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About This Role
Office Location:Lower Manhattan, NY (4 days per week)
TS/SCI Required
Accrete is a dual\-use AI software company that licenses its Knowledge Engine Platform and Expert AI Agents to government and enterprise clients. The platform creates a unified semantic representation of an organization’s ground truth—capturing tacit domain knowledge, understanding contextual relevance, and connecting information silos—enabling expert agents to reason across complex data and support high\-stakes decisions with confidence.
From national security to commercial use cases, Accrete delivers mission\-critical decision intelligence and automation on a single platform.
We are looking for a highly technical Full\-Stack Engineer with strong full\-stack engineering capabilities to help build and deploy AI\-powered applications for mission\-critical environments.
What You’ll Do
- Design, build, and deploy AI\-enabled applications used by government and enterprise customers
- Develop full\-stack product features across frontend, backend, APIs, and infrastructure
- Build and integrate LLM\-powered workflows, AI agents, retrieval systems, and semantic search capabilities
- Collaborate closely with product, design, infrastructure, and applied AI teams
- Work directly with end users and stakeholders to rapidly prototype and iterate on solutions
- Optimize application performance, reliability, scalability, and security
- Contribute to architecture decisions and engineering best practices across the platform
- Deploy and maintain applications in secure and regulated environments
- Help shape internal tooling and developer workflows leveraging modern AI\-assisted engineering practices
Qualifications
- Active TS/SCI Clearance required
- 4\+ years of software engineering experience
- Strong full\-stack engineering experience building production\-grade applications
- Proficiency with modern frontend frameworks such as React, Next.js, or TypeScript\-based web applications
- Strong backend development experience with Python, Node.js, or similar technologies
- Experience building and consuming APIs and distributed systems
- Familiarity with AI/ML systems, including LLMs, retrieval pipelines, vector databases, or agentic workflows
- Experience working with cloud infrastructure such as AWS, Azure, or GCP
- Strong understanding of databases, authentication, and secure application design
- Ability to operate in fast\-paced, ambiguous environments with high ownership
Preferred Qualifications
- Experience supporting government or defense\-related programs
- Experience deploying applications in classified or air\-gapped environments
- Familiarity with Kubernetes, Docker, CI/CD pipelines, and infrastructure\-as\-code
- Experience with graph databases, knowledge systems, or semantic technologies
- Prior startup or high\-growth technology company experience
- Strong product intuition and ability to work cross\-functionally with end users
Salary Range: 170k\-235k
*The salary range provided reflects the estimated compensation for this role based on the expected qualifications and experience level. The final offer may vary depending on factors such as skills, experience, and alignment with role requirements.*
Core Values \& Expectations:
Impact
You take full ownership and accountability for your work, consistently seeing projects through from inception to completion with a strong bias for action. Proactively identifying challenges, you drive solutions rather than waiting for direction, and hold yourself and others to the highest standards for delivering results. With strategic thinking and a problem\-solving mindset, you make informed decisions leveraging data and expertise, always looking for ways to improve processes, optimize workflows, and enhance outcomes beyond your immediate responsibilities.
Collaboration
You work seamlessly across teams, prioritizing shared goals and team success over individual credit. Engaged listening and open, candid communication are at the heart of your approach, ensuring alignment and synergy throughout the organization. You value diverse perspectives, seeking input from others to drive better results. By treating colleagues with respect and professionalism, you help build a culture of trust, supporting each other through challenges, celebrating successes, and constructively addressing conflicts to strengthen relationships and improve outcomes.
Passion for AI \& Innovation
You are deeply excited about the transformative potential of AI and committed to contributing to a company shaping the future of work. With curiosity and a growth mindset, you continuously seek to learn, adapt, and stay at the forefront of new developments. Your enthusiasm for innovation drives you to explore new ideas, challenge the status quo, and find creative solutions that deliver meaningful impact. You approach your work with energy and a desire to advance both technology and the way we work.
Company Benefits
- Competitive Salary: Aligned with experience and market standards
- Comprehensive Insurance: Health, dental, and vision coverage for you and your family
- 401(k) Plan: Build your financial future with our retirement savings plan
- Growth \& Development: Access professional learning opportunities and career advancement support
- Onsite Perks: Enjoy catered lunches, snacks, and a fully stocked kitchen
- Team Bonding: Company\-sponsored happy hours and social events to connect and unwind
*Accrete is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law.*
Salary Context
This $170K-$235K range is above the median for AI Software Engineer roles in our dataset (median: $190K across 193 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 3,824 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At accrete, 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 $234,620 based on 682 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($202K) sits 14% below the category median. Disclosed range: $170K to $235K.
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.
accrete AI Hiring
accrete has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span IN, US, New York, NY, US. Compensation range: $235K - $235K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above the national 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 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 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 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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