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Lenovo · 1 week ago

Associate AI Engineer

North Carolina, United States

Full-time

Onsite

New Grad, Entry Level

Lenovo is a global technology powerhouse focused on delivering Smarter Technology for All. They are seeking a motivated AI Engineer to design, develop, and explore next-generation AI systems, working closely with teams to advance Lenovo's Hybrid AI vision.

ComputerConsumer ElectronicsElectronicsHardwareMobileWearables

No H1B

Responsibilities

Build agentic AI workflows Design and implement multi-step AI agents that can plan, call tools/APIs, reason over intermediate results, and complete complex tasks end-to-end

Develop orchestration layers Implement services that route and coordinate calls between foundation models, tools, data sources, and other agents (e.g., planners, executors, evaluators)

Design context and memory management Help design and implement strategies for managing context and “memory” across interactions (e.g., chunking, retrieval, long-context handling, and stateful workflows), so agents can work over large and evolving information

Prototype and productionize Take ideas from notebook-level prototypes to robust, observable, production-ready systems, including APIs, services, and internal tools

Work with foundation models Fine-tune, adapt, and post-train large models (e.g., instruction-tuning, RAG, tool-use finetuning, preference optimization) in collaboration with senior ML engineers

Experiment and evaluate Design experiments, build evaluation harnesses, and analyze metrics to compare different prompts, policies, and agent strategies

Engineer for reliability and safety Contribute to guardrails, monitoring, and fallback strategies so agentic systems behave predictably and safely in real user environments

Collaborate across disciplines Work closely with product, research, and platform teams to understand real use cases and ship features that solve concrete user problems

Continuously learn new tools frameworks Ramp quickly on new agent frameworks, libraries, and infrastructure as the ecosystem evolves

Qualification

PythonMachine LearningDeep LearningML FrameworksLarge Language ModelsAnalytical SkillsDebugging SkillsCommunication SkillsTeam Collaboration

Required

Bachelor's degree in Computer Science, Computer Engineering, or a closely related technical field (e.g., EE, Math, Statistics, Physics) with a strong computing/programming focus

Strong programming experience in Python

Solid CS fundamentals: data structures, algorithms, complexity, concurrency

Experience building and debugging non-trivial software systems (class projects, internships, research code, or open-source)

Coursework or project experience in machine learning or deep learning (e.g., supervised learning, optimization, neural networks)

Familiarity with at least one modern ML framework such as PyTorch, TensorFlow, or JAX

Conceptual understanding of large language models (LLMs) or other foundation models (what they are, common use cases, limitations)

Experience using LLMs via APIs (e.g., OpenAI, Anthropic, etc.) or open-source models for a project, internship, or research

Demonstrated ability to quickly learn new tools, libraries, and frameworks

Strong analytical and debugging skills; comfortable working with incomplete or noisy real-world data

Clear written and verbal communication skills

Ability to work effectively in a team setting: code reviews, design discussions, and cross-functional collaboration

This position is NOT ELIGIBLE FOR VISA SPONSORSHIP, including Optional Practical Training (OPT) or Curricular Practical Training (CPT). All applicants must be currently authorized to work in the United States for any employer

Preferred

Projects involving agents, tool use, RAG, or workflow/orchestration frameworks (e.g., LangChain, LlamaIndex, custom planners/executors)

Exposure to context and memory mechanisms: retrieval/vector stores, long-context handling, or session/state management for LLM applications

Experience with foundation model fine-tuning or parameter-efficient methods (e.g., LoRA), or post-training techniques (e.g., instruction-tuning, preference optimization)

Backend experience: building APIs/services, working with cloud platforms and containerization (e.g., Docker), and basic MLOps (logging, monitoring, experiment tracking)

Notable ML/AI projects, open-source contributions, or research in ML, NLP, RL, or systems for ML