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Temporary Meta Machine Learning Jobs in Irving, TX

KaashTech LLC

AI Architect

Dallas, TX · On-site

$62.50 - $82.50/hr

Deep knowledge of generative AI, agentic AI systems, and traditional machine learning, including ... Meta Llama and other open-source models for on-premise or cost-optimized deployments * Ability to ...

Adecco

Machine Operator

Fort Worth, TX · On-site

$16 - $20/hr

... learning. * Temporary assignment with opportunity for permanent hire based on performance and ... Operate rotary machines, up and down machines, laminators, and choppers according to established ...

Droisys

Generative AI LangGraph experience

Plano, TX · On-site

... Claude, Meta Llama, or similar. * Prompt Engineering: Expertise in designing, testing, and ... Machine Learning Libraries: Familiarity with ML/AI libraries such as PyTorch, TensorFlow, Hugging ...

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How much do temporary meta machine learning jobs pay per hour?

As of Aug 21, 2026, the average hourly pay for temporary meta machine learning in Irving, TX is $21.91, according to ZipRecruiter salary data. Most workers in this role earn between $18.94 and $24.47 per hour, depending on experience, location, and employer.

What is a temporary Meta machine learning job?

Temporary Meta Machine Learning jobs are short-term positions at Meta (formerly Facebook) that focus on developing, deploying, or researching machine learning models and technologies. These roles may support ongoing projects, fill gaps during employee leave, or address spikes in workload. Responsibilities can include data preprocessing, model training, evaluation, and collaborating with cross-functional teams. Temporary roles often give candidates exposure to Meta's cutting-edge AI tools and processes, and may sometimes lead to permanent opportunities.

What are the key skills and qualifications needed to thrive as a temporary Meta machine learning engineer?

To thrive as a Temporary Meta Machine Learning Engineer, you need a strong background in computer science, statistics, and machine learning, typically with experience in Python and relevant ML frameworks. Familiarity with tools such as TensorFlow, PyTorch, cloud platforms, and version control systems is often required, along with a proven ability to rapidly learn new technologies. Strong problem-solving skills, adaptability, and effective communication are essential for collaborating within dynamic teams and meeting project goals on tight timelines. These skills ensure that you can quickly contribute to impactful ML projects, deliver results efficiently, and integrate well into fast-paced, innovative environments.

What are some common challenges faced by professionals in temporary machine learning roles at Meta, and how can they be addressed?

Professionals in temporary machine learning roles at Meta often encounter challenges such as quickly acclimating to complex codebases, integrating with established teams, and delivering impactful results within a limited timeframe. Success in these roles typically requires strong technical skills, adaptability, and effective communication. Proactively seeking guidance, leveraging available documentation, and collaborating closely with permanent team members can help overcome these hurdles and maximize contributions during the temporary assignment.

What is the difference between Temporary Meta Machine Learning vs Data Scientist?

AspectTemporary Meta Machine LearningData Scientist
CredentialsTypically requires a background in computer science, statistics, or related fields; certifications in machine learning or data analysis are commonRequires a degree in computer science, statistics, or related fields; certifications like Certified Data Scientist are advantageous
Work EnvironmentProject-based, often contract roles within tech companies, startups, or consulting firmsFull-time or contract roles in various industries including finance, healthcare, and tech
Industry UsagePrimarily in tech, AI, and machine learning-focused companiesWidely used across multiple industries including finance, healthcare, marketing, and tech

Temporary Meta Machine Learning roles focus on short-term projects involving machine learning model development and deployment, often requiring specialized technical skills. Data Scientist roles are broader, encompassing data analysis, statistical modeling, and insights generation across diverse industries. While both roles require strong analytical skills and technical knowledge, Temporary Meta Machine Learning positions are more specialized in AI and machine learning applications.

What cities near Irving, TX are hiring for Temporary Meta Machine Learning jobs?

