AI/ML Engineer developing and deploying solutions for pharmacy technology at VXForward. Collaborating with cross - functional teams to enhance workflows and operational efficiency.
About the role
- Machine Learning Engineer focusing on end-to-end ML lifecycle at SandboxAQ. Joining AI Generation Engine to design and prototype AI-first products leveraging Large Quantitative Models.
Responsibilities
- Design, construct, and manage robust data pipelines for the training, validation, and continuous retraining of Large Quantitative Models (LQMs) and agentic frameworks
- Develop, implement, and rigorously test novel ML models and algorithms, defining appropriate metrics to ensure model performance aligns with high-level product objectives
- Lead the effort in cleaning, transforming, and engineering features from complex and large-scale datasets to optimize LQM performance and predictive accuracy
- Conduct deep analysis of model behavior, performance, and failure modes, tuning hyper-parameters and optimizing model architecture for efficiency, speed, and accuracy in a production context
- Collaborate closely with AI researchers, product managers, and SWEs to translate high-level business objectives into actionable ML development and deployment roadmaps
- Champion and enforce exceptional engineering standards for code quality, system efficiency, and security in a prototyping environment
- Drive technical execution with high autonomy, making critical design and implementation decisions independently
Requirements
- BS in Software Engineering, Computer Science, or equivalent field of study
- 8+ years of postgraduate experience in software development
- Experience developing highly-available, performant, scalable ML systems, including large-scale data processing pipelines
- Strong expertise in Python (including the ML stack: PyTorch, TensorFlow, JAX, NumPy, Pandas)
- Long, successful history of driving the full ML lifecycle: from initial data exploration and hypothesis testing to architecture, model training, evaluation, and production deployment
- Deep proficiency in MLOps and software best practices, including CI/CD for ML, experiment tracking (e.g., Weights & Biases, MLflow), automated testing, and version control for both code and datasets
Benefits
- Comprehensive medical, dental, and vision coverage for employees and dependents with generous employer premium contributions
- Retirement savings with company matching
- Paid parental leave
- Inclusive family-building benefits
- Flexible paid time off
- Company-wide seasonal breaks
- Support for flexible work arrangements that enable sustainable performance
- Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs
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