ZipRecruiter

Log In

1

Embedded Machine Learning Engineer Jobs in Houston, TX

Pivotal Solutions

Senior AI Engineer

Houston, TX · On-site

$99K - $137K/yr

Design, develop, and deploy advanced AI and machine learning models to solve complex business ... Mentor junior engineers and provide technical guidance on AI best practices, model development, and ...

Varsity Tutors

Machine Learning Tutor

Sugar Land, TX · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Missouri City, TX · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Houston, TX · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Pearland, TX · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

next page

Showing results 1-20

All JobsEmbedded Machine Learning Engineer JobsEmbedded Machine Learning Engineer Jobs in Houston, TX

Embedded Machine Learning Engineer information

See Houston, TX salary details

$66.8K

$146.5K

$166.2K

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

As of Jul 10, 2026, the average yearly pay for embedded machine learning engineer in Houston, TX is $146,477.00, according to ZipRecruiter salary data. Most workers in this role earn between $125,600.00 and $165,200.00 per year, depending on experience, location, and employer.

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

To thrive as an Embedded Machine Learning Engineer, you need expertise in machine learning algorithms, embedded systems programming (C/C++ or Python), and a solid understanding of hardware constraints, usually supported by a degree in computer science, electrical engineering, or related fields. Familiarity with tools like TensorFlow Lite, ONNX, microcontroller SDKs, and experience with real-time operating systems (RTOS) are typically required. Strong problem-solving, communication skills, and the ability to collaborate across multidisciplinary teams help you stand out in this role. These skills are crucial for efficiently deploying intelligent models on resource-constrained devices, ensuring optimal performance and seamless integration in real-world applications.

What does an Embedded Machine Learning Engineer do?

An Embedded Machine Learning Engineer designs and implements machine learning models that can run efficiently on embedded systems, such as microcontrollers and edge devices. Their work involves optimizing algorithms to fit within the resource constraints of these devices, integrating ML models into hardware, and ensuring real-time performance. They collaborate closely with hardware engineers and software developers to deploy intelligent features in products like smart sensors, IoT devices, and autonomous systems.

What are some common challenges faced by Embedded Machine Learning Engineers when deploying models to hardware devices?

One of the main challenges for Embedded Machine Learning Engineers is optimizing machine learning models to run efficiently on devices with limited memory, processing power, and energy capacity. Ensuring real-time performance while maintaining accuracy often requires model quantization, pruning, or using lightweight architectures. Additionally, engineers must carefully manage hardware-software integration and address issues like compatibility with various microcontrollers and ensuring secure, reliable updates for deployed models. Close collaboration with hardware engineers and software developers is essential to overcome these challenges and deliver robust embedded AI solutions.

What is the difference between Embedded Machine Learning Engineer vs Firmware Engineer?

AspectEmbedded Machine Learning EngineerFirmware Engineer
Required CredentialsBachelor's/Master's in Computer Science, Electrical Engineering, or related; knowledge of ML frameworksBachelor's in Electrical Engineering, Computer Engineering, or related; embedded systems experience
Work EnvironmentDevelops ML models for embedded devices, often in IoT or smart devicesDesigns and implements low-level firmware for hardware devices
Industry UsageTech companies, IoT, consumer electronics, automotiveConsumer electronics, automotive, industrial equipment

The Embedded Machine Learning Engineer focuses on integrating machine learning models into embedded systems, while the Firmware Engineer specializes in developing low-level software for hardware devices. Both roles require embedded systems knowledge but differ in their core focus and skill sets.

What are popular job titles related to Embedded Machine Learning Engineer jobs in Houston, TX? For Embedded Machine Learning Engineer jobs in Houston, TX, the most frequently searched job titles are:
What job categories do people searching Embedded Machine Learning Engineer jobs in Houston, TX look for? The top searched job categories for Embedded Machine Learning Engineer jobs in Houston, TX are:
What cities near Houston, TX are hiring for Embedded Machine Learning Engineer jobs? Cities near Houston, TX with the most Embedded Machine Learning Engineer job openings:
Embedded Machine Learning Engineer jobs near you

Senior Machine Learning Engineer - Agentic Workflow

Vitol

Houston, TX • On-site

$99K - $137K/yr

Full-time

Re-posted 4 days ago

Job description

Company Description

Vitol is a leader in energy and commodities. Vitol produces, manages and delivers energy and commodities to consumers and industry worldwide. In addition to its primary business of trading, Vitol is invested in infrastructure globally, with $10+billion invested in long-term assets.

