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 ones 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 tools and practices for continuous deployment and monitoring of AI models.

Experience with data visualization tools like Tableau Grafana Plotly-Dash.

We are committed to an inclusive and diverse team. Netradyne is an equal-opportunity employer. We do not discriminate based on race color ethnicity ancestry national origin religion sex gender gender identity gender expression sexual orientation age disability veteran status genetic information marital status or any legally protected status.

If there is a match between your experiences/skills and the Companys needs we will contact you directly.

Netradyne is an equal-opportunity employer.

Applicants only - Recruiting agencies do not contact.

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