Senior Staff Machine Learning Engineer

Netradyne harnesses the power of Computer Vision and Edge Computing to revolutionize the modern-day transportation ecosystem. We are a leader in fleet safety solutions. With growth exceeding 4x year over year, our solution is quickly being recognized as a significant disruptive technology. Our team is growing, and we need forward-thinking, uncompromising, competitive team members to continue to facilitate our growth. Job Responsibilities: As a Senior Staff Machine Learning Engineer, you will set technical direction to our cross-functional team consisting of Data Scientists and Data/SW/ML Engineers. Your primary responsibilities will include: • Owning the architecture of large-scale cloud ML systems end to end — data ingestion and feature pipelines, training infrastructure, model serving, monitoring and retraining. • Design, develop and deploy production ready scalable cloud solutions that utilize Gen-AI, agentic AI, DNN, Traditional ML models, data-driven rules and ETL pipelines. • Architecting distributed, fault-tolerant services and data platforms that operate reliably at high throughput, with clear SLAs, observability and cost controls. • Applying advanced statistical methods, machine learning and deep learning techniques to uncover trends, patterns, and anomalies in large-scale datasets. • Creating robust frameworks and tools to automate and enhance data mining, labeling, model training, and validation processes for internal ML/DL initiatives. • Setting engineering standards across teams — design review, testing strategy, CI/CD and release practice — and mentoring Staff and Senior engineers. • Collaborating closely with cross-functional teams to identify and implement data-driven solutions addressing key business challenges. • Conducting studies, setting up automation tools and frameworks, and regularly publishing internal and external KPI audits. • Develop and maintain ROI models and frameworks to quantify the business impact of data science initiatives. Requirements: • B. Tech, M. Tech or PhD in Data Science, Computer Science, Electrical Engineering, Operations Research, Statistics, Mathematics or a related area. • At least 8 years of working experience in machine learning, data science or a related domain, including 5+ years building and shipping production ML systems at scale. • Demonstrated depth on both sides of the role: building distributed data and ETL pipelines, and training, tuning and deploying models in production. • Strong large-scale software engineering fundamentals: distributed systems, concurrency, microservice and API design, caching, queueing, idempotency and failure handling. • Proven experience designing and operating systems on public cloud at scale — AWS preferred (Kinesis, SQS, EKS, Lambda, Auto Scaling Groups, S3), including cost, capacity and reliability trade-offs. • Strong foundational knowledge in Statistics, Probability Theory, Machine Learning and Gen-AI.