Lead Machine Learning Engineer Resume

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Lead Machine Learning Engineer Resume Luka Berg Lead Machine Learning Engineer (212) 555-0184 luka.berg@example.com linkedin.com/example/lukaberg New York, NY 10011 STRENGTHS Platform leadership Set direction for content ML systems, then stayed close to delivery. Teams gained clearer decisions and stronger ownership. Practical mentoring Design reviews became coaching moments. Engineers left with sharper tradeoffs, useful feedback, and confidence during production work. Production judgment When model freshness slipped, monitoring and recovery paths clarified priorities. Stakeholders received faster answers and steadier service. Cross-team translation Connected editorial goals with engineering choices during ranking work. Partners gained shared language for impact, risk, and timing. Systems thinking Traced failures from event streams through serving endpoints. That habit exposed quality gaps before they reached audience products. SKILLS Machine Learning Engineering Deep Learning Statistical Modeling Recommendation Systems Object Detection Automated Tagging RAGs Feature Engineering Data Quality Model Evaluation Batch Training Online Serving Python Java REST LANGUAGES English Native 40 German Intermediate 20 MY CAREER 15 Years Lead Machine Learning Engineer at Brightline Digital Networks (2.7 Years) Principal Machine Learning Engineer at HarborPoint Streaming Labs (3.4 Years) Senior Machine Learning Engineer at CivicWave Media Technologies (3.4 Years) Machine Learning Engineer at SignalForge Analytics (3.3 Years) Software Engineer at Redwood Software Group (2.2 Years) SUMMARY Lead Machine Learning Engineer with 15 years of software engineering experience and 13 years delivering production machine learning systems for high-volume content and audience platforms. Brings hands-on depth across data collection , data exploration, feature engineering , batch training, deep learning, statistical modeling, and low-latency online serving . Builds scalable AWS infrastructure, Spark and Databricks pipelines, REST microservices, and observable distributed systems that support recommendation, object detection, automated tagging, and retrieval experiences. Known for connecting product, editorial, data, and engineering goals with practical technical decisions. Eager to help Northstar Media Systems create useful personalized experiences while mentoring engineers, reducing delivery risk, and strengthening production ownership. EXPERIENCE Lead Machine Learning Engineer Brightline Digital Networks January 2024 - Present New York, NY Leads technical direction for a content intelligence platform spanning recommendation, automated tagging, retrieval, data pipelines, online inference, observability, and production operations. Guides engineers and partners with product, editorial, and data science leaders on initiatives that connect audience needs with scalable machine learning delivery. Set platform direction for recommendation, automated tagging, and retrieval across web, mobile, and connected devices. Rebuilt inference workflows with AWS Step Functions , Lambda, Glue, SQS, SNS, and Personalize for reliable recovery. Standardized Spark and Databricks feature libraries , aligning training data with low-latency REST serving. Guided design reviews, risk assessments, testing plans, and production readiness across product and editorial teams. Expanded Datadog metrics, logs, dashboards, and alerts for model freshness, latency, drift, and pipeline failures. Introduced CI/CD , model validation, rollback, incident response , and documentation practices across Scrum teams. Principal Machine Learning Engineer HarborPoint Streaming Labs July 2020 - December 2023 Jersey City, NJ Owned architecture for personalized content-ranking services supporting large streaming and news applications. Combined event-driven data platforms, Spark and Databricks machine learning pipelines, deep learning workflows, REST services, and team coaching to improve discovery and operational confidence. Owned architecture for personalized ranking services supporting millions of daily recommendation decisions across applications. Designed REST microservices with Kafka and Kinesis signals, preserving scalable boundaries for candidate generation. Led Spark and Databricks pipelines for feature preparation, batch training , evaluation, and model promotion. Delivered deep learning tagging workflows with editorial queues, improving classification and content discovery. Translated audience goals into experimentation plans with product managers, editors, platform engineers, and analysts. Mentored six engineers while standardizing code review, integration testing, CI/CD, and post-incident learning. Senior Machine Learning Engineer CivicWave Media Technologies January 2017 - June 2020 Boston, MA Developed production personalization services for regional publishers, connecting behavioral data, catalog metadata, and contextual signals with audience-facing products. Advanced data exploration , feature engineering, object detection, semantic tagging, quality controls, and Agile delivery while coaching junior engineers. Developed recommendation services combining behavioral data, metadata, and contextual signals for publisher experiences. Built