Interested in this AI/ML Engineer role at Nsight Health?
Apply Now →Skills & Technologies
About This Role
AI Engineer
Remote
Employment Type: Full\-Time \| Individual Contributor
About Nsight Health
Nsight Health is transforming how care is delivered through Remote Patient Monitoring (RPM), Chronic Care Management (CCM), and Behavioral Health Integration (BHI). We empower healthcare providers to manage chronic conditions using real\-time data, AI\-enabled technology, and 24/7 clinical support. Our HIPAA\-compliant platform connects patients and care teams nationwide—improving outcomes, adherence, and peace of mind. Join a fast\-growing, mission\-driven team that blends healthcare and technology to make a measurable difference in people’s lives.
Nsight Health — Where Technology Meets Compassion.
Position Summary
We are seeking an AI Engineer to own the technical operation and continuous improvement of Nsight's AI phone agents — the automated voice systems handling outbound and inbound patient calls across RPM and care management programs. This is not a passive vendor oversight role. You will be the hands\-on operator of the system: fluent in the provider's UI, API, and webhook integrations, holding them to quality and HIPAA compliance standards, and driving measurable improvement in clinical voice interaction outcomes.
Reporting to the VP of Engineering, you will work closely with the EVP of Patient Experience and the R\&D team to configure and tune AI phone agents, build PHI\-safe audio processing pipelines, architect quality intelligence systems, and automate downstream action on detected failures — before anyone has to find them manually. This role operates inside an organization that runs at an AI\-to\-human engineering output ratio most teams haven't attempted. AI agents are first\-class contributors to the pipeline here. You are expected to build and operate within that model from day one.
AI Fluency Requirement \- Non\-Negotiable
Nsight Health is an AI\-first organization. Every member of our leadership and operations team is expected to actively use AI tools in their day\-to\-day work \- not as a novelty, but as a core productivity multiplier. This role requires genuine curiosity about AI, comfort experimenting with tools like Claude, ChatGPT, and workflow automation platforms, and the judgment to know when AI helps and when it doesn't. If AI makes you uncomfortable, this is not the right role.
Key Responsibilities
Vendor \& Provider Management: Serve as the technical liaison between Nsight and the IVA provider; translate clinical and operational requirements into clear direction for the provider and their capabilities back to internal stakeholders. Manage day\-to\-day performance against quality and HIPAA compliance standards and drive resolution when expectations aren't met. Define acceptance criteria and build layered test harnesses for all provider releases before they touch production clinical workflows or patient data.
AI Phone Agent Configuration \& Optimization: Rapidly become an expert in the provider platform; configure and tune AI phone agents to meet clinical and operational goals. Actively influence the provider's NLU engine design — working alongside their team on tuning, accuracy, and continuous iteration until the IVA agents are best in class for clinical voice interactions.
Alerting \& Quality Intelligence: Build and own the alerting layer: continuously scrape provider data, classify failure patterns, and fire automated alerts before issues require manual intervention. Architect AI\-driven quality intelligence pipelines that surface failures proactively and route them to the right owner without manual triage.
PHI\-Safe Pipeline \& Compliance: Build PHI\-safe audio processing pipelines using local speech\-to\-text so recordings never leave the infrastructure, with PHI redacted in memory before anything reaches storage. Maintain HIPAA\-compliant data handling across all AI pipelines and BAA\-covered third\-party integrations.
AI\-Native Tooling: Build the tooling the work requires — semantic search, vectorized data processing, automated alerting, or whatever approach best fits the problem. Outcomes matter, not the stack.
The Impact You’ll Make
Patient Experience at Scale: The AI phone agents you configure and optimize are the first touchpoint for thousands of Medicare patients across 350\+ clinics — your work directly shapes their care experience.
Compliance by Design: By building PHI\-safe pipelines and holding the provider to documented HIPAA standards, you protect both patients and the organization from the ground up.
AI Operations Pioneer: You will define what production\-grade AI voice operations looks like in a regulated healthcare environment — building the alerting, quality intelligence, and automation infrastructure that keeps it running without manual oversight.
Qualifications
Required:
- 4\+ years of ML or AI engineering experience, with at least 2 years working directly with production LLM integrations in systems serving real users — not research models or demos
- Deep NLP/NLU expertise: intent recognition, entity extraction, utterance design, and understanding of how NLU accuracy degrades in production without active maintenance
- Voice AI experience in a production environment: speech\-to\-text, TTS, or conversational AI with NLU at its core
- NLU training and tuning in production: managed training data pipelines, designed utterance sets, measured accuracy against real interactions, and iterated based on results
- Healthcare data fluency: claims data, RPM vitals streams, EHR data models, and experience running AI on HIPAA\-covered patient data
- Hands\-on experience with multi\-agent system design including specialist agents, adversarial review layers, and orchestration patterns at scale
- Prompt engineering for classification and structured output at production volume: severity calibration, JSON response enforcement, and iterative refinement driven by precision/recall data
- PHI\-safe AI pipeline design: in\-memory redaction, local inference where required, and data minimization patterns
- LLM platform fluency: Anthropic Claude API, AWS Bedrock, and OpenAI API
- Vector and embedding infrastructure: pgvector, Pinecone, or comparable
- Agent and orchestration frameworks: LangChain, LlamaIndex, CrewAI, or equivalent
- PostgreSQL and SQL fluency; alerting and issue routing tooling (Jira API, Slack webhooks, PagerDuty, or equivalent)
- Telephony platform familiarity: VoIP systems such as Five9, Twilio, or equivalent
- Daily, demonstrated use of Claude Code, GitHub Copilot, or equivalent AI\-assisted development tools. This is a hard requirement.
Preferred:
- Voice and speech tooling: Whisper, Deepgram, AWS Transcribe, or TTS providers; experience in a clinical or regulated audio context
- Tray.io workflow automation and integration experience
- AWS Glue and Apache ecosystem experience (Airflow, Kafka, Spark) for data pipeline and orchestration work
- Bachelor's degree in Computer Science, Engineering, or a related field. Relevant experience will be weighted heavily over educational credentials alone.
Compensation \& Benefits
- Competitive base pay: $160,000 \- $1650,000 annually.
Additional Compensation:
- Performance\-Based Bonus: Eligible for an annual bonus based on company and individual performance.
Benefits Include:
- PTO Accrual Based
- Medical, Dental, Vision, and supplemental insurance options
- 401(k) Plan with 3\.5% Company Match
- Company\-provided equipment
Join Our Mission\-Driven Team
At Nsight Health, you'll be part of a fast\-growing organization that sits at the intersection of healthcare, technology, and compassion. We're looking for an AI Engineer who takes pride in production\-grade work, operates with precision in a regulated environment, and wants their expertise to directly improve how patients experience care.
Salary Context
This $160K-$165K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Nsight Health, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($162K) sits 10% below the category median. Disclosed range: $160K to $165K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Nsight Health AI Hiring
Nsight Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $165K - $165K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.