Senior Staff Machine Learning Engineer

LocationOffice - Bengaluru, Bengaluru, Karnataka, India

Work modeon-site

Typefull-time

SeniorityLead

Experience8+ yrs

DepartmentAnalytics 1

Company size501–1,000 people

PostedSep 30, 2026 · 1w ago

Verified live47m ago

At a glanceSummarised by Seekless from the posting.

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

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)

Strong foundational knowledge in Statistics, Probability Theory, Machine Learning and Gen-AI

Excellent programming skills – Python (required)

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

Excellent programming skills – Java/Rust/C++ (desired)

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

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

Skills

Python

Java

Rust

C++

AWS

Kinesis

SQS

EKS

Lambda

Auto Scaling Groups

Terraform

Kubernetes

Helm

Kafka

FastAPI

MLFlow

Huggingface

LangGraph

OpenAI

Anthropic

Claude Agent SDK

OpenAI Agents SDK

Model Context Protocol (MCP)

GitHub Copilot

Tableau

Grafana

Plotly-Dash

About the company

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.

Responsibilities

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

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.

Nice to have

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.

Legal

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.

Hiring process

If there is a match between your experiences/skills and the Company’s needs, we will contact you directly.

Netradyne is an equal-opportunity employer.

Applicants only - Recruiting agencies do not contact.

Recruitment Fraud Alert!

There has been an increase in fraud that targets job seekers. Scammers may present themselves to job seekers as Netradyne employees or recruiters. Please be aware that Netradyne does not request sensitive personal data from applicants via text/instant message or any unsecured method; does not promise any advance payment for work equipment set-up and does not use recruitment or job-sourcing agencies that charge candidates an advance fee of any kind. Official communication about your application will only come from emails ending in ‘@netradyne.com’ or ‘@us-greenhouse-mail.io’.

Please review and apply to our available job openings at Netradyne.com/company/careers. For more information on avoiding and reporting scams, please visit the Federal Trade Commission’s job scams website.

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