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Remote Machine Learning Architect Jobs in Orlando, FL

Worth AI

Data Platform Engineer

Orlando, FL · On-site +1

$106K - $128K/yr

Architect and implement entity resolution logic to de-duplicate and link disparate data points into ... Background supporting machine learning or real-time decisioning use cases from a platform point of ...

Luxus

Recruiting Coordinator

Orlando, FL · Remote

$55K/yr

Architecture and design * Civil, structural, transportation, and MEP engineering * Construction and ... learning recruiting technology, and working independently in a remote environment. This is a ...

Luxus

Recruiting Coordinator

Orlando, FL · Remote

$55K/yr

Architecture and design * Civil, structural, transportation, and MEP engineering * Construction and ... learning recruiting technology, and working independently in a remote environment. This is a ...

Owens Corning

Field Technical Specialist

Orlando, FL · Remote

Remote (National Role; supporting East or West Region) Travel: Up to or greater than 75%, including ... Technical degree in Engineering, Architecture, Construction Technology, Building Sciences, or a ...

Hansoninc

Deputy CAD Administrator

Orlando, FL · On-site +1

$50/hr

Position can be located in any of our offices, hybrid or fully remote Key Responsibilities CAD ... Support the development and delivery of CAD training materials and learning sessions. System ...

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All JobsMachine Learning Architect JobsRemote Machine Learning Architect JobsRemote Machine Learning Architect Jobs in Orlando, FL

Remote Machine Learning Architect information

See Orlando, FL salary details

$43.4K

$120.2K

$188.1K

How much do remote machine learning architect jobs pay per year?

As of Sep 3, 2026, the average yearly pay for remote machine learning architect in Orlando, FL is $120,196.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,000.00 and $155,000.00 per year, depending on experience, location, and employer.

What is a remote machine learning architect?

A Remote Machine Learning Architect is a professional who designs, builds, and oversees machine learning systems and infrastructure while working remotely. They collaborate with data scientists, engineers, and stakeholders to define system architecture, select appropriate algorithms, and ensure scalable deployment of machine learning models. Their responsibilities include setting technical standards, optimizing workflows, and ensuring integration with existing IT infrastructure, all accomplished through remote communication and collaboration tools. This role requires strong expertise in machine learning, cloud platforms, and software engineering.

How does a remote machine learning architect typically collaborate with distributed teams to deliver successful projects?

As a Remote Machine Learning Architect, effective collaboration with globally distributed teams is essential. You will often coordinate with data scientists, software engineers, and business stakeholders via virtual meetings, shared documentation, and project management tools. Regular communication, clear documentation of model designs, and version control practices are crucial to ensure alignment and smooth integration of machine learning solutions. Adopting agile methodologies and being proactive in addressing time zone differences help maintain project momentum and foster a productive team environment.

What are the key skills and qualifications needed to thrive as a remote machine learning architect, and why are they important?

To thrive as a Remote Machine Learning Architect, you need deep expertise in machine learning algorithms, model development, and a solid background in computer science or related fields, often supported by an advanced degree. Familiarity with cloud platforms (such as AWS, Azure, or GCP), deep learning frameworks (like TensorFlow or PyTorch), and relevant certifications are typically expected. Strong problem-solving, communication, and project management skills help you collaborate effectively with distributed teams and stakeholders. These skills and qualities are crucial for designing scalable ML solutions that drive business value in a remote work environment.

What is the difference between Remote Machine Learning Architect vs Data Scientist?

AspectRemote Machine Learning ArchitectData Scientist
Required CredentialsMaster's or PhD in CS, AI, or related fields; certifications in ML frameworksMaster's in Data Science, Statistics, or related; certifications in data analysis tools
Work EnvironmentDesigning ML systems, collaborating with engineering teams, remote or on-siteAnalyzing data, building models, often remote or in-office
Industry UsageTech, finance, healthcare, e-commerceResearch, finance, marketing, tech

Remote Machine Learning Architects focus on designing and implementing scalable ML systems, while Data Scientists analyze data and build models. Both roles require advanced degrees and often overlap in skills, but their core responsibilities differ in scope and focus.

What are popular job titles related to Remote Machine Learning Architect jobs in Orlando, FL?

For Remote Machine Learning Architect jobs in Orlando, FL, the most frequently searched job titles are:

What job categories do people searching Remote Machine Learning Architect jobs in Orlando, FL look for?

The top searched job categories for Remote Machine Learning Architect jobs in Orlando, FL are:

What cities near Orlando, FL are hiring for Remote Machine Learning Architect jobs?

