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. • Excellent programming skills – Python (required) and Java/Rust/C++ (desired), with strong fundamentals in object-oriented programming, algorithms, and data structures. • Good understanding of database internals and schema design for relational (RDBMS) and non-relational (NoSQL) data stores, including the ability to write and reason about complex SQL. • Experience with transformer architectures and large language models (LLMs), and with Gen-AI tools and workflows. • Knowledge of best practices in software development, including version control, code review, automated testing, continuous integration and continuous delivery. • Experience with observability and production operations — metrics, tracing, logging, alerting and incident response for services and ML pipelines. • Proven ability to influence technical decisions beyond one's own team.   Desired skills: • Agentic AI systems — tool use, planning, multi-agent orchestration, memory, guardrails and agent evaluation. • Hands-on experience with the Claude Agent SDK, OpenAI Agents SDK and Model Context Protocol (MCP) servers and connectors. • AI-native development practice — working effectively with coding agents, GitHub Copilot, Claude Code or similar, and setting team conventions for their use. • Test-Driven Development (TDD) and Spec-Driven Development (SDD); designing specs and evals that agents and humans can both work against. • LLMOps: prompt and context management, retrieval-augmented generation, model routing, caching, token cost optimisation and offline/online eval harnesses. • Infrastructure as code and container orchestration — Terraform, Kubernetes, Helm; multi-region and blue-green or canary deployment patterns. • Streaming and service technologies such as Kafka streams, Queues, Rest API and gRPC systems. • Tools: FastAPI, MLFlow, Huggingface pipelines, LangGraph, OpenAI, Anthropic API. • Experience with MLOps