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Product Specialist, Clinical AI Applications
Classification: FLSA Exempt
Reports To: Product Manager, Clinical Applications
Summary
Under the direct guidance of the Product Manager, Clinical Applications, works with a variety of teams to ensure excellent product delivery. This position is responsible for supporting the design, development, approval, launch, and ongoing improvement of clinical AI products, including coordination of feedback from customers, internal teams, external partners, and product leadership.
Duties and Responsibilities
- Responsible for an understanding of histopathology, digital pathology, and AI in pathology workflows.
- Identifies product gaps, and generates new ideas that grow market share, improves customer experience, and drives growth, and presents those ideas to leadership for approval.
- In partnership with the Product Manager, creates buy\-in for the product vision both internally and with key external partners, with a focus on external relationships.
- Responsible for translating product strategy and roadmap into detailed product requirements.
- Collaborates with engineering and AI development teams to prioritize activities to deliver with quick time\-to\-market and optimal resources.
- Provides feedback on product pricing and positioning strategies.
- Collaborates on product launches including working with public relations team, executives, and other product management team members.
- Develops promotional plans consistent with product line strategy for approval.
- Attends and hosts relevant events, exhibitions, and meetings.
- Prepares and submits product\-specific collateral and product presentations for distribution and approval.
- Drives life science and clinical product projects in collaboration with internal and external stakeholders.
- Visits customers to solicit feedback on company products and services.
- Assists in writing usability, summative test, clinical test and validation plans, user guides, installation plans, algorithm tuning plans and other relevant product documents.
- Responsible for the appropriate accessing and handling of electronic Protected Health Information (PHI) as outlined by policy and the Health Insurance Portability and Accountability Act (HIPAA)
- This job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities that are required of the employee. Duties, responsibilities, and activities may change, or new ones may be assigned at any time with or without notice
Minimum Qualifications
- Bachelor’s Degree in technical or scientific field (Computer Science, Engineering, Mathematics, Biology, Biochemistry, Chemistry, Medicine, Etc.)
- One (1\) year of experience in product development, product management, lab management, or similar positions within the life science or diagnostics industries
- Authorization to work in the United States, or appropriate authorization to work in the applicant’s current home country (Indica Labs does not sponsor work visas)
- Valid Passport for international travel and willingness to travel internationally
Preferred Qualifications
- Two (2\) years of experience in product development, product management, lab management, or similar positions within the life science or diagnostics industries
- Candidates in the USA, EU, UK, and Ireland will be given preference for this position
Knowledge, Skills, and Abilities
- Advanced written and verbal communication skills
- Ability to deliver presentations to small and large scientific audiences, often with short notice.
- Ability to provide superior customer service.
- Intermediate skills in Information Technology (IT) and network knowledge
- Ability to listen to and understand information and ideas in speaking so others will understand.
- Knowledge of scientific software packages
- Skilled at swiftly changing priorities as requirements are identified.
Location
This position reports to our headquarters in Albuquerque, New Mexico, USA, and is open to global applicants. Candidates currently residing within 100 miles of Albuquerque are considered for 100% in\-office positions. Candidates currently residing outside of this area are considered for fully remote positions in their home state or country.
Working Conditions and Physical Effort
- Up to 50% travel
- No, or limited physical effort required
- No, or limited exposure to physical risk
- Work is normally performed in a typical interior/office work environment
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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 Indica Labs, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
Indica Labs AI Hiring
Indica Labs has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Albuquerque, NM, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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