{"id":1528394,"url":"https://alion.io/job/netradyne-senior-staff-machine-learning-engineer","title":"Senior Staff Machine Learning Engineer","company":{"id":48148,"name":"Netradyne","domain":"netradyne.com","url":"https://alion.io/company/netradyne","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":91,"open_postings":7,"ghost_share":0,"stale_share":0.143,"repost_share":0,"time_to_fill_p50_days":68,"computed_at":"2026-10-12T06:55:09Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":33000,"max_usd":82000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":10},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon Kinesis","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"C++","optional":false},{"name":"CI/CD","optional":false},{"name":"Computer Vision","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Java","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Rust","optional":false},{"name":"SQL","optional":false},{"name":"Anthropic","optional":true},{"name":"Apache Kafka","optional":true},{"name":"Blue-Green Deployment","optional":true},{"name":"Canary Release","optional":true},{"name":"Claude","optional":true},{"name":"Claude Code","optional":true},{"name":"Copilot","optional":true},{"name":"FastAPI","optional":true},{"name":"Function Calling","optional":true},{"name":"Grafana","optional":true},{"name":"gRPC","optional":true},{"name":"Helm","optional":true},{"name":"Hugging Face","optional":true},{"name":"Kubernetes","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LLM Evaluation","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"LLMOps","optional":true},{"name":"MLFlow","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"OpenAI","optional":true},{"name":"OpenAI Agents SDK","optional":true},{"name":"Plotly","optional":true},{"name":"RAG","optional":true},{"name":"Rest API","optional":true},{"name":"Tableau","optional":true},{"name":"Terraform","optional":true},{"name":"Tool Use","optional":true}],"status":"live","first_seen_at":"2026-09-30T11:42:48Z","employer_posted_date":"2026-09-30","last_verified_at":"2026-10-12T15:27:29Z","board_verified":true,"closed_at":null,"days_open":12,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":12},"description":"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.\nJob Responsibilities:\nAs 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:\nOwning the architecture of large-scale cloud ML systems end to end - data ingestion and feature pipelines, training infrastructure, model serving, monitoring and retraining.\nDesign, develop and deploy production ready scalable cloud solutions that utilize Gen-AI, agentic AI, DNN, Traditional ML models, data-driven rules and ETL pipelines.\nArchitecting distributed, fault-tolerant services and data platforms that operate reliably at high throughput, with clear SLAs, observability and cost controls.\nApplying advanced statistical methods, machine learning and deep learning techniques to uncover trends, patterns, and anomalies in large-scale datasets.\nCreating robust frameworks and tools to automate and enhance data mining, labeling, model training, and validation processes for internal ML/DL initiatives.\nSetting engineering standards across teams - design review, testing strategy, CI/CD and release practice - and mentoring Staff and Senior engineers.\nCollaborating closely with cross-functional teams to identify and implement data-driven solutions addressing key business challenges.\nConducting studies, setting up automation tools and frameworks, and regularly publishing internal and external KPI audits.\nDevelop and maintain ROI models and frameworks to quantify the business impact of data science initiatives.\nRequirements:\nB. Tech, M. Tech or PhD in Data Science, Computer Science, Electrical Engineering, Operations Research, Statistics, Mathematics or a related area.\nAt 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.\nDemonstrated depth on both sides of the role: building distributed data and ETL pipelines, and training, tuning and deploying models in production.\nStrong large-scale software engineering fundamentals: distributed systems, concurrency, microservice and API design, caching, queueing, idempotency and failure handling.\nProven 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.\nStrong foundational knowledge in Statistics, Probability Theory, Machine Learning and Gen-AI.\nExcellent programming skills - Python (required) and Java/Rust/C++ (desired), with strong fundamentals in object-oriented programming, algorithms, and data structures.\nGood 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.\nExperience with transformer architectures and large language models (LLMs), and with Gen-AI tools and workflows.\nKnowledge of best practices in software development, including version control, code review, automated testing, continuous integration and continuous delivery.\nExperience with observability and production operations - metrics, tracing, logging, alerting and incident response for services and ML pipelines.\nProven ability to influence technical decisions beyond one's own team.\nDesired skills:\nAgentic AI systems - tool use, planning, multi-agent orchestration, memory, guardrails and agent evaluation.\nHands-on experience with the Claude Agent SDK, OpenAI Agents SDK and Model Context Protocol (MCP) servers and connectors.\nAI-native development practice - working effectively with coding agents, GitHub Copilot, Claude Code or similar, and setting team conventions for their use.\nTest-Driven Development (TDD) and Spec-Driven Development (SDD); designing specs and evals that agents and humans can both work against.\nLLMOps: prompt and context management, retrieval-augmented generation, model routing, caching, token cost optimisation and offline/online eval harnesses.\nInfrastructure as code and container orchestration - Terraform, Kubernetes, Helm; multi-region and blue-green or canary deployment patterns.\nStreaming and service technologies such as Kafka streams, Queues, Rest API and gRPC systems.\nTools: FastAPI, MLFlow, Huggingface pipelines, LangGraph, OpenAI, Anthropic API.\nExperience with MLOps tools and practices for continuous deployment and monitoring of AI models.\nExperience with data visualization tools like Tableau, Grafana, Plotly-Dash.\nWe 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.\nIf there is a match between your experiences/skills and the Company's needs, we will contact you directly.\nNetradyne is an equal-opportunity employer.\nApplicants only - Recruiting agencies do not contact.\nRecruitment Fraud Alert!\nThere 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’.\nPlease review and apply to our available job openings at Netradyne.com/company/careers. 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