Machine Learning Engineer

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning Engineer (7 months, remote) requiring expertise in Python, fraud detection, and credit risk management. Candidates must have 3+ years in ML model development within the fraud/risk domain and experience with Airflow and large-scale production systems.
🌎 - Country
United States
💱 - Currency
$ USD
💰 - Day rate
Unknown
Unknown
🗓️ - Date discovered
April 18, 2025
🕒 - Project duration
More than 6 months
🏝️ - Location type
Remote
📄 - Contract type
W2 Contractor
🔒 - Security clearance
Unknown
📍 - Location detailed
United States
🧠 - Skills detailed
#Strategy #Storage #GCP (Google Cloud Platform) #Cloud #Data Architecture #Neural Networks #Apache Beam #Infrastructure as Code (IaC) #GIT #Public Cloud #Airflow #Python #Leadership #NLP (Natural Language Processing) #Linux #ML (Machine Learning) #Anomaly Detection #BigQuery #Dataflow #Scala #Programming #Debugging #Deep Learning #Docker #Data Processing #Reinforcement Learning
Role description

Title: Sr Software Engineer- Machine Learning

Location: Remote

Duration: 7 Months

Interview process: 30-min manager screen, followed by a 60 min technical assessment (technical deep dive) with 1 member of the hiring team

Only W2 or self corp……………………

Job Description:

Python programming skills for machine learning applications in the risk/fraud domain are expected

Got anyone for this – big name companies and ideal fraud/risk space!!!

About The Team

The Risk Decisioning team keeps Etsy a safe and trusted marketplace by building advanced and scalable ML technologies to detect and prevent risk and fraud. We currently have 8 engineers on the team, with a mix of ML Engineers, Applied Scientists, and Full Stack Engineers.

About The Role

We are looking for someone who is passionate about applying ML to deliver customer impact and have the domain expertise and vision to serve as the team’s technical leader in ML. Our work addresses pressing, real-world problems, including detection of transactional fraud, fake account creation, collusion fraud, and more. As a member of the engineering team, this role is a great opportunity to collaborate with Senior ML professionals on large-scale projects protecting millions of users.

Typical responsibilities of this role include:

   • Solve customer and business problems (protecting against seller fraud, transactional fraud, account takeover, fake accounts, etc.) using machine learning techniques like graph ML, deep neural networks, and anomaly detection.

   • Take ideas and scale them to millions to users. Develop and implement end-to-end plans, including idea generation, project planning, model development, production model serving, and ownership of model performance.

   • Architect new and improve on existing ML systems, including building data architectures (e.g. large-scale graph data processing and storage), enabling faster and more robust model development, and improving model serving and orchestration.

   • Provide technical leadership on long-term strategy, roadmap, ML design, and system architecture.

   • Help to coach and mentor more junior team members.

   • Collaborate with team members and cross-team partners for problem identification, technical design/delivery, and product operationalization.

   • Share impactful and innovative work in the wider ML research community, including presenting at top-tier ML conferences such as: KDD, ICML, NeurIPS, etc.

   • Of course, this is just a sample of the kinds of work this role will require! You should assume that your role will encompass other tasks, too, and that your job duties and responsibilities may change from time to time at Etsy's discretion, or otherwise applicable with local law.

Nice To Have

   • BigQuery

   • Dataflow (Apache Beam)

   • Google Cloud Platform (GCP) for Machine Learning

Requirements

Must-Haves

   • Airflow

   • Credit risk management

   • Excellent Python programming skills

   • Fraud Detection

   • Machine Learning Algorithms

   • You have 3+ years experience building ML models in the fraud/risk management space.

   • Experience deploying, debugging, and fine-tuning machine learning models in large-scale production systems in public clouds, with experience in Infrastructure as Code.

   • You are comfortable with using git, Linux environments, dockers, and other tools for writing robust, production-ready code.

   • You have focused experience deploying models in production at scale like unsupervised anomaly detection, graph neural network, deep learning, natural language processing, or reinforcement learning.

Nice-to-Haves

   • Google Cloud Platform (GCP) experience is a plus