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About This Role
About PharmaEssentia:
It’s not often you get the chance to make a real impact on the lives of others, while expanding your own possibilities. You’ll find that rare opportunity at PharmaEssentia. Join us, and let’s transform lives, together.
PharmaEssentia Corporation is a rapidly growing biopharmaceutical innovator. We are leveraging deep expertise and proven scientific principles to deliver effective new biologics for challenging diseases in the areas of hematology and oncology, with one product approved and a diversifying pipeline. We believe in the potential to improve both health and quality of life for patients with limited options today through the combination of rigorous research and innovative thinking. Founded in 2003 by a team of Taiwanese-American executives and renowned scientists from U.S. biotechnology and pharmaceutical companies, today we are listed on the Taiwan Stock Exchange (TWSE: 6446) and are expanding our global presence with operations in the U.S., Japan, Singapore, South Korea, and China, along with a world-class biologics production facility in Taichung.
Position Summary
The AI Engineering & Architecture Lead owns PharmaEssentia’s AI architecture and leads the engineering team that builds on it. This is the most important single hire in the Global Data & AI Enablement (GDAI) plan: the team today has no dedicated AI engineering capability, and this role creates it.
Reporting to the Head of Global Data & AI Enablement, this leader is accountable for turning governed data assets into production AI applications and agents that change how commercial teams work — opportunity identification, proactive field alerts, HCP intelligence, field copilots, next-best-action workflows, and omnichannel intelligence. Backend ETL is delivered by an external vendor into Snowflake, so this role’s value sits above that layer: architecture, build-versus-buy judgment, engineering standards, and delivery. Success is measured by sustained adoption and business outcomes, not by the number of tools launched.
Key Responsibilities
Architecture & Build-versus-Buy
- Define the AI and application architecture — retrieval, orchestration, agent frameworks, evaluation, observability, and integration patterns — across the GDAI portfolio
- Make and defend build-versus-buy decisions, retaining enough internal capability to own the architecture rather than depending on vendors for judgment or prioritization
- Design for the data environment as it is: vendor-delivered raw data in Snowflake, a semantic layer built under internal oversight, and a roadmap to internalize both over time
Delivery of Production AI Applications* Lead delivery of production AI applications and agents against the Commercial roadmap, from first use case through sustained operation
- Prioritize engineering capacity with AI Product Strategy & Value so that every major initiative has an owner, a business case, and a measured outcome
- Replace dashboard search with proactive delivery: systems that detect meaningful signals and push the right insight to the right person at the right time
Engineering Standards & Responsible Deployment* Set standards for evaluation, testing, monitoring, cost control, security, and responsible deployment, and enforce them across internal and outsourced work
- Establish how AI output destined for field-facing use is reviewed, logged, and kept within promotional, privacy, and medical–commercial separation requirements
Vendors & Team* Evaluate and integrate vendor platforms, and hold vendors to technical account against architecture, security, and quality standards
- Build and mentor the AI engineering team — one senior engineer in the first wave, a second engineer subject to demonstrated adoption and value
Required Qualifications* Bachelor’s degree required; technical degree (computer science, engineering, or related field) preferred
- 10+ years in software or ML engineering, including 3+ years leading engineers
- Has shipped and operated LLM-based or agentic applications in production — not prototypes, proofs of concept, or pilots — and can describe what broke and what it cost
- Hands-on ownership of retrieval, orchestration, evaluation harnesses, monitoring, and cost control for AI systems in production
- Experience architecting applications on a cloud data platform, ideally Snowflake
- Demonstrated build-versus-buy decisions, and experience holding a technology vendor to technical account on scope and quality
- Still technical: able to review code, debug a production system, and set standards by example
Preferred Qualifications* Experience in pharmaceutical, biotech, or healthcare commercial environments, or another regulated industry with review and audit requirements
- Experience standing up an AI engineering function from zero rather than inheriting one
- Integration experience with CRM and field-facing systems (Veeva, Salesforce) and marketing platforms
- Familiarity with evaluation, guardrail, and observability tooling for LLM applications
- Experience in an organization where adoption, not delivery volume, was the measure of success
Location and Work Arrangement
This position is based in Burlington, Massachusetts, and follows a hybrid work model requiring on-site presence three days per week.
The expected base salary range for this position is $190,000-245,000. Salary offers are based on a wide range of factors including relevant skills, training, experience, education and geographical location. Market and organizational factors are also considered. In addition to base salary and a competitive benefits package, successful candidates are eligible for short-term and long-term incentives.
Benefits of working with our team:
PharmaEssentia USA strives every day to improve the lives of patients as well as our employees. As a valued member of PharmaEssentia USA, you will enjoy the following benefits:* Comprehensive medical coverage
- Dental and vision coverage
- Generous paid time-off
- 401(k) retirement plan with competitive company match
- Medical & Dependent Care Flexible Spending Account
- Up to $150 monthly cell phone reimbursement
- Employee Assistance Program
- Free parking
EEO Statement:
At PharmaEssentia USA, we are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants without regard to race, color, religion, sex, pregnancy (including childbirth and related medical conditions), national origin, age, physical and mental disability, marital status, sexual orientation, gender identity, gender expression, genetic information (including characteristics and testing), military and veteran status, and any other characteristic protected by applicable law. PharmaEssentia USA believes that diversity and inclusion among our team are critical to our success as a global company, and we seek to recruit, develop and retain the most talented people from a diverse candidate pool.
PharmaEssentia does not accept unsolicited agency resumes. Staffing agencies should not send resumes to our HR team or to any PharmaEssentia employees. PharmaEssentia is not responsible for any fees related to unsolicited resumes from staffing agencies.
At PharmaEssentia, our goal is to treat as many people with cancer as possible. That means challenging the status quo with better science that leads to better lives. By joining our team, you will not only expand your own possibilities, but you will contribute to expanding options for people with cancer.
https://us.pharmaessentia.com/careers/
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Salary Context
This $190K-$245K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At PharmaEssentia, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. Disclosed range: $190K to $245K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
PharmaEssentia AI Hiring
PharmaEssentia has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Burlington, MA, US. Compensation range: $150K - $245K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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