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Hourly Embedded Machine Learning Jobs in Florida

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Embedded Software Engineer

Miramar, FL · On-site

$110K - $135K/yr

Key Responsibilities Computer Vision & Machine Learning * Develop, train, and deploy object ... Embedded & Systems Integration * Deploy inference to edge compute on production equipment; optimize ...

Design, develop, and deploy machine learning, artificial intelligence, and advanced statistical ... hourly rate listed for this position is provided as a good-faith estimate of what the company ...

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Hourly Embedded Machine Learning information

What is an hourly embedded machine learning engineer?

An Hourly Embedded Machine Learning engineer is a professional who specializes in developing and deploying machine learning models on embedded systems, such as microcontrollers, IoT devices, or edge devices, and is compensated on an hourly basis rather than a salaried or project-based arrangement. These engineers work to optimize algorithms so they can run efficiently on devices with limited computing power, memory, and energy resources. Their responsibilities often include model selection, quantization, optimization, and integration of machine learning pipelines into hardware. Hiring on an hourly basis allows for flexibility in project scope and duration, making it ideal for companies with specific, time-limited needs. They often collaborate with hardware engineers, data scientists, and software developers to create intelligent embedded solutions.

How does an hourly embedded machine learning professional typically collaborate with hardware and software teams during a project?

As an Hourly Embedded Machine Learning professional, you will often work closely with both hardware and software engineering teams to ensure that machine learning models are efficiently integrated into embedded systems. This typically involves frequent communication to align on hardware constraints, such as memory and processing power, and to optimize algorithms for real-time performance. You may also participate in joint debugging sessions and code reviews to address integration issues and streamline deployment. Collaboration is key, as successful projects depend on the seamless interaction between machine learning solutions and the embedded hardware platform.

What are the key skills and qualifications needed to thrive as an hourly embedded machine learning engineer, and why are they important?

To thrive as an Hourly Embedded Machine Learning Engineer, you need a solid background in embedded systems, machine learning algorithms, and programming languages like C/C++ and Python, often supported by a degree in computer engineering or a related field. Familiarity with tools such as TensorFlow Lite, embedded Linux, microcontroller development environments, and model optimization frameworks is typically required. Strong problem-solving skills, adaptability, and effective communication help you address complex technical challenges and collaborate with cross-functional teams. These skills are crucial for designing efficient, real-time ML solutions that operate reliably on resource-constrained embedded devices.

What is the difference between Hourly Embedded Machine Learning vs Hourly Data Scientist?

AspectHourly Embedded Machine LearningHourly Data Scientist
CredentialsKnowledge of embedded systems, programming, ML algorithmsDegree in Data Science, Statistics, or related field
Work EnvironmentEmbedded hardware, IoT devices, real-time systemsData analysis, modeling, visualization in office or cloud
Industry UsageConsumer electronics, automotive, IoT devicesFinance, healthcare, marketing, research

Hourly Embedded Machine Learning specialists focus on integrating ML models into embedded systems and hardware, often working with IoT devices and real-time constraints. In contrast, Hourly Data Scientists analyze large datasets to develop predictive models primarily in cloud or office environments. While both roles require programming skills, embedded ML emphasizes hardware integration, whereas data science centers on data analysis and visualization.

What are the most commonly searched types of Embedded Machine Learning jobs in Florida?

The most popular types of Embedded Machine Learning jobs in Florida are:

What cities in Florida are hiring for Hourly Embedded Machine Learning jobs?

Cities in Florida with the most Hourly Embedded Machine Learning job openings:

Infographic showing various Hourly Embedded Machine Learning job openings in Florida as of August 2026, with employment types broken down into 1% As Needed, 77% Full Time, 21% Part Time, and 1% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution.

Embedded Software Engineer

Miramar, FL • On-site

Phiston Technologies, Inc.
Computer and Peripheral Equipment Manufacturing • 11 - 50 employees

$110K - $135K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 8 days ago

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Job description

Position Summary
We are building automated asset identification and serialization into our equipment — vision systems that read, decode, and verify identifiers on media and drives as they move through the destruction process, and that produce the auditable records our clients rely on for chain-of-custody compliance.

This role owns that capability. You will develop and productionize computer vision and machine learning pipelines for label and barcode recognition, integrate them with machine controls and back-office systems, and serve as the internal technical owner for external engineering vendors contributing to these programs.

This is a hands-on individual contributor role with real ownership. You will write production code, and you will also be the person who defines scope for outside contractors, reviews what they deliver, and decides whether it is good enough to ship.

