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About This Role
About Us
At Prenuvo, we are on a mission to flip the paradigm from reactive "sick-care" to proactive health care. Our award-winning whole body scan is fast (under 1 hour), safe (MRI has no ionizing radiation), and non-invasive (no contrast). Our unique integrated stack of optimized hardware, software, and increasingly AI, coupled with the patient-centric experience across our domestic and global clinics, have allowed us to lead the change against "we caught it too late again".
We are looking for a Senior Forward Deployed AI Engineer to embed with our clinical, product, and operations teams and ship AI features that are validated, governed, and monitored by default.
This role does not provide visa sponsorship. Applicants must be legally authorized to work in the United States at the time of hire and must not require employer sponsorship for a work visa (current or future).
Help reshape the world through proactive healthcare while working with cutting-edge technology and high performing teams with deep expertise - join us to make a difference in people's lives!
What You'll Do
- Embed with one or two teams per cycle and own each AI solution end to end from intake brief to a shipped, monitored feature running in production.
- Make the build / wrap / vet call on every initiative and set the validation bar it has to clear. This is the judgment that keeps un-vetted features away from patients.
- Build the agentic layer: tool and function calling, RAG, MCP integrations, multi-step orchestration, and human-in-the-loop checkpoints.
- Architect on the Prenuvo AI platform shared model serving, foundation-model contracts (AWS Bedrock), eval harness, monitoring, tool registry, prompt guardrails. No parallel stacks.
- Define evaluation before you build: acceptance criteria, offline and online evals, and escalation triggers agreed up front rather than discovered later.
- Run each initiative through our AI intake and risk categorization so governance is inherited rather than bolted on. You design to the standard; Regulatory, Privacy, and Legal hold formal sign-off.
- Leave a reusable asset behind every engagement a validated template, a registered tool, or a reference architecture contributed back to the shared layer.
- Raise the AI fluency of the teams around you through design reviews and office hours, so the next team can ship without you in the room.
- Move us toward a world where more teams ship AI features than our AI department could ever build alone new teams starting from a template and a champion, not a blank page.
What You'll Bring
- Six or more years building and delivering product-grade software, including a track record of shipping AI-powered features to production LLM and/or imaging using tool calling, RAG, or agent orchestration. Notebooks and demos are not the bar. You're an experienced full-stack developer.
- Probabilistic-software literacy: you evaluate models, reason explicitly about failure modes, and answer "what is the performance?" with a method rather than "we'll find out in production."
- The judgment and standing to hold a validation bar with senior clinical, product, and engineering stakeholders and to explain the reasoning behind it in their language.
- Product delivery instincts: you can scope a fuzzy request, say no to the wrong version of it, and translate a product need into a validated design.
- Strong Python and practical cloud engineering and deployment (APIs, deployment, ideally AWS and Bedrock), and comfort working directly inside another team's codebase.
- Hands-on with modern evaluation frameworks and human-in-the-loop design — you've built eval harnesses and acceptance criteria, not just consumed them.
Nice to Have:
- Experience in regulated, clinical, or healthcare-data environments FDA, Health Canada, ISO 13485, or equivalent and comfort working with PHI.
- AWS and Bedrock specifically, though any major cloud translates.
- MCP, or experience building tool/function interfaces other teams consume.
- Consulting, solutions architecture, or any form of forward-deployed experience —> you've been the outsider embedded in someone else's team before.
Our Values
First: we are Pioneers
- Transforming healthcare requires divergent thinking, bias for action, disciplined experimentation, and consistent grit and determination to maintain momentum. This journey is as challenging as it is rewarding.
Second: we are Platform-Builders
- We're always building foundations that allow us to achieve tomorrow more than we did today. We never lose sight of what's ahead – in a mindset of ownership and duty to our mission.
Above all: we are Patients
- We could all be the next person who walks through our very doors, seeking clarity or peace of mind. We are proud of our impact on our patients' lives, and we won't stop till everyone can benefit from our work.
What We Offer
- An avenue to make a positive impact on people's lives and their health
- We believe in preventative healthcare for everyone, including our team - Prenuvo provides free, whole-body scans to each team member
- Growth opportunities are at the heart of our people journey, we're doing big things with bright minds - there is no single path to success, it can be shaped along the way
- Building strong relationships is at the core of everything we do - our team gets together each week to connect, share, and socialize
- Recognizing time away to restore is vital to our wellbeing - we have a flexible vacation policy and we will encourage you to use it
- We now offer the Prenuvo's Commuter Benefits Plan to help cover your transit and parking costs. Whether you ride, drive, or park, we've got you covered, making your commute easier and more affordable!
- Prenuvo offers a 401(k) retirement savings plan to eligible employees, allowing team members to make pre-tax contributions toward their retirement. While Prenuvo facilitates access to the plan, the company does not currently offer matching contributions.
- We offer a comprehensive benefits package including health, dental, vision, including Mental Health coverage, to support you and your family
- The base salary for this role ranges from $145,000-$165,000 USD per year, depending upon experience and geographic location
We are an equal-opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
As part of the hiring process, successful candidates will undergo a background check in compliance to applicable federal, provincial, and state rules.
Please be advised that official communication from our recruitment team will only come from our authorized domain [prenuvo.com]. If you are contacted by a recruiter, please ensure their email address ends with @prenuvo.com. We do not use third-party recruitment services or any other email domains for hiring purposes. If you receive communication that you believe to be fraudulent, please report it immediately to [email protected].
Salary Context
This $145K-$165K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Prenuvo, 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 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($155K) sits 27% below the category median. Disclosed range: $145K to $165K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Prenuvo AI Hiring
Prenuvo has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $165K - $165K.
Location Context
AI roles in Los Angeles pay a median of $210,000 across 1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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