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Job Requirements
Sweetwater, the nation's #1 online retailer of pro audio equipment and music instruments, is seeking a Senior AI & AppSec CyberSecurity Engineer to join our growing IT security team. In this senior-level role, you will design and implement the strategy behind how Sweetwater adopts new technology securely.
You'll be responsible for building automated, low-friction guardrails that reduce risk without slowing teams down, and lead secure code review and threat modeling across the software development lifecycle. This is a hands-on position where you stay current with secure coding practices in a fast-evolving development environment, leverage AI to speed up vulnerability review and triage, and mentor engineers while driving secure-by-design decisions across our organization.
This position is a role for security leaders who understand that great security is built into products from the start, not simply put in afterward. You will partner with development teams to establish secure coding standards, guide architectural decisions, and ensure that security doesn't become a roadblock to innovation.
Job Responsibilities
- Lead the security oversight of AI tools, platforms, and AI-powered applications adopted across the organization, including evaluating new vendors and models prior to adoption, reviewing supply chain risk, and assessing adversarial robustness while managing data handling and intellectual property considerations
- Drive Application Security (AppSec) initiatives across the software development lifecycle, including secure code review, threat modeling, and security testing while leveraging automated tools to improve efficiency and coverage
- Develop and maintain AI and AppSec security standards and documentation, supporting audit and regulatory compliance requirements across the enterprise
- Partner with software development teams to establish and enforce secure coding standards and testing practices across the organization's applications, including standards for reviewing and validating code
- Support the organization's risk exception process, performing security review of policy exceptions and unusual use cases in partnership with relevant stakeholders
- Mentor junior security engineers, providing technical guidance and supporting their professional development and growth within the security discipline
- Identify, triage, and remediate AI governance and application security risks across Sweetwater's development program within established service levels
- Stay current with emerging AI security threats and secure coding practices, and update security frameworks and documentation accordingly
Qualifications
- Bachelor's degree in Computer Science, Information Security, or related field, or equivalent professional experience
- 6+ years of cybersecurity experience, including at least 1 year focused on AI/ML security or governance and 3 years focused on application security
- Demonstrated experience in AI/LLM application security, including prompt injection defense, model risk assessment, and generative AI governance frameworks
- Strong background evaluating and governing AI coding agents and AI code assist platforms used across both professional software engineering and citizen development contexts
- Expertise in evaluating and advising on static, dynamic, and software composition analysis (SAST/DAST/SCA) scanning practices within CI/CD pipelines
- Solid understanding of user-developed, low-code/no-code, and shadow IT governance frameworks, including tiered risk models and automated policy enforcement
- Strong communication and documentation skills, with the ability to translate security concepts for both technical and non-technical audiences
- Hands-on experience reviewing code and conducting security assessments in real-world development environments
- One of the following preferred: CSSLP (Certified Secure Software Lifecycle Professional) or GSSP (GIAC Secure Software Programmer)
Sweetwater “Ideal” Team-Player
- WOWs The Customer – You possess a deep passion and desire for creating amazing customer & colleague experiences
- Gets Things Done – You have a great work ethic and move through tasks with a sense of urgency
- Obsesses over the Details – You are committed to paying attention to the details
- Drives Continuous Improvement – You are always focusing on effective and efficient work and ways to get better
- Develops the Future – You are committed to the pursuit of growing personally & professionally and have a focus on bringing your colleagues along in that journey
We function best as a unified team, so relocation to our state-of-the-art campus in Fort Wayne, IN is required. Interested and qualified candidates, please apply. We look forward to exploring your #FullTimeDream with Sweetwater!
Sweetwater’s culture is one that is built on the creation and celebration of music, which unifies people from all walks of life. Our individual differences make Sweetwater stronger as a company and a better place to work. Having a truly diverse workforce allows us the opportunity to collectively respect, learn and grow from each unique perspective and experience. We have welcomed thousands of employees and families who have joined the Sweetwater team from all over the nation and the world, and we welcome you, too, to find out why we consider a career at Sweetwater the full-time dream
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Sweetwater, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
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.
Sweetwater AI Hiring
Sweetwater has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fort Wayne, IN, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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