Motion Recruitment
Principal / Senior Solutions Engineer – Top-of-Stack & Agentic AI
Motion Recruitment is hiring a Principal / Senior Solutions Engineer – Top-of-Stack & Agentic AI in x, California, United States. Posted September 12, 2026.
Job at a glance
- Company
- Motion Recruitment
- Location
- x, California, United States
- Pay
- $220,000 – $280,000
- Workplace
- On-site
- Sector
- Engineering & Technology
- Posted
- September 12, 2026
- Apply by
- November 16, 2026
About the job
Job Description We are seeking a Solutions Engineer – Agentic AI & Model Optimization for a Full-Time role based in the California. Our client is a well-capitalized, next-generation enterprise technology provider purpose-building advanced AI software systems, autonomous agent factories, and scalable inference/training optimization pipelines. Their engineering platform enables enterprise customers to build, fine-tune, optimize, and deploy high-impact AI workloads and multi-agent workflows into secure production environments. This is a premier opportunity to serve as the leading technical voice at the frontier of applied enterprise artificial intelligence. The #1 feature of this opportunity is the opportunity to deploy advanced model architectures and agentic frameworks backed by world-scale dedicated compute: you will partner with commercial leadership to architect autonomous agent systems, run model optimization evaluations, and demonstrate enterprise feasibility to Chief AI Officers and engineering leadership. We are seeking a researcher-grade or senior systems technologist with deep foundational AI knowledge (dating beyond recent trends) who combines hands-on engineering depth with consultative, customer-facing polish and leadership ambition. In exchange, you will work on state-of-the-art problems with massive computing support in a high-autonomy, growth-focused culture. Required Skills & Experience · 5+ years of combined experience in applied AI engineering, systems engineering, or technical pre-sales/solutions architecture with enterprise customers.
· Deep theoretical and practical knowledge of model architectures, transformer fundamentals, model fine-tuning, and inference/training optimization techniques (e.g., quantization, model parallelism, memory optimization).
· Hands-on experience designing or deploying autonomous agent frameworks, multi-agent orchestration, tool use, and structured workflow automation.
· Strong programming proficiency in Python and modern deep learning frameworks (PyTorch, Hugging Face, TensorRT-LLM, vLLM, or equivalent runtimes).
· Demonstrated customer-facing acumen with the ability to run technical discovery, design proofs-of-value (PoVs), and effectively present complex AI concepts to executive and research audiences. Desired Skills & Experience · Academic or commercial research background in machine learning, natural language processing, or autonomous systems prior to recent generative AI cycles.
· Experience addressing enterprise concerns around data privacy, secure model inference, deterministic agent behavior, and evaluation benchmarks.
· Prior experience at an AI-native startup, enterprise AI software vendor, or frontier applied AI organization.
· Master’s or Ph.D. in Computer Science, Machine Learning, Computational Mathematics, or related field, or equivalent practical experience. What You Will Be Doing Tech Breakdown
· 40% Agentic AI Systems, Multi-Agent Orchestration & Workflow Design
· 35% Inference & Training Optimization (Quantization, Runtimes, Model Serving)
· 25% Applied Model Development, Evaluation & Tool Integration Daily Responsibilities
· 45% Customer Architecture Discovery, PoV Scoping & Executive Briefings
· 35% Hands-On Technical Solution Design, Prototype Validation & Technical Proposals
· 20% Team Collaboration & Research/Product Roadmap Alignment The Offer • Commission eligible You will receive the following benefits:
· Medical Insurance
· Dental Benefits
· Vision Benefits
· Paid Time Off (PTO) Applicants must be currently authorized to work in the US on a full-time basis now and in the future.