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Multimodal Learning Jobs in Texas (NOW HIRING)

Avride

Senior / Staff Machine Learning Engineer

Austin, TX · On-site

$124K - $171K/yr

... and Multimodal Large Language Models (MLLMs). These models power both onboard and offboard ... About the Role We are hiring experienced Machine Learning Engineers across Senior, Staff, and ...

HireNetworks

Head of Platform

Austin, TX

$178K - $230K/yr

The platform ingests multimodal bio signals, estimates human state, executes closed-loop adaptation ... Own state modeling, personalization engines, and reinforcement learning systems * Define model ...

webAI Inc

AI Research Director

Austin, TX · On-site

Expertise in modern AI architectures including transformers, diffusion models, multimodal systems, and reinforcement learning * Experience designing, training, and optimizing large-scale ML models

Apple

ML Engineer - Creator Studio

Austin, TX · On-site

Experience delivering products in Multimodal-LLMs/Foundation models, Generative AI, Machine Learning or related areasProven track record in training, evaluating, and deploying multimodal large ...

Diverse Lynx

AI/ ML Engineer

Irving, TX

$120K - $125K/yr

Strong grasp of deep learning architectures including CNNs, RNNs, Transformers, and multimodal models. Clear understanding of when and why to apply each architecture for spatial, temporal, or cross ...

Apple

ML Engineer - Creator Studio

Austin, TX

Experience delivering products in Multimodal-LLMs/Foundation models, Generative AI, Machine Learning or related areas * Proven track record in training, evaluating, and deploying multimodal large ...

Apple

ML Engineer - Creator Studio

Austin, TX · On-site

$171K - $302K/yr

Experience delivering products in Multimodal-LLMs/Foundation models, Generative AI, Machine Learning or related areas Proven track record in training, evaluating, and deploying multimodal large ...

Apple

ML Engineer - Creator Studio

Austin, TX · On-site

$171K - $302K/yr

Experience delivering products in Multimodal-LLMs/Foundation models, Generative AI, Machine Learning or related areas Proven track record in training, evaluating, and deploying multimodal large ...

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Multimodal Learning information

What is multimodal learning?

Multimodal learning is an area of machine learning that involves integrating and processing information from multiple types of data, such as text, images, audio, and video. The goal is to create models that can understand and make predictions based on more than one data modality, similar to how humans use various senses. This approach is used in applications like speech recognition with visual cues, image captioning, and video analysis. By combining different data types, multimodal learning systems can achieve better accuracy and more robust understanding.

What is the difference between Multimodal Learning vs Data Scientist?

AspectMultimodal LearningData Scientist
Required CredentialsAdvanced degrees in AI, Machine Learning, or Computer ScienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, AI development teams, academiaBusiness, tech companies, analytics teams
Industry UsageAI research, multimedia applications, roboticsData analysis, predictive modeling, business insights

Multimodal Learning focuses on developing AI models that process and integrate multiple data types like images, text, and audio. Data Scientists analyze data to extract insights, build models, and support decision-making. While both roles involve data and algorithms, Multimodal Learning is specialized in AI model development for complex data integration, whereas Data Scientists work broadly across data analysis and interpretation.

What are the key skills and qualifications needed to thrive as a Multimodal Learning Specialist, and why are they important?

To excel as a Multimodal Learning Specialist, you need a solid background in machine learning, data science, and computer vision, often supported by an advanced degree in a related field. Familiarity with deep learning frameworks like TensorFlow or PyTorch, experience integrating data from diverse sources (e.g., text, audio, images), and knowledge of relevant algorithms are crucial. Strong problem-solving abilities, creativity, and effective collaboration are standout soft skills for this role. These competencies are vital for developing innovative models that can process and interpret complex, multi-source data to drive impactful AI solutions.

What are some common challenges faced by professionals working in multimodal learning roles, and how can they be addressed?

Professionals in multimodal learning frequently encounter challenges related to integrating and aligning data from multiple sources, such as text, images, audio, or video. Ensuring data quality and consistency across modalities can be complex, and developing models that effectively combine heterogeneous information often requires advanced technical skills and innovative thinking. Collaboration with domain experts and other data scientists is key to overcoming these obstacles, as is staying up to date with the latest research and tools in machine learning. Regular team meetings and cross-disciplinary workshops can help foster a collaborative environment and promote knowledge sharing.
What are popular job titles related to Multimodal Learning jobs in Texas? For Multimodal Learning jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Multimodal Learning jobs in Texas look for? The top searched job categories for Multimodal Learning jobs in Texas are:
What cities in Texas are hiring for Multimodal Learning jobs? Cities in Texas with the most Multimodal Learning job openings:
Multimodal Learning jobs near you

