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Embedded Machine Learning Engineer Jobs in Texas

As a Senior Machine Learning Engineer, you'll own impactful problems end-to-end-from data ... AI is deeply embedded in how we evolve at Bumble. In this role, you'll independently apply modern ...

Senior Machine Learning Engineer

Plano, TX · On-site

$100K - $137K/yr

Senior Machine Learning Engineer Location: Ann Arbor, Michigan Experience Level: 7+ Years Department: Data Science / Engineering Employment Type: Full-time About the Role: We are looking for an ...

Senior Machine Learning Engineer

Austin, TX · On-site +1

$335K - $400K/yr

We are hiring Senior Machine Learning Engineers We are hiring engineers with significant expertise in both machine learning and software engineering. You will be working with our engineering and ...

We are hiring Senior Machine Learning Engineers We are hiring engineers with significant expertise in both machine learning and software engineering. You will be working with our engineering and ...

We are hiring Senior Machine Learning Engineers We are hiring engineers with significant expertise in both machine learning and software engineering. You will be working with our engineering and ...

Summary The Machine Learning Engineer designs and evolves enterprise AI systems and architectures that enable scalable, secure, and high-impact adoption across the organization. This role defines end ...

Summary The Machine Learning Engineer designs and evolves enterprise AI systems and architectures that enable scalable, secure, and high-impact adoption across the organization. This role defines end ...

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Showing results 1-20

Embedded Machine Learning Engineer information

See Texas salary details

$65.2K

$142.9K

$162.1K

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

As of Jul 20, 2026, the average yearly pay for embedded machine learning engineer in Texas is $142,900.00, according to ZipRecruiter salary data. Most workers in this role earn between $122,500.00 and $161,200.00 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 cities in Texas are hiring for Embedded Machine Learning Engineer jobs? Cities in Texas with the most Embedded Machine Learning Engineer job openings:
Senior Machine Learning Engineer

Senior Machine Learning Engineer

Bumble Inc.

Austin, TX • On-site

$220K - $250K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 13 days ago


Job description

Introduction to the role & team At Bumble, we're building a world where all relationships are healthy and equitable, and machine learning is central to how we make that real for millions of people every day. As part of our Machine Learning team, you'll help shape intelligent systems that power meaningful connections, safer interactions, and more personalised experiences across our platform.
As a Senior Machine Learning Engineer, you'll own impactful problems end-to-end-from data exploration through to production deployment, while collaborating closely with Product, Engineering, and Data partners. You'll bring curiosity into how we experiment, iterate, and improve, and you'll role model our values of Curiosity and Excellence by continuously raising the bar in how we build and apply AI.
AI is deeply embedded in how we evolve at Bumble. In this role, you'll independently apply modern machine learning and emerging AI techniques, contributing to scalable systems while ensuring thoughtful, responsible use of AI in everything we ship.
What you'll do
  • Build and deploy machine learning models that improve recommendations, ranking, and personalization, driving measurable impact on user experience and engagement
  • Own problems end-to-end, from data exploration and feature engineering through to model training, evaluation, and production deployment
  • Develop and maintain scalable ML pipelines using tools such as Spark and Airflow to support reliable, high-quality model delivery
  • Apply modern ML frameworks (e.g. PyTorch or TensorFlow) to design, train, and optimise models in production environments
  • Contribute to experimentation frameworks, including A/B testing and offline evaluation, to iterate on model performance with an agile mindset
  • Collaborate cross-functionally with Product and Engineering, working with purpose to translate product questions into ML solutions
  • Take ownership of delivering high-quality solutions and see work through from insight to impact, balancing speed and rigor
  • Apply responsible AI practices, ensuring fairness, transparency, and safety are considered in model development and deployment

