Interested in this AI/ML Engineer role at Triple Ring Technologies?
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About This Role
Triple Ring Technologies, Inc. offers a unique environment for talented individuals interested in undertaking varied technical challenges, primarily in medical device and life science industries. Headquartered in Newark, CA, we are an innovative research and development company that partners with clients to deliver complex technical solutions. Our highly interdisciplinary team includes senior professionals from industry, finance, and academia, with extensive experience in medical device, life science, clean tech, security, and industrial technologies. We rely upon each other for technical excellence, real\-world engineering and commercial wisdom. Learn more at www.tripleringtech.com.
Job Summary
Every HR team runs on a layer of operational work, that includes communications, reporting, documentation, and recurring manual steps. A lot of it is repetitive and a natural fit for thoughtful automation. This internship exists to build that automation.
You'll be embedded with the HR team, reporting to the Senior Director of IT \& Incubation, with sponsorship from the Chief People Officer and the HR Operations lead, to build AI\-powered tools that give the team back time for higher\-value work. That means using Microsoft 365 Copilot and Claude to design, prototype, test, and document working solutions, not slide decks about what could be built, but functional tools the HR team can use when you're done.
This role sits at the intersection of HR operations, software development, and AI tooling. It's genuinely self\-directed: you'll look at how a workflow runs, find where the leverage is, and build your way to a solution — with regular check\-ins and sponsors you can go to when you're stuck, rather than a daily task list. If that kind of ownership excites you, keep reading.
Responsibilities
- Shadow HR team members across HRBP, HR operations, and recruiting to map current workflows end\-to\-end
- Identify the highest\-volume, most repetitive manual tasks (documentation, communications, reporting, policy FAQ responses) and rank them by effort\-to\-impact ratio
- Define the build roadmap with sponsors before any code is written
- Conduct an IT/Legal data\-governance and threat\-modeling review of the roadmap to lock in data and security approach upfront
- Develop internal HR tools and automations using approved enterprise instances of Microsoft 365 Copilot, Claude, and other IT\-approved tools
- Write, review, test, correct, and augment AI\-produced code in an iterative cycle
- Partner with a TRT engineer for code review on anything moving toward real use
- Build and test exclusively against synthetic or fully de\-identified datasets signed off by an HR/IT data owner — no real employee data in the development loop
- Provide regular progress updates to HR leadership and sponsors
- Finalize tools with documentation sufficient for the HR team to independently operate and maintain them
- Confirm a permanent technical owner and runbook for any tool going into production
- Deliver a prioritized roadmap of remaining opportunities with effort and impact estimates
- Present findings and a 90\-day execution plan to HR leadership
Education, Experience, and Skills
- Demonstrated, hands\-on experience using generative AI tools to build software, real builds you can walk through, not theoretical familiarity (Claude, ChatGPT, Copilot, Gemini, or open\-source models all count)
- Currently enrolled in or recently completed a degree or program in a technical field (Data Science, Computer Science, or related) or equivalent demonstrated experience via self\-taught or bootcamp paths
- Ability to operate independently or semi\-independently, owning progress between check\-ins
- Ability to translate between technical and non\-technical colleagues, HR stakeholders aren't engineers; bridging that gap is core to the role
- Sound judgment handling sensitive and confidential organizational information
Strong Differentiators (Not Required)
- Experience with workflow\-automation tools (Power Automate, Zapier, Make, or similar)
- Hands\-on familiarity with Microsoft 365 Copilot or Claude in a professional or project context
- Prior work in HR, operations, or business process analysis
What You'll Walk Away With
- A portfolio of real tools you prototyped and, where approved through our governance process, deployed and used by the team, with documented outcomes
- Deep working knowledge of how AI tools are evaluated, governed, and implemented inside an enterprise HR function, including the data\-governance and privacy practices that distinguish enterprise\-grade AI work and will matter in every role you take after this
- Direct access and visibility to senior IT and HR leadership, and a real look at how a multidisciplinary medtech development company operates
- Sponsors who can speak to your technical judgment, not just your effort
The hourly range for jobs which are performed in US, Massachusetts is $18\.00 to $20\.00\. Please note that the salary range is a guideline and compensation may vary based on factors such as qualifications, skill level, experience, competencies and work location.
This role will be eligible for our hybrid work model which requires working onsite a minimum of 3 days per week: Tuesday, Wednesday, and 1\-day of your choice. In certain circumstances the hybrid work model may change to accommodate business needs.
*Triple Ring Technologies is an Equal Employment Opportunity Employer and is committed to workforce diversity, providing equal employment opportunities to all qualified applicants without regard to race, sex, sexual orientation, gender identity, national origin, color, age, religion, protected veteran or disability status, or genetic information.*
*Triple Ring Technologies complies with federal and state disability laws and makes reasonable accommodations for applicants and candidates with disabilities. If reasonable accommodation is needed to participate in the job application or interview process, please email* *[email protected]**.*
Salary Context
This $37K-$41K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Triple Ring Technologies, 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 $181,170 based on 12,692 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,880. This role's midpoint ($39K) sits 78% below the category median. Disclosed range: $37K to $41K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Triple Ring Technologies AI Hiring
Triple Ring Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $41K - $41K.
Location Context
AI roles in Boston pay a median of $215,350 across 442 tracked positions. That's 8% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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