Interested in this AI Agent Developer role at Saga Legal AI?
Apply Now →About This Role
### About Saga AI Labs
Saga is building a new category: AI\-native characters that form synthetic relationships with people across digital platforms.
Agents on our B2B2C platform appear as AI\-native characters across social platforms, where they create content, respond to fans, build ongoing relationships, and drive measurable outcomes \- from community growth and user acquisition to downloads, in\-game purchases, subscriptions, sales, and long\-term engagement.
We believe the next generation of brand and game interaction will not be static content or one\-off campaigns. It will be persistent AI characters that know their audience, remember context, evolve over time, and move across social, games, and interactive worlds.
We are an early\-stage team building with design partners in gaming, entertainment, and consumer brands. The product is moving from social\-first AI character deployments toward a repeatable platform that brands and studios can launch, manage, measure, and scale.
### The Role
We’re looking for a Head of Product to define and scale Saga’s character AI platform—starting with social\-first agents and expanding into multi\-platform experiences.
You’ll operate at the intersection of:
- AI systems (LLMs, agents, memory, evals)
- Social products (engagement loops, identity, relationships)
- Interactive experiences (games, persistent worlds)
- Developer platforms (APIs, tools, distribution)
- B2B SaaS and enterprise workflows for brands, studios, marketers, and growth teams
- Trust, safety, compliance, moderation, and brand governance for AI agents operating at scale
- Growth, user acquisition, campaign performance, conversion funnels, and measurable customer ROI
You’ll help answer a core question: *What makes an AI character feel like someone, not something—and how does that scale?*
You will also help answer the commercial version of that question: How do AI\-native characters safely create relationships that drive measurable business outcomes for studios and consumer brands?
You will own Saga’s product strategy across the company, own the product roadmap, report to the CEO, and partner closely with engineering, design, GTM, customer success, and external design partners. Over time, you will help build the product operating system and product function for the company.
### What You’ll Do
#### 1\. Define relationship\-first product strategy
- Design how AI characters form and maintain ongoing relationships with users
- Shape interaction models across social platforms (content, replies, DMs, presence)
- Identify what drives retention, attachment, and repeat engagement
- Translate relationship quality into measurable customer outcomes: engagement, community growth, user acquisition, conversions, revenue impact, and long\-term fan value
- Define which use cases Saga should serve first across brand marketing, gaming, social engagement, and UA/growth teams
### 2\. Build AI\-native character systems
- Own core primitives: memory, personality, continuity, voice
- Design how characters evolve over time and across contexts
- Partner with engineering on agent architecture and iteration cycles
- Turn loose AI behavior into scalable product primitives that customers can configure, evaluate, and trust
- Define how character memory, persona, tone, goals, boundaries, and campaign context should work across channels
### 3\. Drive product iteration \& evaluation
- Define what “good” looks like for a relationship with an AI character
- Build evaluation frameworks combining:
+ engagement metrics (retention, depth of interaction)
+ qualitative signals (believability, emotional resonance)
- Rapidly iterate based on real user behavior
- Build the measurement stack for character quality, commercial performance, and customer ROI
- Define and improve KPIs including conversation depth, repeat interaction, retention, campaign performance, conversion funnels, downloads, purchases, subscriptions, and cost\-effective UA
- Create experimentation and analytics systems that show which characters, behaviors, campaigns, and channels are working
### 4\. Lead social \& content dynamics
- Shape how characters behave on platforms (posting, responding, initiating)
- Balance content creation vs. interaction
- Understand and leverage platform\-specific dynamics (e.g., feed vs. DM vs. comments)
- Define platform\-specific playbooks for how agents should acquire attention, deepen relationships, route users toward actions, and avoid off\-brand or unsafe behavior
- Partner with customers and internal teams on campaign strategy, content loops, creator/IP dynamics, and social distribution mechanics
### 5\. Evolve into a multi\-platform system
- Translate social\-first learnings into game and interactive environments
- Define how characters persist across surfaces and contexts
- Help build a platform for studios and developers over time
- Define how agents move from social surfaces into games, websites, communities, apps, and interactive worlds while preserving memory, identity, and user context
- Develop the product path from bespoke deployments to reusable platform capabilities, APIs, integrations, and customer self\-serve workflows
### 6\. Build the brand and studio platform
- Own the product layer that allows customers to configure, launch, monitor, and improve AI characters across channels.
- Build customer\-facing tools for persona design, campaign goals, memory controls, approval workflows, analytics, safety settings, moderation, and performance optimization.
- Define the platform primitives needed for brand marketers, game studios, and UA/growth teams to deploy agents without requiring bespoke engineering every time.
- Develop workflows for campaign setup, content review, escalation, reporting, permissions, auditability, and customer success operations.
- Help the company move from high\-touch service delivery to a scalable B2B product, with a path toward enterprise\-grade capabilities.
7\. Own trust, safety, and governance
- Design the product systems that allow AI agents to represent brands safely at scale.
- Own product requirements for brand safety, user safety, moderation, escalation, disclosure, privacy, memory retention, consent, auditability, and compliance.
- Partner with engineering, legal, policy, and customers to reduce hallucinations, off\-brand behavior, manipulation risk, regulatory exposure, and platform policy violations.
- Build human\-in\-the\-loop controls, review systems, safety evals, guardrails, and monitoring workflows that customers can trust.
- Make trust, safety, and compliance core parts of the product offering \- not afterthoughts.