About You
  • Typically requires 5-8 years of experience, though we welcome candidates with alternative backgrounds that demonstrate equivalent skills.
  • Strong experience building and deploying machine learning models in production environments
  • Proficiency in Python and experience with at least one major ML framework (e.g. PyTorch, TensorFlow)
  • Experience working with data pipelines and distributed systems (e.g. Spark, Airflow) to support ML workflows
  • Familiarity with experimentation methodologies such as A/B testing and model evaluation techniques
  • Ability to collaborate effectively across functions, demonstrating strong ownership and a collaborative mindset
  • Demonstrates an agile mindset, adapting approaches based on data and evolving priorities while maintaining focus on outcomes
  • Growing AI fluency, with the ability to independently apply ML techniques and emerging tools (including LLMs) to solve problems responsibly

$220,000 - $250,000 a year
For base compensation, we set standard ranges for all roles based on function, level, and geographic location. This position is also typically eligible to participate in our short- and long-term incentive programs. Benefits include Medical, Dental, Vision, 401(k) match, Unlimited Paid Time Off Policy.
Maven Fertility: $10,000 lifetime benefit for fertility, adoption, abortion care, and more.
26 Weeks Parental Leave: For both primary and secondary caregivers.
Family & Compassionate Leave: Inclusive of domestic violence recovery.
Unlimited Paid Time Off: Take the time you need.
Company-wide Week Off: Annual collective rest for the entire company.
Focus Fridays: No meetings, emails, or deadlines-just deep work.
About Us
Bumble Inc. is the parent company of Bumble Date, BFF, and Badoo. The Bumble platform enables people to build healthy and equitable relationships, through Kind Connections. Founded by Whitney Wolfe Herd in 2014, Bumble was one of the first dating apps built with women at the center and connects people across dating (Bumble Date) and friendship (BFF). BFF is a friendship app where people in all stages of life can meet people nearby and create meaningful platonic connections and community based on shared interests. Badoo, which was founded in 2006, is one of the pioneers of web and mobile dating products.
AI Fluency
AI is important to us. We're excited by people who are curious and experimental, and who think thoughtfully about how AI can amplify their impact and outcomes.
We encourage you to use AI responsibly as you prepare your application. Please don't use it to fabricate experiences or answer questions live in interviews. We care deeply about authenticity and want to understand your real skills, judgment and voice, because building a meaningful, genuine connection with you matters to us.
Final Compensation
Will be determined based on factors such as the selected candidate's qualifications, relevant experience, skill set, and other job-related considerations.
Benefits & Perks
Insurance: Medical/dental/vision, 30-day eligibility. Bumble has multiple competitive offerings that will be available to you on the first of the month following date of hire.
Unlimited PTO + 1 company-wide week off + Focus Fridays every week
Fully paid life and long-term disability insurance
401k with 4% company match if you contribute 6%, 90-day eligibility
Monthly wellness benefit and access to Noom, Unmind, and Your Money Line
Maternity and Fertility benefit + 26 week paid parental leave
Premium App Access
Inclusion at Bumble Inc.
Bumble Inc. is an equal opportunity employer and we strongly encourage people of all ages, colour, lesbian, gay, bisexual, transgender, queer and non-binary people, veterans, parents, people with disabilities, and neurodivergent people to apply. We're happy to make any reasonable adjustments that will help you feel more confident throughout the process, please don't hesitate to let us know how we can help.
In your application, please feel free to note which pronouns you use (For example: she/her, he/him, they/them, etc).
AI in Bumble Inc. Hiring
At Bumble, we may use AI tools to support parts of our recruitment process - such as helping us record, transcribe, and summarize conversations, and supporting job alignment by comparing resumes and job descriptions to highlight skills and potential roles that may be a good match. These tools help us work more efficiently and stay focused on you during our conversations. Importantly, all hiring decisions are made by people. AI is used only to support our team's efficiency and improve the candidate experience - not to evaluate or decide on your candidacy. Participation in AI-supported interviews and conversations is completely voluntary and will not impact your candidacy. If you'd prefer to opt out, simply let your recruiter or interviewer know at the start of a call, or anytime during the interview or conversation. Summaries and related data are retained only as long as needed in line with our internal data retention policies. If at any point you'd like a transcription or summary deleted, please contact your recruiter directly.
For further information on how we hold and manage your data, please refer to our Privacy Policy.