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At a glance
- Employment type
- Full-time
- Location
- Ireland · Remote within Ireland
- Workplace
- Remote
- Category
- AI
Technologies & skills
Primary technologies
What you'll be doing
Join Twilio as a Machine Learning Engineer on the Conversation Intelligence team to design, develop, and deploy AI-powered features that extract meaning from voice and messaging data. Collaborate with experienced ML practitioners to build scalable ML solutions and production inference services.
- Design and develop machine learning solutions ensuring accuracy, performance, security, and scalability
- Implement and maintain end-to-end AI/ML pipelines from data ingestion to model deployment
- Instrument AI/ML services with metrics, logging, and telemetry to monitor performance and operational health
- Participate in on-call rotations, execute progressive rollouts, and apply mitigation strategies for production inference services
Key requirements
Must-have
- Bachelor's degree in Computer Science, Mathematics, Statistics, or related field, or equivalent practical experience
- 2+ years of experience in machine learning engineering or applied ML
- Proficiency in Python and at least one ML framework (PyTorch, TensorFlow, or JAX)
- Familiarity with NLP libraries such as Hugging Face Transformers, NLTK, or SpaCy
- Experience developing, testing, and deploying ML services or features in a collaborative engineering environment
- Experience with model versioning, experiment tracking, and cloud-based infrastructure (AWS, GCP, or Azure)
- Experience utilizing Large or Small Language Models within software systems
Nice-to-have
- Hands-on experience with conversational AI, or LLM fine-tuning and prompt engineering in production
- Exposure to agentic AI frameworks such as LangGraph, AutoGen, CrewAI
Experience: 2+ years in machine learning engineering or applied ML
Role signals
- Technical focus
- machine learning engineering, NLP, AI/ML pipelines, production inference
- Hands-on vs management
- Hands-on
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Full job description
Who we are
At Twilio, we’re shaping the future of communications, all from the comfort of our homes. We deliver innovative solutions to hundreds of thousands of businesses and empower millions of developers worldwide to craft personalized customer experiences.
Our dedication to remote-first work, and strong culture of connection and global inclusion means that no matter your location, you’re part of a vibrant team with diverse experiences making a global impact each day. As we continue to revolutionize how the world interacts, we’re acquiring new skills and experiences that make work feel truly rewarding. Your career at Twilio is in your hands.
We use Artificial Intelligence (AI) to help make our hiring process efficient. That said, every hiring decision is made by real Twilions!
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See yourself at Twilio
Join the team as Twilio’s next Machine Learning Engineer.
About the job
This position is to design and engineer AI powered features that makes every customer conversation smarter. As a Machine Learning Engineer on the Conversation Intelligence team, you'll develop and deploy solutions that extract meaning from voice and messaging data at Twilio scale. You'll work alongside experienced ML practitioners to ship real features - from model pipelines to production inference - that directly shape how businesses understand their customers.
Responsibilities
In this role, you’ll:
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Design and development of machine learning solutions, ensuring accuracy, performance, security, and scalability.
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Implement and maintain end-to-end AI/ML pipelines - from data ingestion and feature engineering through to model development, validation, and deployment with guidance from senior engineers on complex architectural decisions
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Instrument AI/ML services with appropriate metrics, logging, and telemetry to monitor model performance and operational health against defined SLOs
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Participate in on-call rotations, executing progressive rollouts and applying standard mitigation strategies to keep production inference services healthy
Qualifications
Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to apply. If your career is just starting or hasn't followed a traditional path, don't let that stop you from considering Twilio. We are always looking for people who will bring something new to the table!
*Required:
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Bachelor's degree in Computer Science, Mathematics, Statistics, or a related quantitative field, or equivalent practical experience
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2+ years of experience in machine learning engineering or applied ML, with demonstrated proficiency in Python and at least one ML framework (PyTorch, TensorFlow, or JAX) and familiarity with NLP libraries such as Hugging Face Transformers, NLTK, or SpaCy.
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Experience developing, testing, and deploying small-to-medium scoped ML services or features in a collaborative engineering environment, including model versioning, experiment tracking, and cloud-based infrastructure (AWS, GCP, or Azure)
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Proficiency in Python (preferred) or similar OO language.
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Experience utilizing Large (or Small) Language Models within software systems.