Vitol's customers include national oil companies, multinationals, leading industrial companies and utilities. Founded in Rotterdam in 1966, today Vitol serves its customers from some 40 offices worldwide. Revenues in 2024 were $331bn.

Our people are our business. Talent is precious to us and we create an environment in which individuals can reach their full potential, unhindered by hierarchy. Our team comprises more than 65+ nationalities and we are committed to developing and sustaining a diverse work force. Learn more about us here.

This Role is located in Houston, TX - In office 5x a week

Job Description

We are seeking a Senior Machine Learning Engineer / Platform Engineer to design and build a production-grade agentic workflow platform. This role sits at the intersection of LLM systems engineering, distributed platforms, and applied ML, with a strong emphasis on orchestration, reliability, and extensibility. You will be responsible for architecting and implementing agent-based workflows that integrate large language models, retrieval systems, structured knowledge, and external APIs-designed for robustness, observability, and real-world business use.

  • Design and implement multi-agent and single-agent workflows using orchestration patterns and tools, context engineering, memory management, and guardrail strategies.
  • Design RAG pipelines incorporating vector search, hybrid retrieval, and citation tracking.
  • Implement knowledge graph-backed reasoning, including ontologies, entity resolution and graph-based context construction.
  • Design evaluation frameworks for agent task completion correctness, quality, cost, and latency.
  • Develop and deploy machine learning models, focusing on production readiness, scalability, and performance.
  • Collaborate with data scientists to transition experimental models into robust, production-grade applications.
  • Integrate with collaboration platforms (e.g., Teams, alerting systems) for intelligent distribution of insights. 
  • Implement and manage CI/CD pipelines to automate deployment, testing, and monitoring of models.
  • Architect and deploy systems on AWS, leveraging compute, storage and security services
Qualifications
  • Bachelor's or master's degree in computer science, Engineering, or related field.
  • 6+ years of experience in software engineering, ML engineering, or platform engineering.
  • Strong proficiency in writing production-grade Python, and experience with Claude Code or Cursor.
  • Hands-on experience with LLM-based systems, including:
    • LangChain / LangGraph
    • MCP
    • Langsmith
    • Claude or comparable frontier models
    • AWS AgentCore or comparable agentic frameworks
  • Solid understanding of RAG architectures, embeddings, and vector search.
  • Experience designing and consuming APIs (REST and/or async/event-driven).
  • Strong cloud engineering experience on AWS.
  • Knowledge of how to fine-tune frontier models to specific domain knowledge
  • Experience with distillation, quantization and small language models is a plus
  • Experience deploying traditional machine learning models into production environments using MLOps tools and best practices.
  • Knowledge of distributed systems, large-scale model optimization, and API development.
  • Exceptional ability to work on a team - especially a dynamic, innovative "tiger team" developing early stage PoC systems.
  • Strong understanding of container orchestration and cloud-native application design.
  • Ability to work in dynamic environments, handling rapid experimentation and iterative development.
Additional Information

Personal Characteristics 

  • A self-motivated individual who thrives on seeing the results of their work and its impact on the business
  • Strong communication skills, both verbally and in writing
  • A keen sense for the art of the possible
  • Proven ability to be flexible and work hard, both independently and collaboratively
  • Methodical and organized - in general, in experimental design, and in code!
  • Attention to detail with strong analytical, mathematical, and problem-solving skills
  • An interest in learning about the energy commodities space
  • Resourceful and able to think creatively and adapt in a dynamic and energetic environment
  • Team player, with an open, non-political style and a high level of personal integrity
  • Desire to be a thought-partner in a fast-growing team, and make an impact at a business that sits at the heart of the world's energy flows

This Role is located in Houston, TX - In office 5x a week

All your information will be kept confidential according to EEO guidelines.

Apply