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Machine Learning Engineer - LLMs & Generative AI Jobs

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Senior Machine Learning Engineer, Generative AI Products
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Lead the development of cutting-edge Generative AI products in Boston. This senior role requires 5+ years of Python/SQL experience and 3+ years deploying NLP/deep learning models in the cloud. You will architect GenAI applications, build advanced workflows, and establish model guardrails. Enjoy t...
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United States , Boston
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Not provided
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HBS
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Until further notice
Explore cutting-edge careers at the intersection of artificial intelligence and language with Machine Learning Engineer jobs focused on Large Language Models (LLMs) and Generative AI. This specialized profession sits at the forefront of AI development, where engineers design, build, and deploy sophisticated systems capable of understanding, generating, and manipulating human language and creative content. Professionals in this field are the architects behind technologies like advanced chatbots, AI assistants, content creation tools, and complex reasoning systems, transforming how humans interact with machines. A typical Machine Learning Engineer in the LLM and Generative AI domain is responsible for the entire model lifecycle. This begins with researching and adapting state-of-the-art architectures, followed by the large-scale training of models on massive datasets. A core duty is efficient model fine-tuning and alignment, ensuring outputs are helpful, harmless, and honest. Engineers optimize these models for deployment, tackling challenges in inference speed, scalability, and cost-efficiency. They implement robust evaluation frameworks to rigorously assess model performance across metrics like accuracy, bias, and safety. Furthermore, they build and maintain the MLOps pipelines—encompassing data processing, versioning, monitoring, and continuous integration—that are essential for production AI systems. To succeed in these highly sought-after jobs, a strong foundation in computer science, mathematics, and deep learning is non-negotiable. Proficiency in Python and frameworks like PyTorch, TensorFlow, and Hugging Face libraries is standard. Deep expertise in transformer architectures, attention mechanisms, and distributed training techniques is crucial. Practical skills in cloud platforms (AWS, GCP, Azure), containerization (Docker, Kubernetes), and software engineering best practices are required to build scalable solutions. Beyond technical prowess, effective professionals possess problem-solving creativity, a keen understanding of ethical AI implications, and the ability to collaborate with cross-functional teams including researchers, data scientists, and product managers. For those passionate about shaping the next generation of intelligent systems, Machine Learning Engineer jobs in LLMs and Generative AI offer a dynamic and impactful career path, constantly evolving with the rapid pace of innovation in artificial intelligence.

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