Desired:
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Hands-on experience with conversational AI, or LLM fine-tuning and prompt engineering in a production context
-
Exposure to agentic AI frameworks such as LangGraph, AutoGen, CrewAI
Location
Travel
We prioritize connection and opportunities to build relationships with our customers and each other. For this role, you may be required to travel occasionally to participate in project or team in-person meetings.
What We Offer
Working at Twilio offers many benefits, including competitive pay, generous time off, ample parental and wellness leave, healthcare, a retirement savings program, and much more. Offerings vary by location.
Twilio thinks big. Do you?
We like to solve problems, take initiative, pitch in when needed, and are always up for trying new things. That's why we seek out colleagues who embody our values — something we call Twilio Magic. Additionally, we empower employees to build positive change in their communities by supporting their volunteering and donation efforts.
So, if you're ready to unleash your full potential, do your best work, and be the best version of yourself, apply now! If this role isn't what you're looking for, please consider other open positions.
Twilio is proud to be an equal opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Qualified applicants with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Additionally, Twilio participates in the E-Verify program in certain locations, as required by law.
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
Based on this listing, the Machine Learning Engineer role at Twilio focuses on designing, developing, and deploying AI-powered features for conversation intelligence, with an emphasis on NLP, production ML pipelines, and operational excellence in a collaborative, remote-first environment.
- Hands-on ML engineering with NLP/LLMs·High
- Production deployment and operational monitoring·High
- Collaboration in remote, cross-functional teams·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Twilio's Conversation Intelligence Products
0–5 minSpend time understanding Twilio's offerings and how conversation intelligence fits into their platform.
Refresh NLP and LLM Implementation Skills
5–13 minPractice using Hugging Face, NLTK, SpaCy, and integrating LLMs in Python projects.
Revisit ML Pipeline Deployment and Monitoring
13–20 minGo over your experience with end-to-end ML pipelines, focusing on deployment, monitoring, and incident response.
Prepare STAR Stories for Key Responsibilities
20–25 minDraft concise examples from your experience that align with the listed responsibilities and required skills.
Formulate Insightful Questions for the Interview
25–30 minSelect and tailor questions to ask the interviewer based on your research and interests.
Talking points
, 5 itemsDesigning and Deploying ML Solutions
Be ready to discuss specific examples where you have built and shipped ML models, focusing on accuracy, scalability, and security.
NLP Libraries and LLM Integration
Prepare to explain your experience with NLP tools (e.g., Hugging Face, NLTK, SpaCy) and how you've utilized LLMs in real-world applications.
ML Pipeline Instrumentation and Monitoring
Demonstrate your approach to tracking model performance, logging, and ensuring operational health in production environments.
Cloud-based ML Infrastructure
Showcase your familiarity with deploying ML services on cloud platforms (AWS, GCP, Azure) and managing model versioning and experiment tracking.
Collaboration and On-call Responsibilities
Share experiences working in collaborative teams, participating in on-call rotations, and handling production incidents or rollouts.
What to research
, 4 itemsTwilio's Conversation Intelligence Team
Research Twilio's products and specifically their conversation intelligence offerings to understand the business context.
NLP and LLM Tools
Review documentation and recent updates for Hugging Face Transformers, NLTK, SpaCy, and LLM integration best practices.
Cloud ML Deployment
Refresh your knowledge of deploying ML models on AWS, GCP, or Azure, focusing on model versioning and monitoring.
Operational Excellence in ML
Study best practices for instrumenting ML services with metrics, logging, and telemetry, and review incident response strategies.
Questions to ask
, 6 itemsWhat are the main challenges the Conversation Intelligence team is currently facing with ML deployment at scale?
Why ask this? Shows your interest in the team's real-world problems and readiness to contribute.
How does the team approach model monitoring and incident response for production ML services?
Why ask this? Demonstrates your concern for operational reliability and learning culture.
What opportunities are there for hands-on involvement with LLM fine-tuning or prompt engineering?
Why ask this? Clarifies the depth of NLP/LLM work and aligns your interests with the role.
How is collaboration structured between ML engineers and other teams (e.g., product, data engineering) at Twilio?
Why ask this? Helps you understand cross-functional workflows and communication expectations.
What tools and frameworks does the team currently use for experiment tracking and model versioning?
Why ask this? Gives insight into the technical stack and your potential learning curve.
How does Twilio support remote-first collaboration and professional development for ML engineers?
Why ask this? Assesses the company's commitment to remote work and your growth.
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