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Full Time Llm Engineer Jobs (NOW HIRING)

Netflix

OR · On-site

$466K - $750K/yr

... Research Engineer to research, develop, and iterate on LLM prototypes to improve Netflix ... Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation ...

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Full Time Llm Engineer information

What are the key skills and qualifications needed to thrive as a Full Time LLM Engineer, and why are they important?

To thrive as a Full Time LLM Engineer, you need a strong background in computer science, machine learning, and natural language processing, often supported by a relevant degree and experience with large language models. Familiarity with frameworks such as PyTorch or TensorFlow, version control systems like Git, and experience deploying models using cloud platforms or MLOps tools are typically required. Strong problem-solving, collaboration, and communication skills help you work effectively within cross-functional teams and adapt to evolving technologies. These skills ensure the design, optimization, and deployment of robust language models that meet real-world application needs.

What are some common challenges faced by Full Time LLM Engineers when deploying large language models in production environments?

Full Time LLM Engineers often encounter challenges related to optimizing model performance, managing infrastructure costs, and ensuring reliable scaling in production. Handling inference speed and latency is critical, especially when integrating with real-time applications. Additionally, monitoring model behavior for biases, drift, and security vulnerabilities requires ongoing collaboration with data scientists and operations teams. Staying updated on the latest advancements and tools in the LLM landscape is essential for maintaining effective and efficient deployments.

What are Full Time LLM Engineers?

Full Time LLM Engineers are professionals who specialize in developing, fine-tuning, and deploying large language models (LLMs) like GPT, BERT, or similar AI models. They work with machine learning frameworks, manage data pipelines, and optimize model performance for various applications such as chatbots, content generation, and natural language processing tasks. These engineers often collaborate with data scientists, product teams, and software engineers to integrate LLMs into products and services. Their responsibilities may also include monitoring model outputs, ensuring ethical AI use, and staying updated with advancements in the field.

What is the difference between Full Time Llm Engineer vs Machine Learning Engineer?

AspectFull Time Llm EngineerMachine Learning Engineer
Required CredentialsAdvanced degree in law, computer science, or related fields; knowledge of legal data and NLPDegree in computer science, data science, or related fields; expertise in algorithms and data modeling
Work EnvironmentLegal tech companies, AI firms focusing on legal applications, research institutionsTech companies, startups, research labs working on AI and data-driven solutions
Employer & Industry UsageLegal industry, AI legal tools, compliance firmsTechnology industry, AI product development, data-driven applications

While both roles involve AI and data, a Full Time Llm Engineer specializes in legal language models and legal data, whereas a Machine Learning Engineer works broadly across various AI applications and industries. The Llm Engineer focuses on legal-specific NLP tasks, requiring legal knowledge combined with AI skills, while the Machine Learning Engineer has a broader scope in AI development across sectors.

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Full Time Llm Engineer jobs near you
Infographic showing various Full Time Llm Engineer job openings in the United States as of June 2026, with employment types broken down into 26% Full Time, 61% Part Time, and 13% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution.

LLM Inference Frameworks and Optimization Engineer

Together AI

San Francisco, CA • On-site

$160K - $230K/yr

Full-time

Medical

Posted 23 days ago

Job description

About the Role
At Together.ai, we are building state-of-the-art infrastructure to enable efficient and scalable inference for large language models (LLMs). Our mission is to optimize inference frameworks, algorithms, and infrastructure, pushing the boundaries of performance, scalability, and cost-efficiency.
We are seeking anInference Frameworks and Optimization Engineer to design, develop, and optimize distributed inference engines that support multimodal and language models at scale. This role will focus on low-latency, high-throughput inference, GPU/accelerator optimizations, and software-hardware co-design, ensuring efficient large-scale deployment of LLMs and vision models.
This role offers a unique opportunity to shape the future of LLM inference infrastructure, ensuring scalable, high-performance AI deployment across a diverse range of applications. If you're passionate about pushing the boundaries of AI inference, we'd love to hear from you!
Responsibilities
Inference Framework Development and Optimization
  • Design and develop fault-tolerant, high-concurrency distributed inference engine for text, image, and multimodal generation models.
  • Implement and optimize distributed inference strategies, including Mixture of Experts (MoE) parallelism, tensor parallelism, pipeline parallelism for high-performance serving.
  • Apply CUDA graph optimizations, TensorRT/TRT-LLM graph optimizations, and PyTorch-based compilation (torch.compile), and speculative decoding to enhance efficiency and scalability.
Software-Hardware Co-Design and AI Infrastructure
  • Collaborate with hardware teams on performance bottleneck analysis, co-optimize inference performance for GPUs, TPUs, or custom accelerators.
  • Work closely with AI researchers and infrastructure engineers to develop efficient model execution plans and optimize E2E model serving pipelines.
Requirements
Must-Have:
  • Experience:
    • 3+ years of experience in deep learning inference frameworks, distributed systems, or high-performance computing.
  • Technical Skills:
    • Familiar with at least one LLM inference frameworks (e.g., TensorRT-LLM, vLLM, SGLang, TGI(Text Generation Inference)).
    • Background knowledge and experience in at least one of the following: GPU programming (CUDA/Triton/TensorRT), compiler, model quantization, and GPU cluster scheduling.
    • Deep understanding of KV cache systems like Mooncake, PagedAttention, or custom in-house variants.
  • Programming:
    • Proficient in Python and C++/CUDA for high-performance deep learning inference.
  • Optimization Techniques:
    • Deep understanding of Transformer architectures and LLM/VLM/Diffusion model optimization.
    • Knowledge of inference optimization, such as workload scheduling, CUDA graph, compiled, efficient kernels
  • Soft Skills:
    • Strong analytical problem-solving skills with a performance-driven mindset.
    • Excellent collaboration and communication skills across teams.

Nice-to-Have:
  • Experience in developing software systems for large-scale data center networks with RDMA/RoCE
  • Familiar with distributed filesystem(e.g., 3FS, HDFS, Ceph)
  • Familiar with open source distributed scheduling/orchestration frameworks, such as Kubernetes (K8S)
  • Contributions to open-source deep learning inference projects.

About Together AI
Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.
Compensation
We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $160,000 - $230,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.
Equal Opportunity
Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.
Please see our privacy policy at https://www.together.ai/privacy

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