TWG Global · 13 hours ago
Staff / VP, Machine Learning Engineer (NYC)
New York, NY
Full-time
Onsite
Senior Level, Lead/Staff
$290K/yr - $3,000K/yr
8+ years expTWG Global drives innovation and business transformation across various industries, leveraging data and AI. As the Staff Machine Learning Engineer (VP), you will design and deploy advanced ML systems to enhance productivity and support data-driven decision-making across the enterprise.
Construction
Responsibilities
Architect and deploy ML systems and platforms that solve high-impact business problems across regulated enterprise environments
Lead the development of production-ready pipelines, including feature stores, model registries, and scalable inference services
Champion MLOps best practices (CI/CD for ML, model versioning, monitoring, observability) to ensure models are reliable, reproducible, and cost-efficient
Partner with Data Scientists to operationalize experimental models, enabling scalability and generalizability across diverse business domains
Integrate emerging ML engineering techniques (e.g., LLM deployment, fine-tuning pipelines, vector databases, RAG systems) into enterprise-ready solutions
Own the design of foundational ML platforms and frameworks that serve as building blocks for downstream AI applications
Embed controls, governance, and auditability into ML workflows, ensuring compliance with regulatory standards and responsible AI principles
Collaborate with Engineering, Product, and Security teams to embed ML-driven decision-making into enterprise platforms and workflows
Define and track engineering and model performance metrics (latency, scalability, cost, accuracy) to optimize systems in production
Mentor and coach ML engineers, fostering technical excellence, collaboration, and innovation within the AI Science team
Qualification
Required
8+ years of experience building and deploying machine learning systems in production environments at enterprise or platform scale
Proven track record of leading ML engineering projects from architecture to deployment, including ownership of production-grade systems
Deep expertise in ML frameworks and engineering stacks (TensorFlow, PyTorch, JAX, Ray, MLflow, Kubeflow)
Proficiency in Python and at least one backend language (e.g., Java, Scala, Go, C++)
Strong understanding of cloud ML infrastructure (AWS SageMaker, GCP Vertex AI, Azure ML) and containerized deployments (Kubernetes, Docker)
Hands-on experience with data and model pipelines (feature stores, registries, distributed training, inference scaling)
Knowledge of observability and monitoring stacks (Prometheus, Grafana, ELK, Datadog) for ML system performance
Experience collaborating with cross-functional teams in regulated industries (finance, insurance, health) with compliance and governance needs
Exceptional communication and leadership skills, with the ability to translate complex engineering challenges into clear business outcomes
Master's or PhD in Computer Science, Machine Learning, or related technical discipline
Preferred
Hands-on experience with Palantir platforms (Foundry, AIP, Ontology), including developing, deploying, and integrating ML solutions in enterprise ecosystems
Exposure to LLM and GenAI engineering (fine-tuning, vector search, distributed inference)
Experience optimizing GPU clusters, distributed training, or HPC environments
Familiarity with graph databases (e.g., Neo4j, TigerGraph) and their application in AI/ML systems
Benefits
Competitive compensation, benefits, future equity options, and leadership opportunities.
Full range of medical, financial, and/or other benefits.
Company
TWG Global
TWG Global is a unique holding company, strategically investing in and operating businesses across Investment Management, Securities, AI & Technology, Finance & Corporate Lending, Merchant Banking & Private Investments, and Sports, Media & Entertainment.
51-200 employees
Funding
Current Stage
Growth StageCompany data provided by crunchbase