Staff Machine Learning Engineer - AI Platform

About PhantomBuster

PhantomBuster is a web automation and data extraction SaaS that allows businesses to grow faster. We enable thousands of companies to boost their growth by finding and connecting with their ideal customers seamlessly.

Founded in 2016, PhantomBuster developed a toolbox of over 120 flows (Phantoms) to help businesses scale their sales and marketing processes. We allow our users to automate finding and enriching data about their potential customers and leverage that data to connect with them. It's never been easier for non-technical people to extract the data they need, automate actions, and get their work done better and faster.

We are a team of 70 passionate and kind people looking to help more businesses save time on repetitive tasks and focus on what matters. Join us so you can enjoy working at a self-funded, profitable, remote, product-led company!

About the Data Department

You will join our Data Department to support the development of Phantom Intelligence, the platform that powers our product's AI capabilities.

The Data Department currently consists of two teams:

• Analytics Team: composed of two Analytics Engineers, a Data Analyst, and an Analytics Manager.
• Machine Learning Team: composed of one Machine Learning Engineer.

Our stack relies on AWS for AI features and the data lake, and on Snowflake for the data warehouse. We also use transformation tools like DBT and visualization tools like Tableau. Data is also available through self-service tools such as Amplitude, Google Analytics, and ChartMogul.

Your missions

• Define and evolve our infrastructure to allow for better ML and AI capabilities, with a focus on LLM-based and agentic systems.
• Contribute to the development and expansion of our agentic AI framework powered by AWS Bedrock, enabling both internal tools and customer-facing features.
• Identify, source, and refine datasets to allow tuning models, powering retrieval pipelines, or expanding agentic workflows.
• Pre-process data by using techniques such as data cleaning, feature engineering, and transformation.
• Train, evaluate, and deploy both LLM-based systems and traditional machine learning models into production.
• Monitor, debug, and continuously improve deployed models and AI tools.
• Support machine learning usage throughout the company, including selecting the right modeling approach for the use case (LLM vs. traditional ML).
• Support the integration and use of LLMs, including approaches such as fine-tuning, prompt tuning, and retrieval-augmented generation (RAG), to improve accuracy.

You might be a fit if:

• You have an analytical mindset.
• You strive to understand business challenges and leverage ML and LLMs to solve them.
• You stay calm in the face of challenges and have an infectious, can-do attitude to solving problems.
• You have an aptitude for digesting complex ML or AI concepts and effectively communicating them to both technical and non-technical audiences.
• You are a team player with high integrity - you can remain flexible as we grow.
• You are autonomous and rigorous.
• You're resourceful - you might not have all the answers, but you are ready to find them.

Requirements

• 5+ years of experience as a Data Scientist or Machine Learning Engineer.
• Experience working with LLMs (e.g., prompt engineering, fine-tuning, retrieval-augmented generation).
• Experience working with Agents for Amazon Bedrock or similar agent orchestration setups.
• Strong understanding of machine learning algorithms, statistical methods, and data preprocessing techniques.
• Experience with cloud platforms for model training and deployment, especially AWS.
• Proficiency in Python, including experience with libraries such as Scikit-Learn, NumPy, Pandas, and PyTorch/TensorFlow.
• Proficiency in SQL and experience working with data warehouses (e.g., Snowflake, GCP).
• Knowledge of MLOps best practices, including CI/CD pipelines, model monitoring, and versioning (e.g., MLflow, Airflow).
• Experience deploying models to production and supporting them post-deployment.
• Fluency in English.

Bonus points

• Experienced in a SaaS B2B business.
• Experienced within a product-led growth company.
• Contributions to open-source projects related to ML or AI.

What's in it for you?

• Fully remote working environment.
• Freedom to make an impact at a small, self-funded, and profitable tech startup by laying the foundation for machine learning and AI.
• Collaborative and open-minded culture based on rationality, humility, honesty, and long-term thinking.
• Benefits and perks are described below.

Hiring Process

1. Screening with our Talent Acquisition Partner, Jakub (45 min).
2. Job Fit interview with our co-CTO, Nicolas (60 min) + a Data Team Member.
3. Remote exercise.
4. Exercise debriefing with two Data Team Members (60 min).
5. Cultural fit interview with two Colleagues from other departments (60 min).