Machine Learning Engineer

Our Mission

Our mission is to build the performance layer for the blended workforce: one standard, one system, where every human and AI agent is evaluated, coached, and improved together.

Today, Intryc is the platform where fast-growing and enterprise companies evaluate and train their human and AI CX agents. Our customers automate ticket, call, chat, and email scoring, surface patterns and insights in minutes instead of weeks, and turn every finding into targeted training. We are trusted by multi-billion dollar companies and breakout startups including Deel, MaintainX, Preply, Blueground, Fyxer AI, and Ziina.

But quality assurance and training are the beachhead, not the destination. The customer service and experience tech landscape is broken: fragmented tools, siloed data, dashboards that describe problems without fixing them. We intend to replace it. Our ambition is that every customer conversation, whether handled by a person or a model, is measured, understood, and made better by Intryc. When AI is expected to do the work of the best humans, someone has to define what best means and hold everyone to it. That someone is us.

About us

Behind Intryc is a team of ex-Meta, Amazon, Revolut, Confluent, and Twitter operators who spent over seven years raising the bar for customer experience inside those companies. We lived the painfully manual, repetitive, error-prone reality of support QA firsthand. We built Intryc so CX teams can focus on what actually matters: their customers and their business goals.

We went through Y Combinator (S24), we are growing fast, and our US client base is growing fastest of all, which is why we are scaling our presence from our San Francisco HQ.

Our Culture

We do not hide behind fancy titles or bureaucratic processes. We treat people equally and fairly and give them full freedom and autonomy to create something awesome. We back everything with data and logic. We make mistakes, learn from them, and move on. It is all about getting stuff done and owning what you do.

We maintain an extremely high talent density and we do not lower the bar for anything or anyone. We look for smart, ambitious, paranoid people who care deeply about their work and about other human beings. What we offer in return is high agency, real ownership, and real risk and reward.

Our Values

• Fired up and Get Shit Done
• Customer obsessed
• Be open, transparent, and accountable
• Be smart, humble, and empathetic
• Be frugal
• Take action if the outcome is reversible

Responsibilities

• Collaborate with the team to design, implement, and fine-tune ML models, focusing on LLM-based systems.
• Develop and enhance Retrieval-Augmented Generation (RAG) pipelines for intelligent query processing and knowledge retrieval.
• Experiment with and evaluate pre-trained models and fine-tune them for specific tasks.
• Work with large-scale datasets to ensure efficient indexing, retrieval, and contextual relevance.
• Monitor and improve the performance and scalability of deployed models.
• Stay updated with the latest research in LLMs, RAG, and related fields.
• Assist in debugging, testing, and deploying machine learning pipelines.
• Gather customer feedback and iterate on the models' accuracy based on customer use cases.

Qualifications

Required Skills:

• Bachelor's degree in Computer Science, Data Science, or a related field.
• Foundational knowledge of machine learning algorithms and model development.
• Understanding of LLMs (e.g., OpenAI GPT, Hugging Face models) and their applications.
• Basic knowledge of retrieval systems (e.g., Elasticsearch, FAISS, or vector databases).
• Familiarity with the RAG paradigm or similar architectures for context-aware systems.
• Proficiency in Python and common ML libraries.
• Strong problem-solving skills and a passion for learning new technologies.

Preferred Skills:

• Experience fine-tuning LLMs for domain-specific applications.
• Exposure to NLP tasks like summarization, translation, and Q&A systems.
• Knowledge of cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes).

Benefits

• You’ll get to work with incredible talent in a hot and fast-growing start-up in the Gen AI and LLM space.
• Generous stock options.
• Competitive salary.
• Remote first policy with 25 days of holiday a year plus bank holidays.
• Pension plan.
• Private health insurance.
• We’ll arm you with all of the latest tech equipment.

Interview Process

• Intro call with co-founder & CEO
• Intro call with Head of Engineering
• First technical interview
• Intro call with co-founder & CPO
• Final CEO call and offer