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About This Role
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Diversity - Innovation - Caring - Global Collaboration - Winning Spirit - High Performance
At Boston Scientific, we’ll give you the opportunity to harness all that’s within you by working in teams of diverse and high-performing employees, tackling some of the most important health industry challenges. With access to the latest tools, information and training, we’ll help you in advancing your skills and career. Here, you’ll be supported in progressing – whatever your ambitions.
Position Summary
As a forward deployed AI Engineer, you will serve as an embedded technical expert supporting the development, deployment, and sustainment of AI-enabled solutions across Boston Scientific’s global supply chain and sourcing organizations. This role will partner closely with other forward deployed AI engineers, internal product owners, subject matter experts, and end users to co-develop scalable solutions that deliver meaningful business outcomes for a company that builds life-saving medical devices.
The organization will empower its Forward Deployed AI Engineers to address high levels of complexity and own the composition of solutions. The Forward Deployment AI Engineer will be responsible for translating workflow needs into production-ready applications, pipelines, ontology objects, validation logic, and technical documentation. This role will be key in executing against the executive-approved strategic supply chain and sourcing digital roadmap.
Purpose of the Role
Operate as Boston Scientific’s forward deployment AI engineer to ensure key global supply chain and sourcing use cases move from requirements gathering through production deployment with clear ownership, technical documentation, validation, and ongoing support.
Key Responsibilities
Solution Development & Technical Execution
- Embed directly with business operators to deeply understand their workflows, constraints, incentives, failure modes, and sources of friction translating ambiguous problems into solvable, high-value technical opportunities across application behavior, data logic, technical specifications, and validation criteria
- Rapidly prototype and iterate solutions using ontologies, AI, and custom engineering building just enough to validate direction, then scaling the proven patterns into robust systems.
- Design end-to-end workflows that connect ontology objects, data integrations, models, and applications into coherent operational solutions that deliver measurable outcomes.
- Deploy bleeding-edge AI capabilities in a regulated industry, and build alongside external forward deployed engineers, technical experts in scaling technologies and AI leaders.
- Diagnose and resolve complex system behaviors across data pipelines, ontologies, applications, and deployment environments, often in real time with frontline business stakeholders.
- Own the technical execution of assigned sourcing and supply chain use cases from requirements gathering through production deployment.
- Build, configure, test, and refine application functionality in partnership with product owners, sourcing SMEs, process leads, and end users.
- Partner with internal data teams and external technical resources focused on the data platform to identify data gaps, pipeline issues, system constraints, and application defects related to data hydration.
- Contribute to reusable patterns, logic, and documentation in lockstep with INFRA teams that can accelerate future sourcing and supply chain use cases.
Business Validation & Stakeholder Partnership
- Serve as the tactical connective tissue between business and engineering, converting operational insights into structured requirements, architecture decisions, and delivery priorities to then build.
- Facilitate clear feedback loops between technical teams and business stakeholders to ensure user needs are accurately reflected in delivered solutions.
- Partner with users to confirm applications reflect real-world processes, decision points, and compliance requirements. Help turn prototype feedback into prioritized technical changes, defects, enhancements, or product decisions.
- Validate application behavior and data logic with SMEs, end users, and product owners.
- Support user acceptance testing, application readiness, production validation, and post-deployment issue resolution.
- Champion operational excellence and velocity, ensuring engineering choices support maintainability, ontology coherence, security guardrails, and sustainable adoption—not one-off heroics or brittle prototypes.
Required Skills & Qualifications
- Working knowledge of, or strong aptitude to learn, market leading data and execution platform capabilities.
- 1 year of engineering experience
- Proven track record as an expert-level individual contributor
- Software engineering background with proficiency in Expertise in TypeScript, Node.js, React, and Next.js or similar packages/languages/frameworks and Python
- Hands-on experience building with LLMs and AI agents in production systems
- Deep understanding of repository architecture and management
- Familiarity with the developer tools ecosystem VSCode extensions, CLIs, and third-party integrations
- Experience translating ambiguous business requirements into technical specifications, application behavior, validation logic, and deployable solutions.
