Deep Learning Product Research Engineer

Quick Facts

• Role: Deep Learning Product Research Engineer (Generative AI)

Description

Lead product research for generative AI by evaluating emerging models, agent technology, reinforcement learning, and evaluation methods, then translating findings into what will make products succeed. Build proof-of-concept applications, benchmarks, and reference sample code, and turn customer/developer/benchmark signals into structured product intelligence and roadmap recommendations. Develop enterprise-ready enablement assets and reusable LLM evaluation tooling, and distill work into authoritative technical content such as code examples, write-ups, white papers, demos, and talks.

Responsibilities

• Evaluate emerging generative AI approaches (models, agents, RL, evaluation methods) and assess implications for products
• Build PoC applications, benchmarks, and reference sample code to validate capabilities and product value
• Produce product intelligence from signals: adoption trends, friction points, reproductions, and roadmap inputs
• Create enablement assets: reference architectures, integration playbooks, performance tuning recipes, and demo-to-production workflows
• Partner with research, engineering, product, technical marketing, field teams, and customers to shape feature requests and usability improvements
• Advance internal LLM expertise using evaluation harnesses, profiling utilities, and agentic workflows
• Publish technical assets including demos, talks, white papers, and patent filings where appropriate
• Stay current with advances across training, post-training, inference, agentic systems, evaluation, deployment, and safety

Requirements

• Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, Artificial Intelligence, or related field, or equivalent experience
• 5+ years of proven experience in software engineering, machine learning engineering, AI engineering, solutions architecture, applied research, or similar
• Hands-on experience with machine learning, deep learning, or agentic AI (build/train/fine-tune/evaluate/deploy/optimize models and AI applications)
• Practical generative AI experience with LLMs, RAG, agentic workflows, model evaluation, and/or AI application development
• Experience with Python and modern deep learning frameworks/libraries (e.g., PyTorch, Hugging Face Transformers, LangChain, LlamaIndex, TensorFlow)
• Familiarity with AI-assisted development tools and coding agents (e.g., Codex, Claude Code, Cursor)
• Ability to create clear, accurate, technically rigorous developer content (tutorials, blogs, sample code, white papers, benchmarks, demos)
• Strong communication and presentation skills

Benefits

• Comprehensive benefits package
• Base salary determined by location and experience; base range listed for Level 3 and Level 4
• Eligible for equity and benefits