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About This Role
PLEASE NOTE BEFORE APPLYING:
CODOXO IS NOT ABLE TO OFFER SPONSORSHIP OR ACCOMMODATE ANY CANDIDATES THAT ARE CURRENTLY BEING SPONSORED NOW OR IN THE FUTURE
Of the $3\.8T we spend on healthcare in the United States annually, about a third of it is estimated to be lost due to waste, fraud and abuse. Codoxo is the premier provider of artificial intelligence\-driven solutions and services that help healthcare companies and agencies proactively detect and reduce risks from fraud, waste, and abuse and ensure payment integrity. Codoxo helps clients manage costs across network management, clinical care, provider coding and billing, payment integrity, and special investigation units. Our software\-as\-a service applications are built on our proven Forensic AI Engine, which uses patented AI\-based technology to identify problems and suspicious behavior far faster and earlier than traditional techniques.
We are venture backed by some of the top investors in the country, with strong financials, and remain one of the fastest growing healthcare AI companies in the industry. *Position Summary*:
The Lead AI Platform \& Products Engineer is the highest\-level technical leadership role within Codoxo's AI engineering organization and serves as the technical authority for AI architecture, platform strategy, and engineering excellence across Machine Learning, Generative AI, and Agentic AI.
In addition to leading the technical direction of Codoxo's AI platform, this role provides day\-to\-day technical leadership and initial people management responsibilities for AI engineers, fostering engineering excellence, mentoring talent, developing future technical leaders, and driving execution across multiple AI initiatives.
The Lead AI Platform \& Products Engineer designs, delivers, and scales enterprise AI solutions spanning Machine Learning, Generative AI, and Agentic AI while partnering closely with AI leadership, every developer in the AI function, Product, Engineering, and Security to transform research into secure, scalable, production\-ready AI capabilities deployed across Codoxo's AWS\-based SaaS platform and power Codoxo AI products, creating unprecedented value for our customers. *Key Responsibilities: Lead architecture and evolution of production AI platforms spanning Machine Learning, Generative AI, and Agentic AI.
- Design AutoML, MLOps, Model\-as\-a\-Service, feature engineering, automated retraining, monitoring, and model lifecycle platforms.
- Build production GenAI solutions using Amazon Bedrock, RAG, vector databases, prompt engineering, PEFT (LoRA/QLoRA), and responsible AI.
- Architect autonomous multi\-agent systems using Bedrock AgentCore, MCP, Strands, and modern agent frameworks.
- Define AI platform standards, reusable components, governance, observability, and security best practices.
- Provide technical leadership and initial people management through mentoring, coaching, workload planning, performance feedback, and technical growth.
- Partner with Product and Engineering to deliver scalable AI products while ensuring HITRUST, HIPAA, NIST 800\-53, FedRAMP, and responsible AI compliance.
*Qualifications:** 8\+ years software engineering experience, including 5\+ years building production AI systems.
- Demonstrated expertise across Machine Learning, Generative AI/LLMs, and Agentic AI.
- Expert Python and AWS (Bedrock, SageMaker, Lambda, ECS/EKS, Step Functions, API Gateway, EventBridge, Aurora PostgreSQL, S3, IAM, CloudWatch).
- Deep experience with MLOps, CI/CD, experimentation, drift detection, production model operations, and AI platform engineering.
- Hands\-on experience with RAG, vector search, embeddings, prompt engineering, AWS Evaluations (AWS Evals), steering hooks and guardrails, model evaluation, and LLM optimization.
- Experience with Bedrock AgentCore, MCP, Strands, LangGraph, CrewAI, or equivalent.
- Healthcare claims/FHIR/ICD\-10/CPT/HCPCS and AI\-assisted IDEs (Claude Code, Cursor, Gemini Code Assist, AWS Q Developer, or equivalent) preferred.
- Remote Work Requirements: To ensure reliable performance on the company\-issued CODOXO laptop, employees working remotely must have a stable high\-speed internet connection. Internet performance should be measured using a speed test conducted directly on the CODOXO laptop, not based solely on the internet plan purchased from the provider.
- Physical Requirements: Work is performed in an office environment (either in our office or work\-from home) and requires the ability to work on a computer, operate standard office equipment, and work at a desk.
Leadership \& Soft Skills* Provide technical and people leadership across multiple AI initiatives and engineering teams.
- Build, mentor, coach, and develop high\-performing AI engineers.
- Own cross\-functional AI architecture and engineering standards.
- Drive execution by aligning priorities and removing delivery obstacles.
- Influence strategy through collaboration with AI leadership, Product, Engineering, and Security.
- Balance innovation with scalability, reliability, security, and maintainability.
- Communicate effectively with technical and executive stakeholders.
- Foster operational excellence, continuous learning, customer obsession, and responsible AI.
*Accessibility Notice: If you need reasonable accommodation for any part of the employment process due to a physical or mental disability, please send an email to [email protected] with the subject "Accommodation". Reasonable accommodation requests will be considered on a case\-by\-case basis.* Benefits for You* Health, Dental, and Vision insurance with 100% employee premium coverage (Starts Day 1\)
- Unlimited PTO
- Annual Professional Development stipend
- Annual home office stipend
- 401K Match (after 90 days)
We are an Equal Opportunity Employer:
Codoxo provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. This policy applies to all terms and conditions of employment.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Codoxo, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Codoxo AI Hiring
Codoxo has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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