Boston Scientific is actively hiring for 2 AI and machine learning positions, concentrated in Research Engineer (1) and AI/ML Engineer (1) roles. Posted salary ranges span $156K - $202K, with 100% of listings disclosing compensation. The median posted ceiling sits at $179K. Positions are based in Santa Clarita, CA, US, Arden Hills, MN, US. Mid-level roles account for 50% of openings.
Skills & Technologies
Locations
Santa Clarita, CA, US, Arden Hills, MN, US
Hiring by Role Category
Open Positions (2)
Research Engineer III
HR Principal, AI Organization Transformation
What Boston Scientific's hiring tells you
With 2 active AI role(s), this company is in the early exploration phase. That can mean either a pilot project being staffed up or a small embedded AI function inside a larger team. Worth investigating directly: ask the recruiter how the AI work is funded and who it reports to. Posted compensation range ($156K - $202K) suggests transparent and competitive pay practices.
The skill mix here leans toward Python in Research Engineer roles. That is a clue about what Boston Scientific is building: teams hire for the work in front of them, not the work they wish they were doing.
Questions worth asking in the Boston Scientific interview loop
The signals above come from public job postings. The signals you actually need come from the conversation. A few questions calibrated to this company's tier:
- Is this AI work funded for at least 18 months, or is it tied to a specific project deadline?
- Will I be the only person doing this, or are there others I will collaborate with day to day?
- What does success look like at six months? At eighteen months?
Boston Scientific AI and ML Hiring
Boston Scientific has 2 active AI and ML roles in our dataset. Open positions span Research Engineer, AI/ML Engineer. Compensation ranges from $156K - $202K across disclosed roles. Roles are based in Santa Clarita, CA, US, Arden Hills, MN, US.
Salary Benchmarks
The market median for AI roles is $218,800. Research Engineer roles pay a median of $300,000 across the market. AI/ML Engineer roles pay a median of $220,000 across the market. Top-quartile AI compensation starts at $272,100.
Skills Boston Scientific Looks For
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
AI Role Categories
Research Engineer
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Market compensation for Research Engineer roles: $300,000 median across 133 positions with disclosed pay.
AI/ML Engineer
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.
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.
Market compensation for AI/ML Engineer roles: $220,000 median across 3,396 positions with disclosed pay.
The AI Job Market Today
The AI job market spans 2,838 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,002), Data Scientist (256), AI Software Engineer (187). 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 (76) are outnumbered by mid-level (1,297) and senior (1,112) 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 353 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (426 positions), with 2,399 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 $218,800. Top-quartile roles start at $272,100, and the 90th percentile reaches $329,028. 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 $306,000 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 (1,423 postings), Aws (831 postings), Azure (654 postings), Rag (638 postings), Gcp (459 postings), Pytorch (432 postings), Prompt Engineering (418 postings), Claude (369 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.
AI Hiring Overview
The AI job market has 2,838 open positions tracked in our dataset. By seniority: 76 entry-level, 1,297 mid-level, 1,112 senior, and 353 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (426 positions). The remaining 2,399 roles require on-site or hybrid attendance.
The market median for AI roles is $218,800. Top-quartile compensation starts at $272,100. The 90th percentile reaches $329,028. Highest-paying categories: AI Safety ($306,000 median, 19 roles); Research Engineer ($300,000 median, 133 roles); AI Architect ($254,798 median, 61 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Frequently Asked Questions
Frequently Asked Questions
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