Machine Learning Engineer – USA – NLP + Quant Trading Models (Remote)

Netflix is seeking a highly skilled and intellectually curious Machine Learning Engineer (NLP + Quant Trading Models) to join our advanced data science and AI team. This remote, full-time position offers a unique opportunity to work at the intersection of natural language processing (NLP), financial modeling, and quantitative trading strategies, helping Netflix optimize business and content investment decisions using next-generation AI technologies.

In this role, you will design and deploy scalable machine learning models that leverage text analytics, predictive modeling, and algorithmic trading frameworks. You’ll collaborate with cross-functional teams including data scientists, quantitative researchers, engineers, and finance analysts to create intelligent systems that can identify opportunities, predict market trends, and enhance strategic decision-making through data-driven insights.

If you have deep expertise in machine learning, NLP, and quantitative modeling — and thrive in an environment that values creativity, innovation, and high performance — this role is designed for you.

Key Responsibilities:
• Design, train, and deploy machine learning models for NLP-based data analysis and quantitative financial forecasting.
• Develop algorithms for automated pattern recognition, sentiment analysis, and financial time-series modeling.
• Integrate AI-driven predictive systems into Netflix’s decision-making and content investment processes.
• Build scalable pipelines for data collection, cleaning, and feature engineering across structured and unstructured data sources.
• Collaborate closely with data scientists, engineers, and product stakeholders to define model objectives and evaluate performance metrics.
• Experiment with transformer-based architectures (e.g., BERT, GPT, LLaMA) and other deep learning frameworks to enhance language understanding capabilities.
• Utilize advanced quantitative trading techniques and statistical models to simulate performance and forecast ROI.
• Continuously monitor, evaluate, and refine model performance for accuracy, scalability, and fairness.
• Document models, algorithms, and methodologies for reproducibility and transparency.
• Stay current with the latest advancements in AI, deep learning, and quant modeling to integrate best practices into Netflix’s AI ecosystem.

Required Skills and Qualifications:
• Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, Applied Mathematics, or Quantitative Finance.
• 4+ years of hands-on experience in machine learning, NLP, or quantitative modeling.
• Strong proficiency in Python and ML libraries such as TensorFlow, PyTorch, Scikit-learn, Hugging Face Transformers, and NumPy.
• Proven experience building and deploying NLP models (language modeling, sentiment analysis, topic modeling, summarization, etc.).
• Solid understanding of quantitative trading models, statistical arbitrage, and time-series forecasting.
• Experience working with large-scale data processing tools (e.g., Spark, Databricks, or Kafka).
• Strong mathematical foundation in probability, statistics, and optimization.
• Familiarity with cloud-based ML platforms (AWS SageMaker, GCP Vertex AI, or Azure ML).
• Excellent problem-solving skills, attention to detail, and an analytical mindset.
• Effective communication and collaboration skills in a distributed remote environment.

Experience:
• Minimum 4–7 years of professional experience in machine learning, quantitative research, or applied AI.
• Prior experience in financial analytics, algorithmic trading, or investment modeling is highly preferred.
• Proven record of deploying end-to-end ML systems from data ingestion to model deployment and monitoring.

Working Hours:
• Full-Time, Remote (USA)
• Standard work schedule: Monday to Friday, flexible hours across U.S. time zones.
• Occasional collaboration meetings with global teams may require flexible scheduling.

Knowledge, Skills, and Abilities:
• Deep understanding of NLP concepts, deep learning architectures, and quantitative finance principles.
• Proficiency in data visualization tools (e.g., Plotly, Tableau, or Power BI) for communicating results.
• Strong ability to translate complex analytical findings into actionable business insights.
• Familiarity with MLOps best practices, version control (Git), and CI/CD pipelines.
• High adaptability to emerging AI trends, especially in multimodal learning and reinforcement learning.
• A growth mindset with a passion for continuous learning and experimentation.

Benefits:
• Competitive compensation and performance-based bonuses.
• Comprehensive health, dental, and vision insurance coverage.
• 401(k) with company match and financial wellness programs.
• Unlimited paid time off (PTO) and flexible work hours.
• Remote-first culture with opportunities to collaborate across global teams.
• Annual learning and development stipend for certifications or advanced training.
• Access to state-of-the-art computing infrastr