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About This Role
Synack's Penetration Testing as a Service platform manages customers' attack surfaces by discovering new assets, pentesting for critical vulnerabilities and gaining visibility into the root causes of security risks. We are committed to making the world more secure by harnessing a talented, vetted community of security researchers to deliver continuous penetration testing and vulnerability management, with actionable results. Synack's PTaaS platform has uncovered more than 71,000 exploitable vulnerabilities to date, protecting a growing list of Global 2000 customers and U.S. agencies in a FedRAMP Moderate Authorized environment. For more information, please visit www.synack.com.
You'll design and ship AI agents that plan, reason, drive security tooling and surface exploitable vulnerabilities, running autonomously inside sandboxed environments. It's a hands\-on senior role spanning applied AI and cybersecurity. You'll own systems end to end and raise the technical bar for the team around you.
Sounds interesting? Keep reading…
Please note: This is a remote position based in the U.S. We can only hire U.S. citizens for this position due to federal government contract requirements.
Here's what you'll do
- Build AI agents that automate penetration testing: planning, tool use, reasoning over findings, and acting safely within scope.
- Stand up and run the sandboxed cyber\-lab and range infrastructure, containerised on Kubernetes, where these agents execute offensive tooling.
- Ship features from ideation through production, including evals, guardrails and monitoring.
- Improve how our agents work: prompting, tool orchestration, evaluation, and cost.
- Lead code reviews and design sessions, and help the engineers around you grow.
- Work closely with our security researchers to translate how they hack into how our agents operate.
Here's what you'll need
- 7\+ years of experience in software engineering.
- Proven ability to build and scale production systems
- Strong Python, polyglot codebase,
- Fluent or willing to learn Go
- Experience building LLMs an agentic frameworks leveraging tools like RAG, MCP\- from dev environment to production
- Demonstrate ability to create reliable agents (prompting, evaluation, guardrails, understanding of token and cost optimization
- Capable leveraging Docker, Kubernetes, isolated sandbox, cloud experience GCP preferred
- Brings a disciplined testing approach to microservices gPRC, API design, async messaging (Pub/Sub, Kafka)
- Passionate desire to understand how systems get compromised
- Clear communication
- Collaborative ability to partner across teams
Nice To Have
- Experience in a fast\-paced startup environment.
- Experience in Cyber Security.
Ready to join us?
Synack is committed to embracing diversity. Our people are our strength. Each addition to our team is an opportunity to grow and diversify our ideas, experiences, and viewpoints. We strive to be inclusive of Race, Ethnicity, Religion, Sex, LGBTQ\+, Veterans, Disabilities, and Age. Synack welcomes you!
As a candidate, Synack cares about your privacy. Please view our candidate privacy policy.
This position has responsibility to ensure Synack's security and privacy posture is maintained.
$145,000 \- $250,000 Salary is determined by a combination of factors including location, level, relevant experience, and skills. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. The compensation package for this position may also include equity, and benefits.
For more details about our benefits, please see our benefits overview. Then for the Employer code, enter: synack
Salary Context
This $145K-$250K range is above the median for AI Software Engineer roles in our dataset (median: $185K across 231 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 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Synack, 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 $218,500 based on 729 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($197K) sits 10% below the category median. Disclosed range: $145K to $250K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Synack AI Hiring
Synack has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Remote, US. Compensation range: $250K - $250K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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