8\. Lead customer discovery and platformization
- Work directly with design partners, brands, game studios, marketers, and UA/growth teams to identify repeatable use cases.
- Support pilots and early enterprise conversations by clarifying customer needs, product gaps, buying objections, and success criteria.
- Translate customer\-specific deployments into reusable platform primitives, roadmap priorities, and product requirements.
- Make hard tradeoffs between custom work, managed service delivery, and scalable platform investments.
9\. Own product strategy, roadmap, and operating cadence
- Own Saga’s product roadmap and company\-wide product strategy in partnership with the CEO and founders.
- Create the product operating cadence for prioritization, discovery, delivery, experimentation, measurement, and post\-launch learning.
- Bring structure to an early\-stage company without slowing down the speed required to win.
- Over time, help hire and lead product, design, research, and analytics talent as the company scales.
Who You Are
---------------
### You understand social products deeply
- Experience with consumer social, UGC, or engagement\-driven platforms
- Strong intuition for what drives retention, attachment, and habit formation
### You’ve built AI\-native products
- Experience with LLMs, agents, or conversational systems
- Comfortable working with model behavior, prompting, evals, and iteration loops
### You have taste for “relationship dynamics”
- You notice when interactions feel authentic vs. mechanical
- You think about tone, timing, memory, and emotional continuity
- You care about how users *feel*—not just what they do
### You think in systems
- Naturally reason about memory, state, constraints, and failure modes
- Can turn messy AI behavior into structured, scalable primitives
### You’re technical enough to go deep
- Can partner closely with engineering on architecture and tradeoffs
- Bonus: prior engineering background or highly technical PM experience
### You thrive in early\-stage environments
- Comfortable with ambiguity and fast iteration
- Can add structure without killing speed
### You are a growth/product/business operator, not just a character AI thinker
- ### You know how to connect user behavior, product quality, customer value, and revenue impact.
- ### You are comfortable owning KPIs such as engagement, retention, UA efficiency, conversion rate, campaign ROI, downloads, purchases, subscriptions, and customer expansion.
- ### You can balance product vision with the discipline required to build a B2B platform customers will buy, trust, and renew.
### You understand B2B customers and enterprise buying dynamics
- ### You can work with brand marketers, game studios, UA/growth teams, community teams, and executive stakeholders.
- ### You understand the difference between a compelling demo, a successful pilot, and a repeatable product customers can adopt at scale.
- ### You can support customer discovery, pilot design, roadmap tradeoffs, pricing/packaging input, and GTM learning.
### Bottom line: You’re fascinated by how people form attachments—to creators, characters, or even ideas—and you’ve thought deeply about how that translates into product
Must\-Have Experience
-------------------------
- Built AI\-native, data\-driven, technical, or highly ambiguous products.
- Experience at social platforms (TikTok, Instagram, Reddit, etc.).
- Strong product judgment around user behavior, engagement loops, retention, and growth.
- Ability to work deeply with engineering on systems, architecture, tradeoffs, evals, and constraints.
- Experience translating customer needs into platform primitives and repeatable workflows.
- Ability to operate in a zero\-to\-one startup environment where strategy, execution, customer learning, and product process all need to happen at once.
Nice\-to\-Have Experience
=============================
- Enterprise SaaS, marketing technology, customer engagement, CRM, community, or growth platforms.
- AI companions, chatbots, character AI, conversational agents, or agentic systems.
- Gaming, interactive storytelling, game publishing, community management, UA, or live operations.
- Consumer social platforms, creator economy, UGC, influencer marketing, or social distribution.
- Developer tools, APIs, integrations, analytics platforms, or self\-serve customer tooling.
Bonus (but not required)
----------------------------
- Experience with AI companions, chatbots, or character\-based products
- Background in gaming or interactive storytelling
- Founder or early\-stage startup experience
- Experience with brand marketing, game publishing, UA/growth, customer engagement, or enterprise SaaS
- Experience building analytics, attribution, experimentation, moderation, or compliance products
What Success Looks Like (First 6 Months)
--------------------------------------------
- Clear definition of what makes a Saga character compelling on social platforms
- Measurable improvements in user engagement and relationship depth
- Stronger consistency and continuity in character behavior
- Early frameworks for cross\-platform character persistence
- Foundations of a repeatable system for launching new characters
- A clear product strategy for Saga’s social\-first AI agent platform.
- A prioritized roadmap balancing customer deployments, platform primitives, AI quality, safety, and commercial outcomes.
- Live characters showing measurable improvement in engagement, retention, conversation depth, UA, conversion, downloads, purchases, subscriptions, or customer ROI.
- A repeatable framework for defining character quality, relationship depth, safety, compliance, and business impact.
- Customer\-facing workflows for configuring, launching, monitoring, and improving AI characters.
- Stronger reliability, consistency, moderation, and brand safety across deployed agents.
- A clear path from bespoke customer work to scalable platform product.
Role Details
About This Role
AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.
Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.
Across the 3,824 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Saga Legal AI, this role fits into their broader AI and engineering organization.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
What the Work Looks Like
A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
Skills in Demand for This Role
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?
Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
Compensation Benchmarks
AI Agent Developer roles pay a median of $252,000 based on 90 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Saga Legal AI AI Hiring
Saga Legal AI has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Based in Los Altos, CA, US.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.
Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.
What to Expect in Interviews
Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.
When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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