Machine Learning Engineer

• Build and contribute across Etsy’s recommendations stack, including application interfaces, back-end technologies, and machine learning technologies
• Develop with Python, MySQL, Scala, Vertica, PySpark, Google Cloud Platform, and Airflow
• Work cross-functionally with machine learning infrastructure, data science, search experience, and other engineering teams
• Participate in design, prioritization, and implementation
• Deploy machine learning models to production, including application development for features when needed
• Provide detailed and constructive design and code reviews
• Improve ranking and suggestions of localized results through machine learning algorithms
• Create workflows and pipelines that transform data into appropriate buyer results
• Collaborate with engineers, machine learning scientists, product managers, data scientists, designers, and product-facing engineers and managers

Senior Machine Learning Engineer, Recommendations Retrieval

• Lead the future of fraud machine learning at Ramp
• Build core machine learning models
• Design data architectures and set strategic roadmaps to mitigate fraud-related threats while minimizing friction for legitimate users
• Partner with product and engineering counterparts on model design, implementation, execution, and analysis
• Apply statistical and machine learning techniques to large datasets to discover fraud, platform abuse, and identity-theft patterns
• Prototype and productionize machine learning models and rules-based fraud protection systems
• Partner with Fraud Engineering and Data Platform teams to augment and leverage first- and third-party data sources
• Influence machine learning team processes, tools, and systems to enable scalable decision-making

• Own the architecture and implementation of production-grade ML systems and workflows.
• Transition models from development and research into scalable production services.
• Design and build reliable training, inference, evaluation, and deployment pipelines.
• Integrate ML models into APIs, backend services, applications, and core product workflows.
• Optimize ML systems for latency, throughput, scalability, reliability, and cost-efficiency.
• Establish engineering standards for model versioning, testing, observability, and deployment.
• Collaborate closely with domain experts, data scientists, and cross-functional teams.
• Diagnose and resolve technical bottlenecks across the ML application stack.

Senior ML Engineer

• Build and operate infrastructure for training and serving Etsy's machine learning models
• Architect systems for fast, reliable, and scalable model serving at millions of inferences per second
• Write high-quality, production-grade Python code and participate in code reviews and pair programming
• Help drive architectural decisions and contribute to system design discussions
• Build, operate, and improve ML training and serving infrastructure on GCP and Kubernetes
• Design and maintain serving infrastructure for open-source and internally hosted LLMs
• Manage model deployment and resources and tune performance
• Partner with Applied Scientists to translate model training and serving needs into self-service infrastructure
• Optimize tradeoffs across performance, cost, and reliability for infrastructure serving 100M+ users
• Use generative AI and other productivity tools thoughtfully

Senior Software Engineer, ML Enablement

• Drive the architectural vision and system design for core AI/ML-based services
• Act as technical lead for complex, multi-quarter initiatives from inception to global deployment
• Design and build large-scale deep learning infrastructure and platforms for distributed model training
• Ensure low-latency and high-availability at enterprise scale
• Partner with Product Management, Core Engineering, and executive stakeholders to align ML capabilities with business strategy
• Translate abstract product requirements into concrete technical designs
• Mentor and guide senior and mid-level data scientists and engineers
• Establish standard methodologies and define code quality expectations
• Lead architecture design reviews
• Work with accelerators, network fabrics, CUDA, NCCL, RDMA, Databricks, Spark, Kubernetes, Kafka, and Prometheus
• Design, develop, and support robust CI/CD pipelines for AI/ML services
• Ensure models transition smoothly from research into reliable production endpoints

Staff Machine Learning Engineer

• Lead research projects across marketplace dynamics, including simulation, auction and allocation mechanics, long-term objective modeling, exploration and information value, and marketplace experimentation methods
• Take ideas from hypothesis to production through literature review, prototyping, offline validation, shadow testing, and online experiments shipped through partner teams in Discovery and the Seller organization
• Build learned simulators predicting segment-level effects of ranking and policy changes
• Build surrogate models of long-term marketplace outcomes
• Model auction and bidding dynamics and discovery exposure allocation as a portfolio problem, including allocation to rising sellers
• Advance marketplace evaluation through off-policy evaluation, switchback and interference-robust experiment designs, and variance reduction
• Contribute to external technical presence through publications, open-source work, and public benchmarks
• Collaborate with cross-functional teams and influence roadmaps

Machine Learning Engineer, Applied Research

• Build machine learning systems powering sponsored search, including retrieval, ranking, ad allocation, and user engagement modeling on Etsy.com and the Etsy app
• Work cross-functionally with product engineering teams
• Communicate ideas and model capabilities to product managers
• Apply advances in machine learning to improve relevance and buyer ad engagement
• Prototype, optimize, and productionize large-scale machine learning models
• Conduct A/B experiments to validate ML models and pipelines
• Collaborate with product managers, ML engineers, and analytics partners to measure and improve the ads product
• Report to the Engineering Manager

Senior Machine Learning Engineer, Search Ads

• Write high-quality, production-grade Python code
• Participate in code reviews and pair programming
• Contribute to and help drive architectural decisions
• Build, operate, and improve infrastructure for training and serving machine learning models on Google Cloud Platform and Kubernetes
• Design and maintain serving infrastructure for open-source and internally hosted LLMs
• Manage model deployment and resources, and tune performance
• Apply machine learning fundamentals to infrastructure and design decisions
• Partner with Applied Scientists to understand model training and serving needs
• Translate model requirements into infrastructure that reduces workflow friction
• Contribute to system design discussions, balancing performance, cost, and reliability
• Support infrastructure serving more than 100 million users
• Use generative AI and productivity tools thoughtfully

Senior Software Engineer, Machine Learning

• Lead end-to-end development and deployment of advanced AI/ML solutions for model validation and operational domains
• Design and implement production-grade machine learning systems using statistical modeling, deep learning, Generative AI, NLP, graph analytics, and Agentic AI frameworks
• Develop LLM-powered applications, AI copilots, agentic workflows, RAG solutions, multi-agent orchestration frameworks, intelligent decision support systems, and human-in-the-loop AI solutions
• Develop scalable data science and AI pipelines using Python, Databricks, Azure, PySpark, MLflow, vector databases, orchestration frameworks, and modern AI tooling
• Partner with business leaders, fraud strategy, engineering, MLOps, governance, and enterprise AI teams
• Translate ambiguous business problems into analytical frameworks, technical solutions, and actionable insights
• Lead technical architecture discussions and contribute to AI platform strategy, solution design, and enterprise AI standards
• Communicate analytical concepts and AI solution designs to executives, stakeholders, and governance partners
• Ensure model governance, explainability, monitoring, and responsible AI practices throughout the AI/ML lifecycle
• Mentor and guide junior scientists
• Monitor emerging industry trends, academic research, and AI technologies
• Deliver insights, lead iterative learning cycles, and create analytic solutions that become core deliverables
• Design and deliver enterprise analytic solutions and develop business insights from broad data sources
• Conduct research, analysis, presentations, risk assessments, remediation planning, and continuous process improvements
• Manage relationships across business lines and control functions while maintaining regulatory alignment
• Participate as a team member, share knowledge, support team development, and act as a bank brand ambassador

Machine Learning Engineer jobs in New York

Senior Machine Learning Engineer, Recommendations Retrieval