Spark and Databricks workflows for exploration, feature engineering , training sets, and model evaluation. Implemented Python REST endpoints and cloud queues for low-latency ranked content retrieval. Prototyped object detection and semantic tagging for image and video libraries with editorial review. Added Kafka ingestion checks and data-quality controls before downstream model training workflows. Coached junior engineers on experiments, testing, service ownership, Scrum delivery, and technical documentation . Machine Learning Engineer SignalForge Analytics August 2013 - December 2016 Chicago, IL Built supervised learning products from audience behavior and content metadata, supporting recommendation, churn-risk, and targeted-distribution use cases. Established repeatable modeling, batch scoring, REST integration, monitoring, and stakeholder communication practices that prepared experiments for production. Converted audience behavior and content metadata into supervised datasets for recommendation and churn-risk models. Validated statistical and deep learning features through holdout testing and controlled product experiments. Created Spark batch scoring workflows with AWS services, improving repeatability for model releases. Developed REST APIs exposing ranked results with documented contracts, failures, and performance expectations. Established monitoring for data freshness, prediction volumes, and serving errors across production pipelines. Presented evaluation tradeoffs to product, analytics, and engineering partners for practical release decisions. Software Engineer Redwood Software Group May 2011 - July 2013 Austin, TX Built Java and Python services for distributed content and customer-data platforms, gaining foundational experience with REST APIs, relational data, asynchronous processing, testing, cloud deployment, and production support. Contributed within Scrum teams while progressing toward machine learning platform engineering. Built Java and Python services for distributed content platforms with REST APIs and asynchronous processing. Implemented ingestion and transformation components for analytics and downstream reporting workflows. Added unit and integration tests as new data sources and API consumers entered production. Configured queues, scheduled jobs, logs, and deployment scripts under senior engineering guidance. Investigated production defects through log analysis and reproducible test cases without disrupting customer services. Contributed to Scrum planning, peer reviews, design documentation, and technical demonstrations. PROJECTS Audience Personalization Platform 2023 A ranking service became a daily product touchpoint. Built event-driven feature pipelines, model evaluation, and REST inference for timely content recommendations across streaming experiences. Multimodal Content Tagging 2020 Editors needed faster ways to classify growing media libraries. Combined object detection, deep learning inference, and review workflows for searchable image and video collections. Retrieval-Augmented Content Discovery 2025 Search results needed richer context. Prototyped RAG workflows that joined content metadata, semantic signals, and low-latency services for audience-focused discovery. LEADERSHIP & AWARDS Engineering Excellence Award, HarborPoint Streaming Labs, 2022 Technical Innovation Award, CivicWave Media Technologies, 2019 Machine Learning Mentorship Lead, Brightline Digital Networks EDUCATION Bachelor's Degree in Computer Science The University of Texas at Austin GPA: 3.8 2011 Austin, TX Coursework: Machine Learning, Data Structures, Algorithms, Statistical Methods, Distributed Systems CERTIFICATIONS AWS Certified Machine Learning - Specialty 2023 Databricks Certified Machine Learning Associate 2022 TECHNICAL SKILLS AWS Services: Step Functions, Lambda, Glue, SQS, SNS, Personalize Big Data Platforms: Spark, Databricks, Kafka, Kinesis Machine Learning: Deep learning, statistical modeling, recommendation systems ML Operations: Batch training, model evaluation, model promotion Data Engineering: Data collection, data exploration, feature engineering Backend Development: Python, Java, REST microservices Serving Systems: Low-latency serving, online inference, ranking services Observability Tools: Datadog, metrics, logging, monitoring Delivery Practices: CI/CD, automated testing, model validation Reliability Practices: Incident response, rollback procedures, recovery paths Workflow Systems: Workflow orchestration, data pipelines, feature libraries Development Methods: Agile, Scrum, design reviews SKILLS Machine Learning Engineering Deep Learning Statistical Modeling Recommendation Systems Object Detection Automated Tagging RAGs Feature Engineering Data Quality Model Evaluation Batch Training Online Serving Python Java REST PROFESSIONAL AFFILIATIONS Volunteer Workshop Mentor, New York Data Science Community Member, Association for Computing Machinery Member, Institute of Electrical and Electronics Engineers LANGUAGES English (Native) German (Intermediate) ADDITIONAL INFORMATION Work Status : Authorized to work in United States. No sponsorship required. ADDITIONAL INFORMATION Work Status : Authorized to work in United States. No sponsorship required. REFERENCES AVAILABLE ON REQUEST

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