Key Responsibilities

Computer Vision & Machine Learning

•      Develop, train, and deploy object detection and recognition models for barcode, 2D code, and text identification on industrial asset labels.

•      Implement rotation-tolerant detection using oriented bounding box or equivalent approaches; labels arrive at arbitrary orientations, skew, and standoff distances, and the pipeline must handle it without operator intervention.

•      Solve identifier disambiguation: asset labels routinely carry multiple codes — part numbers, model codes, lot codes, vendor identifiers — with no universal marking standard. Build the detection, classification, and confidence-scoring logic that determines which code is the correct serial identifier, and define the fallback path when confidence is insufficient.

•      Integrate OCR for identifiers that are printed as human-readable text with no machine-readable code present.

•      Build and maintain the data pipeline behind the models: capture, annotation standards, augmentation, synthetic data generation, versioned datasets, and reproducible training runs.

•      Define and track model evaluation criteria — precision, recall, mAP, per-class failure analysis — and drive measurable improvement release over release.

Embedded & Systems Integration

•      Deploy inference to edge compute on production equipment; optimize for latency and throughput within the constraints of the hardware (quantization, ONNX/TensorRT, batching).

•      Specify and integrate machine vision hardware: industrial cameras, optics, lighting, triggering, and mounting geometry.

•      Interface vision output with machine control systems (PLC/HMI, serial, Modbus, Ethernet/IP) to gate machine behavior on identification results.

•      Design and build RESTful APIs and services that move serialization data from the machine into back-office and ERP systems, producing the records that support certificates of destruction and audit trails.

•      Own reliability in the field: logging, telemetry, error handling, remote diagnostics, and update mechanisms for deployed systems.

Vendor & Program Ownership

•      Serve as internal technical owner for external engineering vendors and offshore contractors working on vision, firmware, and robotics programs.

•      Write technical scopes of work, define acceptance criteria and milestone gates, and evaluate deliverables against them.

•      Review vendor code and architecture; identify where outsourced work is accruing technical debt and correct course.

•      Run technical cadence with distributed vendor teams across time zones and maintain the internal documentation that keeps knowledge in-house.

•      Support adjacent automation programs, including vision-guided robotic material handling.

Required Qualifications

•      4+ years developing production software in embedded, machine vision, or applied ML roles.

•      Strong Python; working proficiency in C or C++.

•      Practical computer vision experience with OpenCV and modern detection architectures (YOLO family or equivalent), including training and fine-tuning your own models rather than only consuming pre-trained ones.

•      Experience with PyTorch or TensorFlow, including dataset construction, training, evaluation, and deployment.

•      Barcode and 2D code decoding experience (Code 128, Code 39, Data Matrix, QR) using libraries such as ZBar, ZXing, or libdmtx — including the cases where they fail.

•      Experience deploying models to edge or embedded compute (NVIDIA Jetson or similar).

•      RESTful API design and integration; JSON, authentication, error handling.

•      Git, code review, and CI practices.

•      Ability to work onsite in Miramar, FL and directly with equipment on the production floor.

Preferred Qualifications

•      OCR experience (Tesseract, PaddleOCR, TrOCR) applied to low-quality industrial imagery.

•      Oriented / rotated object detection specifically (OBB heads, DOTA-style datasets, or equivalent production experience).

•      Industrial camera integration — GigE Vision, USB3 Vision — and practical lens and lighting selection.

•      PLC/controls integration experience in a manufacturing environment.

•      Firmware experience on ARM/STM32 and RTOS environments.

•      Vision-guided robotics: hand-eye calibration, pick-and-place, 3D or depth sensing.

•      ERP or MES integration experience (NetSuite a plus).

•      Experience managing or technically overseeing external development contractors.

•      BS in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience.

Company Description

At Phiston Technologies, Inc., we believe in innovation, proactive product development, and secure destruction of data. We pride ourselves on being the leading physical data destruction company in the United States. Our clients include some of the top tech companies globally, such as Amazon, Facebook, and Salesforce.

Our products were engineered in response to the rapid growth of information technology and the inherent rise of data breaches across multiple industries. We deeply understand the standards that government and private entities must follow to protect sensitive information from data security breaches.

Our product line-up shows our continued commitment to ever-changing technology and data security demands. We have the only hassle-free automated crushing systems widely reviewed as a necessity in large organizations. Phiston carries a wide array of products from portable desktop destruction destroyers to enterprise-level data center destruction machines.