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About This Role
Requisition ID: 67703
Title: Engineer Sr I - Product Security (AI Platform Engineer) -remote
Salary Range: Salary Minimum: 89,000
Salary Maximum: 160,000
Main Objective: Own and operate Arthrex's Engineering AI platforms, ensuring they are secure, governed, cost-efficient, and reliably operational across all Engineering AI tools and integrations — including Anthropic Claude, OpenAI, Google Gemini, Cognition Devin, Perplexity, and more. This role is within Arthrex's Product Security Engineering team, and you help ensure Engineering AI platforms are well-administered, protected, and aligned with enterprise security and engineering standards. This role is scoped to Engineering AI tooling; enterprise-wide IT infrastructure, Data & Analytics platforms, and corporate AI governance fall outside this role's remit. The ideal candidate would work from our Naples, FL, Boston, MA, or Santa Barbara, CA locations; however, we are open to full-time remote anywhere in the United States.
Essential Duties and Responsibilities:
- Governance & Security: Define and enforce usage policies, acceptable use standards, and access controls across all AI tools used within the Engineering organization. Partner with the Cloud SecOps Lead to implement and maintain security controls across all AI platforms and integrations, monitoring for data exposure risks and supporting periodic security audits and access reviews. Establish and enforce controls for agentic AI workflows, including agentic identity management, scoped permissions for autonomous AI actions, and audit logging of agent-initiated activity to detect and prevent unauthorized or unintended behavior.
- Architecture, Integration & Administration: Partner with the Software Engineering Architect to ensure AI platform integrations align with engineering architecture and security standards; evaluate new AI tooling for engineering fit and maintain connector and integration design consistency across systems. Manage user provisioning, repository management, platform configuration, and connector integrity across all AI tooling environments.
- Cost Management & Platform Health Monitoring: Track and optimize AI platform spend across tools, APIs, and licenses; build cost visibility dashboards and establish spend controls and reporting cadences for leadership. Monitor platform health including usage, adoption trends, model call volumes, latency, and error rates; surface insights and usage reporting to relevant stakeholders on a regular basis.
- Compliance & Documentation: Maintain audit-ready documentation of platform governance controls, compliance posture, runbooks, architecture diagrams, and integration configurations. Document and communicate AI use case wins and gaps to leadership, translating platform adoption data into business impact narratives that justify continued investment and flag areas needing attention.
- Vendor Management, Escalation & Enablement: Serve as the primary escalation point for platform issues; manage vendor relationships and coordinate with AI tool providers on outages, feature gaps, and roadmap alignment. Support engineer onboarding and internal adoption of AI tools, building enablement materials as the platform evolves.
- Designs security architecture of components or functional systems and modifies existing designs to develop or improve products.
- Recommends alterations to development and design to improve the security of products and/or procedures.
- Contributes to a broader design perspective and considers how an application interacts with the underlying infrastructure or external resources.
- Develops threat scenarios and designs responses for associated vulnerabilities to mitigate risk.
- Maintains design history file for assigned projects, adhering to Arthrex design control procedures.
- Determines the necessity of security testing and initiates testing of assigned products.
- Provides Regulatory department with technical support for assigned projects as needed.
- Supports Marketing and Product Management with technical information to be used for training and marketing of assigned products.
- Supports Software Engineering to design and develop components, processes, and training using Security-by-Design and Privacy-by-Design principles.
- Supports surgeon and distributor customers in the sales process by educating and demonstrating security-focused aspects of assigned products as needed.
- Partner with Legal, Compliance, Privacy, and Information Security departments to ensure products and staff comply with required laws, regulations, and policies.
- Reports on progress and status of assigned projects on a timely basis.
- May be required to travel; International travel may be required.
Knowledge:
- Complete understanding and application of principles, concepts, practices, and standards. Full knowledge of industry practices.
Skills:
- AI Platform Administration & Development (required) Hands-on experience administering AI platforms (e.g., Anthropic Claude, OpenAI ChatGPT, Google Gemini, Cognition Devin, Perplexity, or similar), including professional or personal experience building applications, analytics pipelines, or security tooling using agentic software development processes.
- Integration, Access & API Security (required) Experience managing SSO, RBAC, and API security for SaaS or cloud platforms, including connector and integration management using REST APIs, webhooks, or developer-oriented integration platforms such as Azure Logic Apps, GitHub Actions, or similar.
- Automation & Scripting (required) Scripting skills in Python, Bash, or PowerShell for automation and tooling.
- AI Governance & Agentic Security (preferred) Familiarity with AI governance frameworks and responsible AI practices in an engineering or product development context (NIST AI RMF, ISO 42001), including agentic AI concepts such as agent identity, scoped permissions, secure agent orchestration patterns, and prompt injection attack vectors and AI-specific runtime security and guardrail design.
- AI Observability & Runtime Monitoring (preferred) Familiarity with LLM observability and prompt governance tooling (e.g., LangSmith, Helicone, Weights & Biases) and OpenTelemetry (OTel) for LLM tracing and agent observability.
- AI Spend Management (preferred) Familiarity with AI tooling spend management, including API usage cost tracking, license optimization, and usage-based billing across engineering AI platforms.
- Knowledgeable of System and Software Development Processes and Lifecycles required.
- Knowledgeable of application security best practices required.
- Knowledgeable of FDA and ISO guidelines for the development of medical devices required.
- Experience leading strategic discussion that addresses both business and technical risks required.
- Proficiency in the development of threat scenarios and risk mitigation techniques required.
- Proficiency in the use of privacy by design principle required.
Education and Experience:
- 5 years of related experience required; preferably in platform engineering, software/ML engineering, or software engineering toolchain administration.
- Bachelor’s degree required preferably in Engineering (Mechanical, Biomedical, Electrical or Software Engineering), Computer Science, Information Security, or Cybersecurity
Arthrex Benefits
- Medical, Dental and Vision Insurance
- Company-Provided Life Insurance
- Voluntary Life Insurance
- Flexible Spending Account (FSA)
- Supplemental Insurance Plans (Accident, Cancer, Hospital, Critical Illness)
- Matching 401(k) Retirement Plan
- Annual Bonus
- Wellness Incentive Program
- Gym Reimbursement Program
- Tuition Reimbursement Program
- Trip of a Lifetime
- Paid Parental Leave
- Paid Time Off
- Volunteer PTO
- Employee Assistance Provider (EAP)
All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other status protected by law.
Salary Context
This $89K-$160K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Arthrex, 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 Required
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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($124K) sits 41% below the category median. Disclosed range: $89K to $160K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Arthrex AI Hiring
Arthrex has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $160K - $160K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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).
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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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