly unstructured world of real estate.
  • Building platforms that accelerate how fast our models learn. How fast we learn dictates how fast this company can grow.

You'll work directly with researchers, product, and operations to build the automation that scales in the real world. Our systems must be agile, accurate, and resilient in a heterogeneous space. We are growing fast and this work is at the core.
This isn't a role for everyone. We choose hard mode. We're process-light, high-trust, and we don't put artificial boundaries between you and the work. You'll be expected to understand how your piece connects to the product and communicate at that level. We don't have project managers, we don't have scrum. We do reviews, proposals, demos, and trust.
What We're Looking For
You ship. You pick the boring solution when boring is right and the novel one when it isn't. You know when "good enough and shipped today" beats "perfect next quarter."
You have high agency. You don't wait for permission or a perfectly scoped ticket. You see the problem, take ownership end-to-end, and pull in whoever you need. Lean teams, significant latitude, real accountability.
You run at unclear problems. The most valuable problems here don't come with a playbook - messy data, imperfect ground truth, markets that shift under you. Ambiguity is the job, not an obstacle to it.
You hold a high standard. You measure twice and cut once. You review code, raise the bar on everything around you, and treat the quality of our end-to-end judgment as your problem.
You think in first principles. You have opinions on architecture, distributed systems, ML lifecycle tradeoffs, and the constraints and tripwires of operating models in a high-stakes environment.
You default to AI. You've already integrated modern AI tools into your daily workflow. You use them to move faster, not as a crutch.
You communicate well. You write clear design docs, give useful code reviews, push back on bad ideas without making it personal, and can land a technical tradeoff with a non-technical stakeholder.
You believe in what we're building. Not hype, conviction. You see the opportunity in what we're doing and you want to be part of finishing it.
You have fun. We stay human when times are hard. The task is daunting, but we're all in it together.
What You'll Do
  • Build and train models that real customers and real money depend on - pricing, automation, and decision systems in production
  • Work side-by-side with researchers and analysts to turn prototypes into clean, testable, production-ready code and systems
  • Own model pipelines end-to-end: data ingestion, training, validation, versioning, deployment, and monitoring
  • Design, build, and evolve mission-critical services and APIs that connect to real-world, messy operations
  • Build the platform that accelerates the full ML lifecycle: agentic research, automated retraining, experimentation, deployment, monitoring
  • Proactively tackle real-world challenges like sparsity, data drift, and model decay in a volatile market
  • Use AI tools daily and help push them further than anyone else in the industry
  • Lead technical design reviews, mentor teammates, and raise the bar on everything around you

Qualifications
  • Senior-level or above: deep experience shipping and operating production ML systems, ML-adjacent services, or data/ML platforms
  • Strong fundamentals in Python; comfortable picking up new ones
  • Proficiency with statistics and ability to reason distributionally; has put it to work with real-world monitoring of ML systems
  • Expertise with the end-to-end ML lifecycle (training, evaluation, deployment, monitoring, and iteration) and associated tooling (e.g. MLflow, Airflow, Spark, Delta Lake)
  • Demonstrated ability to make and communicate design decisions and tradeoffs across stakeholders
  • Based in or willing to relocate to Miami, Toronto, or Seattle

Nice to Have
  • ML systems experience in business-critical domains: pricing, forecasting, logistics, marketplaces, risk
  • Streaming and event-driven systems (e.g. Kafka), gRPC, Redis, or workflow engines
  • Interest in real estate or other messy, high-stakes domains with imperfect data

Interview Process
We move fast. Typically:
  • Recruiter phone screen (15 min)
  • A 60 minute technical deep dive to understand a past problem or project you've worked on
  • Two 60 minute pairing-style technical reviews

We're not running these to see if you can finish a problem under pressure. We want to know what it's like to work with you. Before each interview you'll receive an email on what to expect.
Not a perfect fit on paper but clearly excellent? Apply anyway and tell us why in your cover letter. We value T-shaped people. If you have deep expertise in an adjacent area and a strong point of view on how it applies here, that's exactly who we want to talk to.
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's how families put down roots, how neighborhoods strengthen, how the future gets built. We're building the modern system of homeownership, giving people the freedom to buy and sell on their own terms. We've built an end-to-end online experience that has already helped thousands of people - and we're just getting started.

Apply