- Company Name
- Texas Instruments
- Job Title
- Machine Learning Research Engineer
- Job Description
-
Job Title: Machine Learning Research Engineer
Role Summary: Lead research and development of large language models (LLM) for embedded edge AI solutions. Drive innovation in LLM architecture, reasoning, agentic capabilities, and integration with domain knowledge to deliver business‑impactful AI systems.
Expectations: Deliver novel, high‑performing LLM solutions that advance state‑of‑the‑art and provide measurable business value. Provide technical leadership, publish research findings, and collaborate cross‑functionally to integrate models into production systems.
Key Responsibilities:
- Design and implement next‑generation LLM architectures, training pipelines, and post‑training methods (e.g., PPO, DPO, GRPO).
- Develop advanced reasoning models integrating domain knowledge and tool access for automated task execution.
- Lead research in code‑generation, evaluation, and optimization using LLMs.
- Build and deploy agentic LLM systems for complex, multi‑step task automation on edge devices.
- Collaborate with software engineering, system design, and product teams to define AI/ML solution requirements and integrate models into production.
- Conduct performance evaluation, benchmarking, and continuous improvement of LLM systems.
Required Skills:
- PhD in Computer Science, Electrical Engineering, or related field.
- Minimum 3 years of research or industry experience in NLP, LLMs, and deep learning.
- Proficiency in Python and C/C++ with strong software engineering fundamentals (debugging, performance profiling, optimization).
- Deep knowledge of transformer‑based LLM architectures and decoder‑only/encoder‑decoder models.
- Hands‑on experience with PyTorch, JAX, ONNX, and LLM libraries such as Hugging Face Transformers, trl, and vllm.
- Expertise in tokenization, embeddings, few‑shot/fine‑tuning, reinforcement learning for LLMs, and post‑training techniques (PPO, DPO, GRPO).
- Experience building LLM agents with tool‑calling capabilities.
- Solid understanding of text processing pipelines and domain‑specific knowledge integration.
- Excellent communication and collaboration skills for distributed teams.
Required Education & Certifications:
- Doctoral degree (PhD) in Computer Science, Electrical Engineering, Electrical and Computer Engineering, or equivalent.
- No mandatory certifications; relevant advanced coursework and research publications are highly valued.