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Software Engineer (Language Modeling), BS+12 yrs

  • Columbia, MD, United States

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Job details

Experience
· 12+ years of experience in software engineering, with a focus on machine learning or natural language processing.
Posting language
English
Posted

Original job description

Description: We are seeking a highly skilled and motivated Sr. LLM Engineer to join our team in driving the advancement of our Language Model infrastructure. As a key member of our AI/ML team, you will be responsible for the training, hosting, and optimization of Large Language Model (LLM) instances within our compute environment. The ideal candidate should possess a strong passion for pushing the boundaries of language technology, a deep understanding of LLM architectures, and the grit to tackle complex challenges head-on. This role requires a self-reliant individual with a drive to identify and fix inefficiencies, constantly striving to improve the codebase and optimize model performance. If you thrive in a fast-paced environment and have an unwavering commitment to delivering cutting-edge language solutions, this position is for you.

Responsibilities:

· Design, develop, and maintain the infrastructure for training, hosting, and serving LLM instances. · Optimize model training pipelines to achieve high performance and resource efficiency. · Implement and integrate state-of-the-art LLM architectures and techniques. · Collaborate with cross-functional teams to understand business requirements and deliver impactful language solutions. · Monitor and analyze model performance metrics, identifying areas for improvement and implementing optimizations. · Develop and maintain documentation, best practices, and coding standards for LLM development and deployment. · Stay up-to-date with the latest advancements in LLM research and industry trends, and incorporate them into our projects. · Mentor and guide junior engineers, fostering a culture of continuous learning and knowledge sharing.

Skills Requirements:

· 12+ years of experience in software engineering, with a focus on machine learning or natural language processing. · Degree in Computer Science, Artificial Intelligence, or a related field. · Strong expertise in deep learning frameworks such as TensorFlow, PyTorch, or MXNet. · Proficiency in programming languages such as Python, C++, or Java. · Solid understanding of LLM architectures, training techniques, and evaluation methodologies. · Familiarity with cloud platforms (e.g., AWS, GCP) and their machine learning services. · Knowledge of software engineering best practices, including version control, testing, and continuous integration/deployment. · Excellent problem-solving and debugging skills. · Strong communication and collaboration abilities to work effectively with cross-functional teams.

Nice to Haves:

· Advanced degree (Master's or Ph.D.) in Computer Science, Artificial Intelligence, or a related field. · Proven track record of implementing and deploying large-scale LLM systems in production environments. · Experience with distributed computing frameworks like Apache Spark or Hadoop. · Experience with natural language understanding, generation, and dialogue systems. · Familiarity with techniques such as transfer learning, few-shot learning, and reinforcement learning. · Contributions to open-source projects or research publications in the field of LLMs. · Experience with serving models using APIs and building scalable inference pipelines. · Knowledge of DevOps practices and tools like Docker, Kubernetes, and Jenkins. YOE Requirement: 12 yrs., B.S. in a technical discipline or 4 additional yrs. in place of B.S.

More about this job

Skills

  • Documentation
  • Machine learning
  • Natural language processing
  • Computer science
  • Artificial intelligence
  • Deep learning
  • TensorFlow
  • PyTorch
  • Apache MXNet
  • Programming languages
  • Python
  • C++
  • Oracle Java
  • AWS
  • Google Cloud
  • CI/CD
  • Problem solving
  • Debugging
  • Communication skills
  • Collaboration
  • Distributed computing
  • Apache Spark
  • Apache Hadoop
  • Research
  • DevOps
  • Docker
  • Kubernetes
  • Jenkins

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