{
  "job_id": "JvWfjZe9r_XFrEO1AAAAAA==",
  "job_city": "Austin",
  "job_state": "Texas",
  "job_title": "Lead Machine Learning Engineer - Generative AI",
  "job_salary": null,
  "job_country": "US",
  "job_benefits": null,
  "job_latitude": 30.267152999999997,
  "job_location": "Austin, TX, United States",
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      "publisher": "WhatJobs",
      "apply_link": "https://www.whatjobs.com/jobs/lead-machine-learning-engineer/austin-texas?id=2585550063&utm_campaign=google_jobs_apply&utm_source=google_jobs_apply&utm_medium=organic"
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      "is_direct": true,
      "publisher": "Talantix.io",
      "apply_link": "https://talantix.io/jobs/lead-machine-learning-engineer-ai-solutions-searchability-20260126-fi8h?utm_campaign=google_jobs_apply&utm_source=google_jobs_apply&utm_medium=organic"
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  "employer_name": "WhatJobs Direct",
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  "job_posted_at": "1 day ago",
  "job_publisher": "WhatJobs",
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  "job_highlights": {
    "Qualifications": [
      "The ideal candidate will have a deep understanding of deep learning architectures, particularly transformers and diffusion models, and extensive experience in building and deploying production-ready ML solutions",
      "You should possess strong leadership qualities, excellent communication skills, and a passion for solving complex problems with state-of-the-art AI techniques",
      "Master's or PhD in Computer Science, Machine Learning, or a related quantitative field",
      "Minimum of 6 years of experience in machine learning engineering, with a significant focus on deep learning and generative models",
      "Proven experience leading ML projects and teams",
      "Expertise in Python and ML frameworks such as TensorFlow, PyTorch, or JAX",
      "Strong understanding of NLP, computer vision, and/or reinforcement learning",
      "Experience with large-scale data processing and distributed training",
      "Familiarity with cloud platforms (AWS, GCP, Azure) and MLOps practices",
      "Excellent problem-solving, analytical, and communication skills"
    ],
    "Responsibilities": [
      "You will lead a team of skilled engineers, mentor junior talent, and architect scalable ML systems",
      "Lead the design, development, and implementation of generative AI models (e.g., text-to-image, text generation, code generation)",
      "Architect and build scalable, production-grade machine learning pipelines and infrastructure",
      "Mentor and guide a team of machine learning engineers, fostering a collaborative and innovative environment",
      "Collaborate with product managers and researchers to translate business requirements into ML solutions",
      "Optimize model performance, efficiency, and robustness",
      "Stay abreast of the latest research and advancements in deep learning and generative AI",
      "Evaluate and integrate new technologies and tools to enhance the ML development process",
      "Ensure the responsible and ethical development of AI models",
      "Present technical findings and project updates to stakeholders",
      "Contribute to the strategic direction of the company's AI efforts"
    ]
  },
  "job_max_salary": null,
  "job_min_salary": null,
  "job_description": "Our client, a fast-growing innovator in artificial intelligence, is looking for a talented Lead Machine Learning Engineer to drive their cutting-edge Generative AI initiatives in Austin, Texas, US . This role offers the chance to work on groundbreaking projects at the forefront of AI, developing and deploying sophisticated models that push the boundaries of creativity and intelligence. You will lead a team of skilled engineers, mentor junior talent, and architect scalable ML systems. The ideal candidate will have a deep understanding of deep learning architectures, particularly transformers and diffusion models, and extensive experience in building and deploying production-ready ML solutions. You should possess strong leadership qualities, excellent communication skills, and a passion for solving complex problems with state-of-the-art AI techniques.\n\nKey Responsibilities:\nLead the design, development, and implementation of generative AI models (e.g., text-to-image, text generation, code generation). Architect and build scalable, production-grade machine learning pipelines and infrastructure. Mentor and guide a team of machine learning engineers, fostering a collaborative and innovative environment. Collaborate with product managers and researchers to translate business requirements into ML solutions. Optimize model performance, efficiency, and robustness. Stay abreast of the latest research and advancements in deep learning and generative AI. Evaluate and integrate new technologies and tools to enhance the ML development process. Ensure the responsible and ethical development of AI models. Present technical findings and project updates to stakeholders. Contribute to the strategic direction of the company's AI efforts. Qualifications:\nMaster's or PhD in Computer Science, Machine Learning, or a related quantitative field. Minimum of 6 years of experience in machine learning engineering, with a significant focus on deep learning and generative models. Proven experience leading ML projects and teams. Expertise in Python and ML frameworks such as TensorFlow, PyTorch, or JAX. Strong understanding of NLP, computer vision, and/or reinforcement learning. Experience with large-scale data processing and distributed training. Familiarity with cloud platforms (AWS, GCP, Azure) and MLOps practices. Excellent problem-solving, analytical, and communication skills. A portfolio of relevant projects or publications is a plus. This is an exceptional opportunity to make a significant impact in the rapidly evolving field of Generative AI and shape the future of intelligent systems. Join a team that is passionate about innovation and discovery in Austin, Texas, US .",
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  "employer_website": "https://en-ke.whatjobs.com",
  "job_onet_job_zone": "5",
  "job_salary_period": null,
  "job_apply_is_direct": false,
  "job_employment_type": "Full–time",
  "job_employment_types": [
    "FULLTIME"
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  "job_posted_at_timestamp": 1774569600,
  "job_posted_at_datetime_utc": "2026-03-27T00:00:00.000Z"
}