- Strong problem-solving skills and comfort operating in ambiguous, fast-moving project environments.
- Ability to document technical decisions, design logic, assumptions, dependencies, and support requirements clearly.
- Demonstrated ability to partner across technical, business, and external consulting teams.
Preferred Skills & Experience
- Experience working in supply chain, sourcing, procurement, contract management, invoice management, supplier management, or compliance-related workflows.
- Familiarity with enterprise systems such as Ariba, SAP, ERP platforms, data warehouses, or related procurement/supply chain applications.
- Experience supporting production applications, troubleshooting data issues, and validating user-facing application behavior.
- Exposure to ontology design, object-oriented data modeling, workflow automation, or AI-enabled business applications.
- Experience working in agile, sprint-based, or product-oriented delivery environments.
- Ability to communicate technical tradeoffs clearly to business stakeholders and leadership.
Requisition ID: 635686
Minimum Salary: $ 71500
Maximum Salary: $ 135800
The anticipated compensation listed above and the value of core and optional employee benefits offered by Boston Scientific (BSC) – see www.bscbenefitsconnect.com—will vary based on actual location of the position and other pertinent factors considered in determining actual compensation for the role. Compensation will be commensurate with demonstrable level of experience and training, pertinent education including licensure and certifications, among other relevant business or organizational needs. At BSC, it is not typical for an individual to be hired near the bottom or top of the anticipated salary range listed above.
Compensation for non-exempt (hourly), non-sales roles may also include variable compensation from time to time (e.g., any overtime and shift differential) and annual bonus target (subject to plan eligibility and other requirements).
Compensation for exempt, non-sales roles may also include variable compensation, i.e., annual bonus target and long-term incentives (subject to plan eligibility and other requirements).
For MA positions: It is unlawful to require or administer a lie detector test for employment. Violators are subject to criminal penalties and civil liability.
Boston Scientific transforms lives through innovative medical technologies that improve the health of patients around the world. As a global medical technology leader for more than 45 years, we advance science for life by providing a broad range of high-performance solutions that address unmet patient needs and reduce the cost of healthcare. Our portfolio of devices and therapies helps physicians diagnose and treat complex cardiovascular, respiratory, digestive, oncological, neurological and urological diseases and conditions. Learn more at www.bostonscientific.com and follow us on LinkedIn.
Boston Scientific Corporation has been and will continue to be an equal opportunity employer. To ensure full implementation of its equal employment policy, the Company will continue to take steps to assure that recruitment, hiring, assignment, promotion, compensation, and all other personnel decisions are made and administered without regard to race, religion, color, national origin, citizenship, sex, sexual orientation, gender identity, gender expression, veteran status, age, mental or physical disability, genetic information or any other protected class.
Please be advised that certain US based positions, including without limitation field sales and service positions that call on hospitals and/or health care centers, require acceptable proof of COVID-19 vaccination status. Candidates will be notified during the interview and selection process if the role(s) for which they have applied require proof of vaccination as a condition of employment. Boston Scientific continues to evaluate its policies and protocols regarding the COVID-19 vaccine and will comply with all applicable state and federal law and healthcare credentialing requirements. As employees of the Company, you will be expected to meet the ongoing requirements for your roles, including any new requirements, should the Company’s policies or protocols change with regard to COVID-19 vaccination.
Among other requirements, Boston Scientific maintains specific prohibited substance test requirements for safety-sensitive positions. This role is deemed safety-sensitive and, as such, candidates will be subject to a prohibited substance test as a requirement. The goal of the prohibited substance testing is to increase workplace safety in compliance with the applicable law.
Salary Context
This $71K-$135K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Boston Scientific, 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 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($103K) sits 51% below the category median. Disclosed range: $71K to $135K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Boston Scientific AI Hiring
Boston Scientific has 3 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, Data Scientist. Positions span Maple Grove, MN, US, Arden Hills, MN, US, Santa Clarita, CA. Compensation range: $73K - $135K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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