Interested in this AI/ML Engineer role at Tulip Interfaces?
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About This Role
This role is located in Somerville, MA \- We are a hybrid work environment and are in the office 3\+ days/per week.
Tulip, the leader in AI\-native frontline operations, is helping companies around the world equip their workforce with composable, connected apps, leading to higher quality work, improved efficiency, and end\-to\-end traceability across operations. Tulip's cloud\-native, no\-code platform, powered by embedded AI, is driving the digital transformation of industrial environments through composable, human\-centric solutions that go beyond disrupting the Manufacturing Execution System (MES) category.
A spinoff out of MIT, Tulip is headquartered in Somerville, MA, with offices in Germany, Hungary, Singapore, Israel and Japan. Tulip has been recognized as a World Economic Forum Global Innovator, a 2024 Deloitte Technology Fast award winner, one of Energage's Top Workplaces USA, and one of Built In Boston's "Best Places to Work" and "Best Midsize Places to Work."
About You:
You have built with LLMs and agents in production, and you understand what it takes to make them reliable — prompting, tool design, and the handoffs between people and AI. You are technically sharp and commercially curious — you want to understand why something matters to the business, not just what to build. You want a small team with outsized impact, direct exposure to leadership, and your fingerprints on how the whole company operates.
What skills do I need?
- 5\+ years of software experience with hands\-on AI/LLM work, delivering AI solutions inside a company, or delivering them to clients in a consulting environment
- Hands\-on experience with LLMs, prompt engineering, agent frameworks, and RAG, built and shipped in production
- Comfort owning a full stack, including strong proficiency in TypeScript or similar and familiarity with API design and integration (RESTful services or similar)
- A track record of building internal tools that non\-technical teams actually adopt — earning trust with business leads, getting to the root of operational problems, and knowing when to build versus buy
- Nice to have: production experience with AWS or GCP, modern CI/CD practices, and a working understanding of AI safety and security
- Bachelor's degree in Computer Science, Engineering, or equivalent working experience
Key Responsibilities:
- Partner with business teams to identify the highest\-leverage AI opportunities across Tulip, then co\-design, build, and launch them
- Build and ship AI agents that non\-technical teams love using, automating workflows and unlocking new team capabilities
- Translate messy business problems into clear, ROI\-driven agentic solutions
- Help architect and build the agentic AI platform that powers Tulip internally, from foundational architecture and standards to secure, scalable integrations across our tech stack
- Track how agentic AI is developing outside Tulip and use that to shape our internal AI strategy
- Contribute to AI enablement across the company, including learning programs, coaching, and change management
Key Collaborators:
- Engineering, Operations, and Go\-To\-Market teams
- Finance and People teams
- IT and Data leadership
Working At Tulip
We know even great candidates experience imposter syndrome. Even if you don't match every requirement, applying gives you the opportunity to be considered.
We're building a strong, diverse team that values hard work, families, and personal well\-being.
Benefits of working with us include:
- Direct impact on product and culture
- Company equity
- Competitive benefits package including Health, Dental, Vision, Short\-term Disability, Long\-term Disability, Life Insurance, AD\&D Insurance, Flexible Spending Account (FSA), Commuter Benefits, Parental Leave, and 401(K)
- Flexible work schedule and unlimited vacation policy
- Learning \& Development program
- Virtual company events and happy hours
- Fitness subsidies
- An inclusive, dog\-friendly office with diverse and inspiring colleagues
We are an equal opportunity employer. At Tulip, we celebrate all. Qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. Help us build an inclusive community that will transform frontline operations.
The compensation information displayed on each job posting reflects the range for new hire pay rates for the position across all US locations. Within the range posted, actual compensation will be determined depending on multiple factors including job\-related knowledge \& skills, experience, business needs, geographical location, market compensation data, and internal equity. *Expected compensation ranges for this role may change over time.* The salary range for this position is $130,000 \- $180,000 per year.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Please note that we may use AI\-based tools to support parts of our hiring process. All data processing is carried out in compliance with local data protection laws, ensuring all personal candidate information is handled securely and ethically.
Salary Context
This $130K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Tulip Interfaces, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($155K) sits 28% below the category median. Disclosed range: $130K to $180K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Tulip Interfaces AI Hiring
Tulip Interfaces has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $180K - $180K.
Location Context
AI roles in Boston pay a median of $210,000 across 166 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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