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About This Role
Date: Sep 28, 2026
Location: Waltham, MA, US, 2451
Company: Dentsply Sirona, Inc
Requistion ID: 84236
Dentsply Sirona is the world’s largest manufacturer of professional dental products and technologies, with over a century of innovation and service to the dental industry and patients worldwide. Dentsply Sirona develops, manufactures, and markets a comprehensive solutions offering including dental and oral health products as well as other consumable medical devices under a strong portfolio of world class brands.
Dentsply Sirona’s products provide innovative, high-quality, and effective solutions to advance patient care and deliver better and safer dental care. Dentsply Sirona’s headquarter is located in Charlotte, North Carolina. The company’s shares are listed in the United States on NASDAQ under the symbol XRAY. Visit www.dentsplysirona.com for more information about Dentsply Sirona and its products.
Dentsply Sirona's software engineering and cloud operations team is seeking a Senior Software Engineer II to focused on AI-driven automation across one or more products, services, or platforms. This is a senior individual contributor role combining hands-on software engineering, technical expertise, and architectural influence. You will design, build, and operate AI-powered automation capabilities, collaborate across teams, and help drive responsible adoption of AI technologies through engineering best practices, governance, and operational excellence.
Responsibilities for Employment -
Technical Leadership & Delivery
- Lead the technical design and implementation of AI automation initiatives within one or more epics or projects.
- Collaborate with Product, UX, QA, Security, and Cloud Operations teams to deliver high-quality solutions.
- Influence technical direction through architecture discussions, design reviews, and engineering best practices.
- Mentor and support engineers through code reviews, technical guidance, and knowledge sharing.
Technical Implementation & Development
- Design and implement AI-enabled automation workflows for parts of the software development lifecycle (requirements/story generation, documentation scaffolding, code scaffolding) within the program area.
- Design and implement RAG pipelines, lightweight intelligent agents, and MCP or equivalent integrations to support engineering workflows.
- Integrate AI-assisted code generation and validation into CI/CD with guardrails, automated checks, feature flags, staged rollouts and safe rollback conditions.
- Ensure human-in-the-loop checkpoints, prompt and policy guardrails, provenance/traceability, and least-privilege access controls for embeddings and logs.
- Prepare runbooks, dashboards and alerts; participate in incident responses and contribute to post-incident reviews for owned services.
- Monitor and optimize reliability, latency, correctness and cost of AI features; manage model drift and retrieval quality metrics.
- Continuously evaluate and integrate emerging AI technologies to improve developer productivity, quality and efficiency.
Governance, safety & quality
- Define and enforce team-level AI governance: data handling policies, retention, access control and regulatory traceability where required.
- Establish detection of metrics and validation processes to detect hallucinations, data leakage and model drift; automate tests where feasible.
- Ensure delivered features include automated tests, documentation, and observability (logs, metrics, traces).
Qualifications
Education:
- BS or MS in computer science or a related engineering discipline.
Years and Type of Experience:
- 8+ years as a Software Engineer, Full Stack Engineer or comparable role.
- Demonstrated experience leading complex technical initiatives, driving architecture decisions, and delivering production-grade software systems.
- Proven track record designing and delivering production-grade systems and driving technical excellence across projects.
- Hands-on experience integrating AI components (RAG, embeddings/vector DBs, light agents, MCP-style integrations) into engineering workflows.
- Experience with distributed systems, APIs and service-level design.
- Experience contributing to operational readiness, incident response, and service reliability practices.
Key Skills, Knowledge & Capabilities:
- Proven pragmatic system and service design skills at program scope.
- Deep understanding of scalability, performance optimization and cost trade‑offs for AI and platform services.
- Practical experience with cloud platforms (AWS/Azure/GCP), containerization (Docker) and exposure to Kubernetes.
- Experience with CI/CD tooling (GitHub Actions/Jenkins/GitLab), feature-flagging, and Infrastructure-as-Code (Terraform/ARM).
- Working knowledge of RAG pipelines, embedding stores/vector DBs, prompt engineering and retrieval-quality evaluation.
- Strong testing and observability discipline: unit/integration tests, logging, metrics and distributed tracing.
- Awareness of data privacy, access controls and secure handling of embeddings/logs.
- Strong collaboration, stakeholder engagement, and communication skills with the ability to influence technical decisions across teams.
- Excellent English communication skills (written and spoken); German advantageous.
- Regulated-industry (medical device) experience is a plus.
Nice to have
- Experience integrating AI checks into CI/CD (policy checks, automated test generation, documentation synthesis).
- Familiarity with model evaluation, offline/online metrics and A/B testing for ML features.
- Experience with MCP or similar context-sharing protocols.
How We Work the DS Way - Key Leadership Behaviors
- Actively articulates and promotes Dentsply Sirona’s vision, mission and values.
- Advocates on behalf of the customer.
- Promotes high performance, innovation and continual improvement.
- Consistently meets Company standards, ethics and compliance requirements.
- Clear and effective communication with stake holders, which span across multiple levels, socio-geographic areas and functional expertise.
The base salary and target annual incentive for this role located in Massachusetts is between $176,000 - $198,000. Actual compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, and specific office location.
Equal Opportunity Employer: Dentsply Sirona is an Equal Opportunity Employer. All qualified applicants will be considered without unlawful discrimination or regard for race, color, religion, sex, sexual orientation, sexual or gender identity, national or ethnic origin, age, marital status, disability, genetic factors, military and veteran status, or any other characteristics protected by applicable local law.
Eligibility: All successful applicants must be eligible to work in the country the position is based.
Assistance: If you need assistance with completing the online application due to a disability, please send an accommodation request to [email protected].
Agencies: Please note that Dentsply Sirona does not accept or respond to unsolicited requests or applications submitted by Recruitment Agencies/ Search Firms.
Notice on Fraudulent Job Offers: Unfortunately, we are aware of third parties that pretend to represent our company offering unauthorized employment opportunities. If you think a fraudulent source is offering you a job, please have a look at the following information: careers.dentsplysirona.com.
Salary Context
This $176K-$198K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Dentsply Sirona, 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 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($187K) sits 12% below the category median. Disclosed range: $176K to $198K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Dentsply Sirona AI Hiring
Dentsply Sirona has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Waltham, MA, US. Compensation range: $198K - $198K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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