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About This Role
Description:
Call Box exists to grow people. We believe that everyone wants to become a better version of themselves, and we work hard to provide software and a culture that maximizes that growth mindset. We hire smart and ambitious doers in order to set them loose in an exciting and complex technology business where they will build, sell, and deploy Artificial Intelligence solutions that help our clients grow their bottom line.
We are building a high-performance, AI-native engineering organization. We have a small team with high output and autonomous execution. This individual contributor role involves shipping and owning your product. You will design features, write production code daily, and solve hard problems in real-time AI systems. With high autonomy you can have high impact.
We are building a conversational voice AI system for the automotive industry. The product processes real-time voice AI calls at scale, serving thousands of dealership customers. The technical challenges include latency, complex state management, AI engineering, and zero tolerance for failure on live customer calls.
What You'll Do:
Build
- Own your design, implementation, deployments, and live-site health.
- Write code that integrates across the full conversational AI stack: speech-to-text, textto-speech, Large Language Models (LLMs), and telephony systems.
- Optimize the real-time audio pipeline for low latency and high-quality results.
- Own the observability platform end to end: distributed tracing on self-hosted LangFuse, CloudWatch dashboards and alarms, and LLM cost attribution. We obsess over operational excellence, measuring what we ship with SLIs, SLOs, and high signal to-noise structured logs, and owning our outcomes.
- Build resilience into live-site systems: circuit breakers, multi-provider LLM failover, and retry and backoff strategies that degrade gracefully under partial failure.
- Design event-driven, cross-account AWS plumbing (EventBridge and SQS) that moves data reliably between services.
- Deliver Infrastructure as Code (IaC) with AWS CDK across containerized and serverless architectures, and own the release-engineering path: semantic-release, monorepo versioning, and cost-allocation tagging.
- Deliver fault-tolerant, highly available telephony integrations with comprehensive error handling.
- We are an AI-first engineering team, and we expect you to work fluently with AI coding agents as part of your daily workflow. Strong prompt design, context management, and optimization across LLM providers is a plus.
- We move quickly and confidently by investing in code quality, automated tests, and high-value code review feedback.
Lead
- Actively contribute to the team's systems design. We value elegance in simplicity and embrace complex technical decisions.
- Drive critical technical decisions that affect broad areas of the platform.
- Establish and enforce engineering standards, best practices, and architecture patterns.
- Lead initiatives for the team that improve engineering productivity, quality, and scalability.
- Design and implement scalable systems spanning both modern and legacy systems.
- Proactively identify and solve technical problems.
- Build maintainable, scalable, and business-aligned systems.
- Translate complex business needs into robust technical strategies.
- Apply a "first-team" mentality, meaning we win and lose as a team.
- Earn trust from others and keep it.
Teach
- Champion knowledge sharing via documentation, talks, and pairing sessions.
- Provide insightful code reviews that raise team quality. Invest in the success of other team members.
- Help engineers of all levels build leadership and technical skills.
- We value direct communication. No empty corporate-speak.
Requirements:
- CS degree (or demonstrated equivalent depth in algorithms, systems design, distributed computing) 5+ years building and scaling production systems
- Expert-level TypeScript and Node.js experience
- Strong AWS experience (ECS, Lambda, EventBridge, RDS/ElastiCache, SQS, CloudWatch, networking, IAM)
- Infrastructure as code with CDK
- CI/CD and release automation (semantic-release or equivalent)
- Production database experience (Postgres)
- Experience integrating with third-party systems such as dealership management systems (DMS), scheduling platforms, and CRMs, or comparable enterprise API integration experience
- Obsessive about testing, observability, and reliability
Our Ideal Candidates:
- Thrive on feedback, coaching, and continuous professional development.
- Are self-motivated, adaptable, and take a curious, strategic approach to problemsolving.
- Have excellent communication skills and a customer-first mindset.
- Have experience with LLM application development (prompt engineering, context management, latency optimization)
- Have experience with real-time systems (WebSockets, event-driven architectures)
- Have experience with high-throughput distributed systems and performance optimization under strict latency constraints
- Have experience with telephony/VoIP systems (SIP, RTP, audio codecs)
What's in It for You?
- Competitive salary package (immediate PTO).
- Full benefits package.
- Fidelity 401k with company match.
- Fun perks including a monthly gym reimbursement, a monthly wellness reimbursement, and a monthly reading allowance.
- Opportunities for continued career growth within the organization.
- Fun and collaborative work environment
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 8,943 AI roles we're tracking, AI Software Engineer positions make up 4% of the market. At Call Box, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,448 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Call Box AI Hiring
Call Box has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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.
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