Cities near Irving, TX with the most Temporary Meta Machine Learning job openings:

Temporary Meta Machine Learning jobs near you

Senior Staff Software Engineer, Perception (R4985)

Shield AI

Dallas, TX • On-site

$233K - $350K/yr

Full-time

Posted 24 days ago

Job description

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI's technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube. 

The Hivemind Solutions Perception team develops the next generation of perception capabilities for autonomous systems by combining state-of-the-art machine learning with the proven foundations of computer vision. The team advances how autonomous platforms understand and interpret the world by developing vision, vision-language (VLM), and vision-language-action (VLA) models that tackle core perception challenges such as object understanding, scene interpretation, and mission-relevant environmental awareness. Working at the intersection of research and production, our engineers build the data pipelines, supervised fine-tuning (SFT) workflows, evaluation frameworks, and deployment infrastructure needed to transform cutting-edge AI research into reliable, mission-ready perception capabilities.
 
In this role, you'll develop and deploy advanced machine learning models that enable autonomous systems to better understand, reason about, and interact with their environment. You'll partner closely with machine learning researchers, autonomy engineers, perception engineers, and platform teams to translate emerging AI capabilities into reliable, production-ready systems for U.S. and international defense customers. This is an ideal opportunity for engineers who enjoy building state-of-the-art AI systems while solving the practical challenges of deploying them on operational autonomous platforms. 
What You'll Do:

Model Development - Design, train, fine-tune, and maintain state-of-the-art vision, vision-language, and vision-language-action models that improve perception and decision-making for autonomous systems. 

Data Pipelines & Model Training - Build scalable data pipelines, supervised fine-tuning (SFT) workflows, and evaluation loops that continuously improve model performance on mission-relevant tasks. 

Model Deployment & Optimization - Deploy and optimize machine learning models for embedded hardware using technologies such as ONNX, TensorRT, and hardware-accelerated inference frameworks. 

Perception & Autonomy Applications - Apply modern machine learning techniques to solve challenging perception and autonomy problems across aerial and other autonomous systems operating in complex, real-world environments. 

Research-to-Production - Translate cutting-edge machine learning research into production-ready capabilities by balancing model performance, robustness, computational efficiency, and operational reliability. 

Cross-functional Collaboration - Partner closely with perception, autonomy, platform, and software engineering teams to integrate machine learning capabilities into mission-ready autonomous systems. 

Model Evaluation & Validation - Develop benchmarks, testing methodologies, and evaluation frameworks to measure model performance, identify failure modes, and guide future improvements. 

Continuous Improvement - Improve training infrastructure, developer tooling, deployment workflows, and model lifecycle management to accelerate experimentation and production delivery. 

Required Qualifications:

  • Typically requires a minimum of 10 years of related experience with a Bachelor's degree; or 9 years and a Master's degree; or 7 years with a PhD; or equivalent work experience.

  • Mastery of machine learning fundamentals. 

  • Experience training an deploying ML models for computer vision in a production setting. 

  • Strong understanding of 3D vision problems/algorithms. 

  • Experience with machine learning frameworks such as PyTorch and TensorFlow. 

  • Demonstrated expertise in deploying models using TensorRT and ONNX. 

  • Proficiency in C++ and Python. 

  • Strong analytical and problem-solving skills, with the ability to translate research into practical applications. 

  • Ability to obtain a SECRET clearance 
Preferred Qualifications:

  • Experience with developing autonomous systems for defense customers. 

  • Experience with training/finetuning vision-language models, vision-language-action models, and/or world models.  

  • Contributions to open-source projects in machine learning or computer vision. 

  • Track record of publications in leading computer vision and robotics conferences and journals (e.g., CVPR, ICCV/ECCV, RAL, ICRA).

$233,760 - $350,640 a year
#LI-DS-1
#LE

Full-time regular employee offer package:
Pay within range listed + Bonus + Benefits + Equity
 
Temporary employee offer package:
Pay within range listed above + temporary benefits package (applicable after 60 days of employment)
 
Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.
 
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Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know. 
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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