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About This Role
WHO WE ARE
At Basis, we empower agencies and brands with cutting-edge software that automates digital media operations. Our all-in-one platform supports seamless planning, reporting, and financial reconciliation across direct, programmatic, search, and social media.
We’re not just building advanced technology; we’re also building a culture where passionate, motivated individuals come together to drive meaningful change. Our commitment to inclusivity and growth ensures our employees thrive both professionally and personally.
With our headquarters in Chicago, we offer flexible work options across the U.S. and Canada, including remote, hybrid, or on-site at headquarters.
ABOUT THE TEAM
Technology is at the core of what we do. The AI Core team at Basis Technologies builds Compass, the AI agent platform behind Basis. Compass agents plan and execute media planning, buying, and operations work on behalf of agencies and brands. We build the orchestration, tooling, and evaluation infrastructure that lets LLM-powered agents work reliably in a complex ad tech domain, and we expose the Basis platform to our own and our customers’ agents through MCP and A2A. The AI Core team also functions as an enabling team for all of Basis Engineering, developing the patterns and best practices for agentic and AI-assisted development that other teams build on.
WAYS YOU’LL CONTRIBUTE
As a Staff AI Engineer, you will be the technical lead for the AI Core team, specializing in the architecture and development of our agentic platform. Other ways you will contribute:
- Set the technical direction for Compass and our LLM-powered features. Architect multi-agent systems, own the major design decisions, and balance rapid experimentation with stable, scalable long-term architecture.
- Lead the design and evolution of the MCP servers, A2A integrations, and APIs that connect internal and external agents to the Basis media buying toolkit.
- Own agent orchestration, evaluation, and observability. Define how we measure agent quality and ensure agents reason, plan, and execute reliably in production. Own our software end to end, including production support for Compass.
- Serve as the team’s primary technical point of contact for Product, QA, Design, Operations, other Engineering teams, and relevant stakeholders. Represent the team in cross-team planning, architecture discussions, and RFCs.
- Drive large-scale technical projects that span multiple teams and systems, from ideation through production. Own team deliverables, decisions, quality, process, and technical direction. Develop a deep understanding of our business domain and stakeholder needs, translate business goals into realistic technical plans, and proactively identify risks and blockers.
- Own the team’s technical quality bar through design and code reviews. Onboard new engineers, mentor senior and mid-level engineers, and set a positive example on how to collaborate in pull requests, demos, and working relationships.
- Lead the adoption of AI-assisted and agentic development across Basis Engineering by establishing best practices and building the examples and infrastructure other teams adopt.
- Embody Basis’ core principles while fostering a positive culture within the team. Actively promote Basis in the community.
WHAT YOU BRING TO THE TABLE
- Minimum of 8 years of professional software engineering experience, including recent experience shipping production LLM-powered applications or agent systems.
- A track record of technical leadership, including owning the technical direction of a team or major system and influencing architecture beyond your immediate area of ownership.
- Expert-level proficiency in Python, with a history of architecting production APIs and backend services and integrating with protocols such as MCP and A2A.
- Deep experience building and operating production-grade agentic systems with frameworks such as Strands, LangChain, or the Claude Agent SDK, including asynchronous and event-driven patterns, prompt engineering, structured outputs, and techniques for improving agent reliability. Sound judgment on when to optimize for speed and when to invest in quality.
- Experience with evaluation and observability tools such as Langfuse or LangSmith, including designing evaluation strategies from the ground up.
- Fluency with AI coding tools (Claude Code, Cursor, Codex, etc.) as a core part of the development workflow, paired with strong judgment and full ownership of AI-generated output.
- Proven ability to mentor engineers and to explain complex agentic systems clearly to engineers, Product and Operations partners, and non-technical stakeholders.
- Thoughtful approach to collaboration, design, and decision-making that prioritizes equity, access, and continuous learning, and a commitment to creating inclusive, respectful environments where all voices are valued and supported.
BONUS POINTS
- Bachelor’s or advanced degree in Computer Science, Engineering, or a related field
- Prior experience in ad tech, media planning systems, or programmatic advertising platforms.
- Hands-on experience with AWS Bedrock, including Bedrock AgentCore (Runtime, Gateway, Identity).
- Experience designing MCP tool sets or skills libraries adopted by multiple teams or external partners.
- Experience with authentication and authorization for non-human actors, such as OAuth 2.0 flows for agents and token vaulting for third-party integrations.
- Experience with RAG systems, vector databases, or knowledge retrieval approaches.
OUR TECH STACK
- Languages: Python
- Frameworks: LangChain, LangGraph, FastMCP, FastAPI
- Protocols & Integrations: MCP, A2A, AG-UI, REST APIs
- Evaluation & Observability: Langfuse, custom evaluation frameworks
- Data: Snowflake, S3
- Infrastructure: AWS, AWS Bedrock (including Bedrock AgentCore)
This salary range reflects a typical compensation range for the role and level in most U.S. locations. Final compensation offered will depend on your location, experience, and skills. Total compensation may also include bonuses, equity, and competitive benefits.Integrity in Hiring At Basis, integrity is at the core of how we hire. To protect our candidates and our company, we verify identity, education, and employment history for all hires. Some roles may also require onsite or in-person interviews as part of the process. Any misrepresentation of identity, experience, or skills may result in removal from the hiring process or termination of employment. We value transparency and authenticity and expect the same from every applicant. Anything Else? Don’t have every skill listed? No problem! We know experience can be built in many ways. If you have relevant skills that aren't reflected in your resume, we encourage you to share them in an optional cover letter.
Life With BasisWe’re committed to our people’s growth and well-being because our success is tied to theirs. That’s why we’ve earned recognition as a top workplace, including: Ad Age, #1 Best Places to Work, WorkLife's #1 Most Committed to Work Life Balance, and the Chicago Tribune's Top Workplaces in Chicago. We offer competitive perks, including a flexible work week, 401k/RRSP matching, mental health support, paid sabbaticals, generous parental leave, flexible work options, and more.
Basis is proud to be an equal-opportunity employer. We celebrate all team members regardless of gender identity, sexual orientation, race or cultural background, religion, disability, age, and beyond.
If you need assistance with interview accessibility, please contact [email protected]. Your privacy is important to us, view our policy here.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $136K-$185K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $192K across 198 roles with salary data).
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 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Basis, 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 1,601 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($160K) sits 27% below the category median. Disclosed range: $136K to $185K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Basis AI Hiring
Basis has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Remote, US, New York, NY. Compensation range: $185K - $185K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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