Interested in this AI/ML Engineer role at Converteo?
Apply Now →About This Role
This is an exciting opportunity to partner with an elite enterprise AI consultancy and build our North American footprint from the ground floor. We are looking for a senior leader to unlock and grow 0\-to\-1 relationships with the world’s most premium pharmaceutical, biotechnology, medical device, and healthcare technology organizations.
#### About Converteo North America
Converteo helps enterprise organizations turn experimental AI pilots into production\-grade agent systems with measurable business impact. Backed by the massive infrastructure and stellar reputation of our established European parent firm \- founded in 2007, with 450\+ experts serving 200\+ major enterprises \- we are headquartered in Paris, with offices in New York, Toronto, and Madrid. We are launching an aggressive expansion into the North American market, with our Life Sciences practice focusing heavily on solving complex clinical, R\&D, and commercial workflow challenges.
*"At Converteo, we thrive on the 'multitasking specialist' model \- we value individuals who can bridge the gap between AI\-driven technical strategy and high\-touch commercial execution."*
Location: Remote
The Model: We are a remote\-first organization that values autonomy and deep work. While our team is global, we believe in the power of periodic, high\-impact gathering.
- Local Presence: This role requires a presence within the NY/NJ/Philly/Boston corridor to facilitate purpose\-driven, in\-person client workshops and strategic team syncs at our NYC hub.
- Travel: This role may involve high\-stakes travel (approx. 25–35%) to lead revenue\-generating meetings, industry roundtables, and key client summits across the US.
- Employment Type: Full\-time (W\-2 Staff).
- Eligibility Requirement: Must be eligible to work in the US.
- Reports to: Partner, Life Sciences.
#### Why This Role Exists
Converteo is a dominant European force turning AI pilots into production\-grade systems. As we aggressively expand our Life Sciences practice into North America, this role serves as our leading subject matter expert and intellectual engine. You will be the primary strategic voice translating complex healthcare and clinical workflows into high\-impact, production\-grade AI strategies.
We are hiring a Principal AI Product Strategist to act as a subject matter expert for the Life Science industry with massive influence across the practice. Working closely with our business development and being part of delivery teams, you will serve as a trusted advisor to clients \- diagnosing industry bottlenecks, owning the blueprinting of AI solutions, and translating technical AI capabilities into clear business value.
#### What Success Looks Like (First 6–12 Months)
- Solution Architecture: Innovative, high\-impact AI and data solution blueprints designed for complex life sciences challenges.
- Commercial Influence: Direct, measurable influence on securing new business and expanding strategic enterprise accounts.
- Client Satisfaction: High client retention and satisfaction driven by your strategic guidance and deep industry expertise.
- Internal Upskilling: Successful mentorship and upskilling of internal delivery teams on the unique nuances of the life sciences sector.
- Value Realization: Compelling business cases and ROI frameworks built for proposed solutions that gain immediate C\-suite buy\-in.
#### What You Will Own
- C\-Suite Advisory \& Translation: Lead strategic consulting engagements and discovery workshops with senior pharma executives, diagnosing client bottlenecks and defining AI transformation roadmaps.
- Strategic Solution Blueprinting: Design high\-level AI\-driven strategies to solve domain\-specific problems, such as accelerating drug discovery, optimizing clinical trials, or personalizing healthcare provider engagement.
- Business Development Enablement: Partner with the Business Development team as the lead strategy expert, architecting the strategic components of proposals and statements of work to ensure our solutions are highly differentiated.
- Market Voice \& Domain Authority: Act as Converteo's foremost expert on the Life Sciences market, ensuring solutions align with regulatory complexities, including FDA guidance, GxP compliance, and data privacy.
- Internal Mentorship: Educate and guide internal technical teams on the operational nuances of the pharmaceutical and biotech value chains.
### Who You Are
- Proven Life Sciences Strategist: 10\-15\+ years of strategic experience focused on the life sciences industry, spent within management consulting, a corporate strategy group at a pharmaceutical/biotech firm, or a similar technology strategy role.
- Clinical \& Domain Authority: Deep, demonstrable expertise across key domains of the life sciences value chain, specifically Clinical Development, R\&D, Medical Affairs, or Commercial operations.
- Executive Presence \& Storytelling: Exceptional communication and presentation skills, with a native ability to command a room of senior executives and translate highly technical AI/ML concepts into clear business value.
- Regulatory Fluency: A comprehensive understanding of the US healthcare ecosystem and its regulatory environments (FDA guidance, GxP, data privacy).
- Consulting DNA: Strong background in a client\-facing consultative environment, with a proven ability to deconstruct complex problems and build consensus across multi\-stakeholder corporate structures.
### Compensation \& Performance Incentives
- Base Salary: $160,000 – $225,000 USD (Depending on geographic zone and experience).
- Benefits: 20 days PTO, winter closure, 11 paid holidays, Summer Fridays.
Salary Context
This $160K-$225K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Converteo, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($192K) sits 12% below the category median. Disclosed range: $160K to $225K.
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.
Converteo AI Hiring
Converteo has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $155K - $225K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.