Director, Machine Learning Engineer

Lead large-scale machine learning engineering efforts to deliver production models for financial services at cloud scale, working onsite in McLean, VA.

Responsibilities

• Deliver machine learning models and software components to address challenging business problems in financial services, partnering with Product, Architecture, Engineering, and Data Science teams
• Drive creation and evolution of ML models and engineering systems for state-of-the-art intelligent applications
• Lead customer-focused, large-scale ML initiatives
• Apply cloud-based architectures and technologies to deliver optimized ML models at scale
• Optimize data pipelines that supply training and inference for ML models
• Develop using programming languages such as Python, Scala, or Java
• Promote best practices across the engineering and modeling lifecycle
• Recruit, nurture, and retain top engineering talent
• Serve as a force-multiplier by balancing hands-on technical work with innovation, mentoring, and elevating peers and junior engineers
• Stay current on technology trends through experimentation and learning; participate in internal and external technology communities; mentor other engineers

Requirements

• Bachelor’s degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
• At least 3 years of people leadership experience
• At least 8 years of programming experience with Python, Java, Golang, or C++
• At least 6 years of ML experience using industry standard frameworks PyTorch or Tensorflow, plus libraries including Pandas, NumPy, Scikit-learn
• At least 6 years operating large-scale distributed systems (including Spark and Ray) to prepare AI or ML data
• At least 5 years deploying and operating production ML solutions and production services in the cloud (AWS, GCP, Azure), using Kubernetes for large-scale containerized ML software systems

Technologies

• Python, Scala, Java, Golang, C++
• PyTorch, Tensorflow, Pandas, NumPy, Scikit-learn
• Spark, Ray
• AWS, GCP, Azure
• Kubernetes
• Open source frameworks, distributed systems, cloud-based architectures

Benefits

• Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
• Comprehensive, competitive, and inclusive set of health, financial and other benefits

Preferred Qualifications

• Master’s or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field
• 5+ years of experience managing and leading an engineering team
• 3+ years of experience architecting and designing resilient, large-scale production machine learning systems from data preparation through model training and inference
• 5+ years of experience optimizing ML algorithms, configurations, and infrastructure
• 5+ years of experience with ML techniques (Supervised, semi-supervised, unsupervised, reinforcement learning) and model types (Regression, Classification, Clustering), including model architectures (RNNs, CNNs, LSTMs, Transformers)
• 5+ years of experience with training concepts (loss function, hyperparameters, regularization) and evaluating model accuracy, diagnosing and addressing common issues (underfitting, overfitting)
• 7+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
• Ability to communicate complex technical concepts clearly to varied audiences
• Driving impacts in the ML industry through conference presentations, papers, blog posts, open source contributions, or patents
• Experience hiring and developing high-performing ML engineers with an inspiring leadership style
• Highly developed interpersonal, presentation, and communications skills

Location: McLean, VA (onsite)

Salary: USD 269,100 - 307,200 per year