Cities near Orlando, FL with the most Remote Machine Learning Architect job openings:

Remote Machine Learning Architect jobs near you

Data Platform Engineer

Worth AI

Orlando, FL • On-site, Remote

$106K - $128K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 5 days ago

Job description

Worth AI, a leader in the computer software industry, is looking for a talented and experienced Data Platform Engineer to join their innovative team. At Worth AI, we are on a mission to revolutionize decision-making with the power of artificial intelligence while fostering an environment of collaboration, and adaptability, aiming to make a meaningful impact in the tech landscape.. Our team values include extreme ownership, one team and creating reaving fans both for our employees and customers.

As a Data Platform Engineer, you will design, build, and operate the core data services that power our products and analytics. You'll own end-to-end data pipelines and API services that ingest, process, and expose high-quality data to internal customers (data science, analytics, product, and other engineering teams) and external partners.

You'll be part of a small, high-impact team that treats the data platform as a product with strong SLAs, and reliable self-service for internal and external users.

Responsibilities

What you'll do:

  • Architect and implement entity resolution logic to de-duplicate and link disparate data points into unified "Golden Records" for businesses and individuals
  • Design and maintain a high-performance global business knowledge graph and ontology to map complex ownership chains, UBOs, and hidden risk relationships across international borders
  • Implement a hybrid storage strategy that bridges graph databases for relationship mapping with document and search stores for rich metadata and adverse media content
  • Optimize the platform for real-time risk assessment, ensuring the ability to traverse multiple levels of ownership in milliseconds to support automated "Go/No-Go" onboarding decisions
  • Design and build scalable data services and APIs for ingesting, transforming, and serving data across the company
  • Develop and maintain batch and streaming data pipelines using modern data processing frameworks and AWS cloud-native tooling
  • Own the reliability, performance, and API first data platform, including monitoring, alerting, and on-call where appropriate
  • Implement best practices for data modeling, quality, lineage, and governance to ensure trustworthy, well-documented datasets
  • Work closely with data scientists, analysts, and application engineers to understand their needs and translate them into robust platform capabilities
  • Drive automation and standardization through CI/CD, model as a service, and reproducible environments
  • Help define and evolve the architecture of our data platform as a true internal service with clear contracts, SLAs, and versioned APIs

Requirements

    • Expertise in Graph Ecosystems: Hands-on experience with Graph databases (e.g., Neo4j, AWS Neptune, or TigerGraph) and query languages like Cypher or Gremlin
    • Identity & Linkage Mastery: Proven experience with Entity Resolution or Record Linkage (e.g., using tools like Senzing, Quantexa, or custom probabilistic matching models)
    • Schema Design: Ability to design flexible ontologies that handle evolving regulatory data (e.g., changing PEP definitions or Sanction list formats)
    • API Performance for Graphs: Experience building GraphQL or REST APIs specifically optimized for graph traversals and deep-tree lookups
    • Experience building centralized data platforms or "data-as-a-service" offerings at scale (e.g., at a large tech or cloud-native company)
    • Strong software engineering skills in at least one language commonly used for data and services (e.g., Python, Java, Go, Rust)
    • Hands-on experience building data pipelines and ETL/ELT workflows on a major cloud provider (AWS preferred)
    • Experience with modern data stack tools such as Spark/Flink, Kafka/Kinesis, Airflow/managed schedulers, and data warehouses (e.g., Snowflake, Redshift, BigQuery, Databricks)
    • Familiarity with DevOps practices: CI/CD, containerization (Docker), orchestration (Kubernetes), and infrastructure-as-code (Terraform)
    • Strong focus on observability (metrics, logs, traces), resilience, and building early warning signals
    • Comfort collaborating cross-functionally and communicating clearly with both technical and non-technical stakeholders.

Nice to Have

    • Background supporting machine learning or real-time decisioning use cases from a platform point of view
    • Compliance Domain Knowledge: Understanding of AML, CTF, and KYC/KYB data structures (e.g., LEIs, ISO 20022)
    • Geospatial Data: Experience handling global address normalization and geospatial indexing for risk detection

** This role is Orlando based, hybrid position in our Winter Park Office.

Benefits

    • Health Care Plan (Medical, Dental & Vision)
    • Retirement Plan (401k, IRA)
    • Life Insurance
    • Flexible Paid Time Off
    • 9 paid Holidays
    • Family Leave
    • Work From Home
    • Free Food & Snacks (Orlando)
    • Wellness Resources

Apply