Senior Software Engineer, Agent

PikaPalo Alto, CA
23hOnsite

About The Position

We're looking for a Senior Agent Engineer to push the boundaries of what AI agents can do at Pika. You'll work on the systems that give AI agents their personality, memory, reasoning, multi-modal capabilities, and ability to act autonomously across platforms. This is the core of what makes Pika's AI products feel alive. You'll design agent architectures, build tool-use frameworks, optimize LLM interactions, and create the systems that allow agents to learn, remember, and evolve over time. If you're excited about building AI systems that feel genuinely intelligent — not just wrappers over chat APIs — this role is for you.

Requirements

  • 5+ years of software engineering experience, with 2+ years working with LLMs or AI agent systems
  • Deep understanding of LLM capabilities and limitations — you know how to get the best out of frontier models
  • Experience building agent systems — tool use, function calling, multi-step reasoning, retrieval-augmented generation (RAG)
  • Strong prompt engineering skills — system prompts, few-shot learning, chain-of-thought, structured output
  • Proficiency in TypeScript and/or Python
  • Understanding of embedding models and vector search for memory and retrieval
  • Comfortable with rapid experimentation — you ship experiments, measure results, and iterate
  • Product intuition — you understand what makes an AI agent feel "alive" vs. robotic
  • Clear communication skills and a team-first mindset

Nice To Haves

  • Experience with multi-modal AI (image generation, TTS, speech-to-text, video generation)
  • Experience with agent frameworks (LangChain, AutoGPT, CrewAI, or custom runtimes)
  • Background in NLP, computational linguistics, or cognitive science
  • Experience with fine-tuning, RLHF, or DPO
  • Familiarity with AI safety and alignment considerations
  • Experience with real-time/streaming LLM responses
  • Previous startup experience — comfortable with ambiguity and moving fast

Responsibilities

  • Design and evolve the agent runtime — the core loop that handles reasoning, tool use, memory retrieval, and response generation
  • Build agent capabilities — image generation, voice synthesis, video creation, web browsing, code execution, and other skills
  • Optimize LLM integration — prompt engineering, context window management, multi-provider model routing (Claude, GPT, Gemini, open-source), cost optimization, and latency reduction
  • Implement memory systems — long-term memory, working memory, episodic recall, and semantic search so agents learn from every interaction
  • Design autonomous behaviors — proactive actions, scheduled tasks, and goal-directed behavior that makes agents feel self-directed
  • Build evaluation and quality systems — benchmarks, A/B testing, and metrics for agent behavior, personality consistency, and response quality
  • Experiment with new architectures — multi-agent collaboration, planning, chain-of-thought reasoning, and other emerging patterns
  • Collaborate with product and design to translate AI capabilities into intuitive user experiences
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