Staff Machine Learning Engineer

**Important note**:
**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, 4-day workweek company

**About the Data Team**:
As the first Machine Learning Engineer, you will join our Data Team to drive the adoption and development of machine learning and AI capabilities within our product.

The Data Team currently consists of three Analytics Engineers, a Data Analyst, and a Head of Data. We are independent and support every team at PhantomBuster.

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

**Your missions**:

- Define and evolve our infrastructure to allow for better ML capabilities.
- Identify, source, and refine datasets to allow tuning models and create ML opportunities.
- Pre-process data by using techniques such as data cleaning, feature engineering, and transformation.
- Train, evaluate, and deploy machine learning models up to production.
- Monitor, debug, and continuously improve deployed models.
- Support machine learning usage throughout the company.
- Support the integration and use of LLMs, including approaches such as fine-tuning, RAG, etc., to improve accuracy.

**You might be a fit if**:

- You have an analytical mindset.
- You strive to understand business challenges and leverage ML 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 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 are 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.
- 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).
- Strong understanding of machine learning algorithms, statistical methods, and data preprocessing techniques.
- Experience with cloud platforms for model training and deployment.
- Knowledge of MLOps best practices, including CI/CD pipelines, model monitoring, and versioning (e.g., MLflow, Airflow).
- Successful deployment of models to production.
- Fluency in English.

**Bonus Points**:

- Experienced in a SaaS B2B business.
- Experienced within a product-led growth company.
- Experienced with large language models and generative AI.

**Why join PhantomBuster?**:

- Fully remote working environment.
- Possibility to work 4 days a week after your probation period.
- Freedom to make an impact at a small, self-funded, and profitable tech startup by laying its foundation for machine learning and AI.
- Benefits and perks are described below.

**Recruitment process**:

- Screening Video Call with our Talent Partner, Jakub (45 minutes).
- Job Fit Interview with Nicolas & one more Data Team Member (60 minutes).
- Remote exercise (one week to do it).
- Exercise debriefing with a Data Team Member and Nicolas (60 minutes).
- Culture Fit Interview with two Colleagues from other departments (60 minutes).