Netradyne

Senior Staff Machine Learning Engineer

Bengaluru, Karnataka, India · On-site · 8+ yrs · Staff · Full-time

  • python
  • java
  • rust
  • c++
  • aws
  • kinesis
  • sqs
  • kubernetes
  • lambda
  • s3

Senior Staff ML Engineer to architect large-scale cloud ML systems, cloud platforms, and lead cross-functional teams at Netradyne.

Apply on Netradyne's site Ask for a referral

Posted 30 Sep 2026 · found on Netradyne's own careers page

Read the full job description

<div class="content-intro"><p style="text-align: justify;"><span style="color: rgb(22, 145, 121);"><strong>Netradyne</strong></span> 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.</p></div><p><span style="color:#000000"><span style="font-size:medium">Job Responsibilities:</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">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:</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Owning the architecture of large-scale cloud ML systems end to end — data ingestion and feature pipelines, training infrastructure, model serving, monitoring and retraining.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• 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.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Architecting distributed, fault-tolerant services and data platforms that operate reliably at high throughput, with clear SLAs, observability and cost controls.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Applying advanced statistical methods, machine learning and deep learning techniques to uncover trends, patterns, and anomalies in large-scale datasets.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Creating robust frameworks and tools to automate and enhance data mining, labeling, model training, and validation processes for internal ML/DL initiatives.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Setting engineering standards across teams — design review, testing strategy, CI/CD and release practice — and mentoring Staff and Senior engineers.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Collaborating closely with cross-functional teams to identify and implement data-driven solutions addressing key business challenges.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Conducting studies, setting up automation tools and frameworks, and regularly publishing internal and external KPI audits.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Develop and maintain ROI models and frameworks to quantify the business impact of data science initiatives.</span></span></p><br><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">Requirements:</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• B. Tech, M. Tech or PhD in Data Science, Computer Science, Electrical Engineering, Operations Research, Statistics, Mathematics or a related area.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• 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.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Demonstrated depth on both sides of the role: building distributed data and ETL pipelines, and training, tuning and deploying models in production.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Strong large-scale software engineering fundamentals: distributed systems, concurrency, microservice and API design, caching, queueing, idempotency and failure handling.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• 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.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Strong foundational knowledge in Statistics, Probability Theory, Machine Learning and Gen-AI.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Excellent programming skills – Python (required) and Java/Rust/C++ (desired), with strong fundamentals in object-oriented programming, algorithms, and data structures.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• 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.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Experience with transformer architectures and large language models (LLMs), and with Gen-AI tools and workflows.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Knowledge of best practices in software development, including version control, code review, automated testing, continuous integration and continuous delivery.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Experience with observability and production operations — metrics, tracing, logging, alerting and incident response for services and ML pipelines.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Proven ability to influence technical decisions beyond one's own team.</span></span></p><br><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">Desired skills:</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Agentic AI systems — tool use, planning, multi-agent orchestration, memory, guardrails and agent evaluation.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Hands-on experience with the Claude Agent SDK, OpenAI Agents SDK and Model Context Protocol (MCP) servers and connectors.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• AI-native development practice — working effectively with coding agents, GitHub Copilot, Claude Code or similar, and setting team conventions for their use.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Test-Driven Development (TDD) and Spec-Driven Development (SDD); designing specs and evals that agents and humans can both work against.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• LLMOps: prompt and context management, retrieval-augmented generation, model routing, caching, token cost optimisation and offline/online eval harnesses.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Infrastructure as code and container orchestration — Terraform, Kubernetes, Helm; multi-region and blue-green or canary deployment patterns.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Streaming and service technologies such as Kafka streams, Queues, Rest API and gRPC systems.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Tools: FastAPI, MLFlow, Huggingface pipelines, LangGraph, OpenAI, Anthropic API.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Experience with MLOps tools and practices for continuous deployment and monitoring of AI models.</span></span></p><p style="color:!important;font-size:medium !important"><span style="color:#000000"><span style="font-size:medium">• Experience with data visualization tools like Tableau, Grafana, Plotly-Dash.</span></span></p><div class="content-conclusion"><p style="text-align: justify;">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.</p> <p>If there is a match between your experiences/skills and the Company's needs, we will contact you directly.</p> <p>Netradyne is an equal-opportunity employer.</p> <p><strong>Applicants only - Recruiting agencies do not contact.</strong></p> <p><strong>Recruitment Fraud Alert!</strong></p> <p>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’.</p> <p>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 <a href="https://consumer.ftc.gov/articles/job-scams">Federal Trade Commission's job scams website</a>.</p> <p style="text-align: justify;">&nbsp;</p></div>

Members get roles like this as soon as we find them on the company's careers page, and paid plans email the ones that match their resume.

Get new roles first — free

More at Netradyne