QDStaff · 11 hours ago
Machine Learning Engineer
New York, NY
Full-time
Hybrid
Mid Level
$140K/yr - $200K/yr
3+ years expQDStaff is a next-generation investment and technology firm located in New York City, seeking an experienced Machine Learning Engineer. The role involves architecting, building, and maintaining robust ML/AI pipelines to support investment research, underwriting, and multi-asset trading strategies, ensuring models are deployed efficiently and monitored transparently.
Computer Games
Responsibilities
Design, build, and maintain scalable ML/AI pipelines for retraining and live or batch inference—ensuring reliability, transparency, and full traceability
Develop and implement monitoring systems that track model health, detect data drift, and assess prediction performance over time
Collaborate closely with data scientists and investment teams to create efficient workflows, including feature stores, data pipelines, and inference tools
Ensure high availability, scalability, and resilience for all deployed models across real-time and offline use cases
Own infrastructure that powers the data science ecosystem, including compute environments, storage layers, automated retraining systems, and self-serve deployment frameworks
Document pipelines, data lineage, and usage protocols to support auditing, troubleshooting, and knowledge sharing
Optimize system performance, evaluate new tools/technologies, and implement MLOps best practices
Spearhead development of advanced agentic workflows—integrating MCPs, orchestrating AI agent lifecycles, and maintaining reliable hosting environments
Ensure end-to-end observability and transparency across all ML/AI system components
Support broader data pipeline buildout and integration initiatives
Qualification
Required
Master's degree required
3-5 years of experience as an ML Engineer, MLOps Engineer, or similar role
Proficiency developing and managing complex ML/AI deployment pipelines using modern orchestration tools
Background with large-scale data systems, distributed storage, and cloud infrastructure
Strong experience monitoring model health, data drift, and consumption metrics
Solid understanding of both batch and real-time inference pipelines, including production-grade APIs
Excellent documentation and communication skills
Passion for designing resilient, scalable, and transparent systems that elevate data-driven decision making
Familiarity with common ML models and deployment workflows
Experience with model-serving platforms such as SageMaker, Vertex AI, BentoML, etc
Ability to build pipelines for live and batch inference at scale
Skilled in managing large numbers of pipelines and implementing monitoring for data flow, health, and accuracy
Experience with open-source LLMs and tools for hosting/serving large models
Experience fine-tuning and evaluating LLMs in production environments
Familiarity with agentic workflow frameworks
Experience building, deploying, and monitoring agentic pipelines and AI agents
Familiarity with data pipeline and orchestration tools (dbt, Prefect, Snowpipe, etc.)
Knowledge of ML servicing tools such as Feature Store, Airflow, Ray, etc
Comfortable managing large compute environments for intensive modeling and experimentation
Familiarity with Kubernetes and associated open-source tools
Benefits
Performance-based bonus.
Comprehensive health, dental, and vision insurance.
Retirement savings plan with company match.
Hybrid work structure with flexibility and strong team support.
Company
QDStaff
Welcome to QDStaff - Your Premier Talent Engine for Hit Games! At QDStaff, we're not your average recruiting firm.
11-50 employees
Funding
Current Stage
Early StageCompany data provided by crunchbase