Senior / Staff Machine Learning Engineer

Avride

Austin, TX • On-site

$124K - $171K/yr

Full-time

Posted 14 days ago

Job description

About the Team
Avride develops autonomous vehicle and delivery robot technology, leveraging deep expertise in autonomous systems. With the recent launch of our robotaxi service in Dallas, we are accelerating innovation and redefining the future of mobility. Our team builds self-driving solutions from the ground up, with machine learning at the core of our development pipeline to enable safe and intelligent navigation. We design and deploy state-of-the-art models to address key challenges in autonomous systems, utilizing advanced deep learning architectures such as Convolutional Neural Networks (CNNs), Transformers, and Multimodal Large Language Models (MLLMs). These models power both onboard and offboard applications, ensuring robust and efficient operation. Your work will directly contribute to enhancing the performance, safety, and reliability of Avride's autonomous vehicles and delivery robots.
About the Role
We are hiring experienced Machine Learning Engineers across Senior, Staff, and Principal levels. to site onsite in Austin, Texas. Whether you're a strong individual contributor ready to take on complex technical challenges, or a seasoned technical leader looking to take on complex technical challenges we want to hear from you.
In this role, you will drive the development and deployment of machine learning solutions for some of the hardest problems in autonomy conducting experiments, managing large-scale datasets, and implementing deep learning models tailored for real-world autonomous systems. At more senior levels, you will also define technical strategy, mentor engineers, and influence how ML is practiced across the organization.
What You'll Do
  • Develop and Optimize Machine Learning Models: Design, implement, and refine deep learning models to ensure efficiency, scalability, and robustness - including models for environmental perception and predicting the behavior of other road users. At Staff and Principal levels, you will set the technical vision for entire model families and drive architectural decisions across teams.
  • Curate and Manage Large-Scale Datasets: Oversee data collection, preprocessing, and augmentation to maintain high-quality datasets for training and evaluation. Senior+ engineers will establish standards and tooling that scale across the organization.
  • Enhance and Maintain Training Pipelines: Develop efficient workflows for training, validation, and testing, incorporating distributed training, hyperparameter tuning, and automated monitoring. Staff and Principal engineers will own the long-term roadmap for training infrastructure.
  • Improve Model Deployment and Efficiency: Optimize inference performance, model compression, and deployment across various hardware platforms.
  • Explore and Apply Cutting-Edge ML Techniques: Stay current with advancements in deep learning and lead the evaluation and adoption of novel approaches. Principal engineers are expected to identify opportunities before they become industry standard.
  • Collaborate and Lead Across Teams: Work closely with researchers, software engineers, and robotics experts to integrate ML into real-world autonomous systems. At Staff and Principal levels, you will drive alignment across functions, mentor junior and senior engineers, and serve as a technical authority across the org.
What You'll Need
  • Strong understanding of fundamental machine learning algorithms and neural network techniques.
  • Deep expertise in at least one modern ML domain, such as computer vision, large language models, or generative AI.
  • Senior: 4+ years of experience developing neural network-based algorithms, including data collection, training, and deployment.
  • Staff: 7+ years of experience, with a track record of leading significant technical initiatives and influencing engineering practices beyond your immediate team.
  • Principal: 10+ years of experience, with demonstrated impact at an organizational or industry level - setting multi-year technical direction and driving outcomes across multiple teams.
  • Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAX, along with PySpark, NumPy, and SciPy.
  • Working knowledge of C++ and SQL.
  • Ability to quickly absorb new concepts from research papers, technical reports, and documentation.
  • Strong collaboration and communication skills, with the ability to align technical work with business objectives at all levels of the organization.
What You Must Have
  • Advanced degree in Computer Science, Machine Learning, Robotics, or a related field.
  • Experience developing ML algorithms for autonomous vehicles or robotics applications.
  • Familiarity with neural network deployment and optimization tools such as Triton, TensorRT, or similar frameworks.
  • Publications in top-tier ML conferences, contributions to patent applications, or ML-related open-source projects.
  • For Staff/Principal: experience building and scaling ML teams, defining org-wide technical standards, or driving cross-company research agendas

Candidates are required to be authorized to work in the U.S. The employer is not offering relocation sponsorship, and remote work options are not available.
Avride is an equal opportunity employer and committed to providing reasonable accommodations to qualified applicants and employees with disabilities to ensure they have equal access to employment opportunities. Avride complies with the Americans with Disabilities Act (ADA), if you need a reasonable accommodation to assist with the application or hiring process, or to perform the essential functions of a job, please email jobs@avride.ai.

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