THE ROLE

We are seeking an experienced Hardware Test Engineer to join our Bangalore-based Manufacturing Test Team. Your primary focus will be to develop an end-to-end test solution for SSD products in a high volume manufacturing environment. This position will require deep technical expertise in test software development with Python, hands-on work with hardware, and close collaboration with cross-functional teams.

WHAT YOU'LL DO

• Test Software Development:
• Design, develop, and deploy software solutions for testing SSD storage hardware in a manufacturing setting.
• Improve existing test software for greater efficiency, scalability, and integration, ensuring alignment with hardware requirements.

• Hardware Debugging: 
• Basic understanding of the SSD storage hardware
• Ability to diagnose and resolve problems within the manufacturing test environment, including failure analysis and troubleshooting across different platforms.

• Cross-Functional Collaboration:
• Drive test requirements to cross-functional teams, ensuring alignment with overall product and manufacturing goals.
• Maintain close coordination with global teams to ensure feedback and improvements are incorporated into the test tools.
• Contract Manufacturer (CM) Communication and Proto Build Management:
• Serve as the main technical interface between the company and CMs, ensuring accurate communication of test requirements and process updates.
• Oversee the management of prototype builds, ensuring that test plans are effectively executed and any issues are promptly addressed.
• Continuous Improvement:
• Lead initiatives to enhance test procedures and tools, focusing on improved debugging, failure handling, and overall testing efficiency.

We are primarily an in-office environment and therefore, you will be expected to work from the Bangalore office in compliance with Pure’s policies, unless you are on PTO, or work travel, or other approved leave.

WHAT YOU BRING

• Bachelor’s degree in Electrical Engineering, Computer Engineering, or a related field.
• 8+ years of experience in software development for hardware manufacturing test tools.
• Software Development Expertise: Strong experience in developing and enhancing test software, with proficiency in Python, or similar object-oriented programming languages.
• Program management: Plan and execute hardware test programs, manage risks, coordinate cross-functional teams, optimize resources, track issues, ensure compliance, and communicate results effectively.
• Test Plan Development: Ability to create detailed, robust test plans from hardware schematics and design documentation, ensuring comprehensive hardware validation.
• Problem-Solving Skills: Strong analytical and problem-solving skills, dedicated to improving test processes and software tools.
• Communication Skills: Excellent interpersonal and communication skills, with experience working in cross-functional and geographically distributed teams.
• Experience in high-volume manufacturing environments is a plus.
• Prior experience working with a hyperscaler company would be an advantage.
• Lean Six Sigma certification is preferred.

#LI-ONSITE

THE ROLE

We are seeking an experienced Hardware Test Engineer to join our Bangalore-based Manufacturing Test Team. Your primary focus will be to develop an end-to-end test solution for SSD products in a high volume manufacturing environment. This position will require deep technical expertise in test software development with Python, hands-on work with hardware, and close collaboration with cross-functional teams.

WHAT YOU'LL DO

• Test Software Development:
• Design, develop, and deploy software solutions for testing SSD storage hardware in a manufacturing setting.
• Improve existing test software for greater efficiency, scalability, and integration, ensuring alignment with hardware requirements.

• Hardware Debugging: 
• Basic understanding of the SSD storage hardware
• Ability to diagnose and resolve problems within the manufacturing test environment, including failure analysis and troubleshooting across different platforms.

• Cross-Functional Collaboration:
• Drive test requirements to cross-functional teams, ensuring alignment with overall product and manufacturing goals.
• Maintain close coordination with global teams to ensure feedback and improvements are incorporated into the test tools.
• Contract Manufacturer (CM) Communication and Proto Build Management:
• Serve as the main technical interface between the company and CMs, ensuring accurate communication of test requirements and process updates.
• Oversee the management of prototype builds, ensuring that test plans are effectively executed and any issues are promptly addressed.
• Continuous Improvement:
• Lead initiatives to enhance test procedures and tools, focusing on improved debugging, failure handling, and overall testing efficiency.

We are primarily an in-office environment and therefore, you will be expected to work from the Bangalore office in compliance with Pure’s policies, unless you are on PTO, or work travel, or other approved leave.

WHAT YOU BRING

• Bachelor’s degree in Electrical Engineering, Computer Engineering, or a related field.
• 5+ years of experience in software development for hardware manufacturing test tools.
• Software Development Expertise: Strong experience in developing and enhancing test software, with proficiency in Python, or similar object-oriented programming languages.
• Program management: Plan and execute hardware test programs, manage risks, coordinate cross-functional teams, optimize resources, track issues, ensure compliance, and communicate results effectively.
• Test Plan Development: Ability to create detailed, robust test plans from hardware schematics and design documentation, ensuring comprehensive hardware validation.
• Problem-Solving Skills: Strong analytical and problem-solving skills, dedicated to improving test processes and software tools.
• Communication Skills: Excellent interpersonal and communication skills, with experience working in cross-functional and geographically distributed teams.
• Experience in high-volume manufacturing environments is a plus.
• Prior experience working with a hyperscaler company would be an advantage.
• Lean Six Sigma certification is preferred.

#LI-ONSITE

What is the InMobi family like?   

We are an infectious bunch. Be it the way we rise to challenges, the innovative products we create, the dreams we chase or the fun we have at work. We are sure that if you meet us, you will be infected too. Today, we are proud to be the leaders in Mobile advertising and are on an accelerated path of being a leader in enterprise software for marketers. We invite you to free yourself, dream big and chase your passion. We are here today because a few of us did just that.   

What can we promise?   

We offer you an opportunity to work on building enterprise platforms that require you to acquire, hone and demonstrate your ML and AI skills in designing and solving for complex problems, thereby creating software that creates value for our customers in a reliable, scalable, secure, and AI-first manner. At InMobi, you get to work on innovative technologies, work closely with business teams and grow as an overall technology leader.  

Modern work environment, flexible schedule, and smart, creative, down-to-earth people. Internal opportunities to move roles and try out bridge assignments with different teams.  Food for your soul – free meals all days of the week, gym, or yoga class to flex those biceps, cocktails at drink cart Thursdays and fun at work on Funky Fridays. We even promise to let you bring your kids and pets to work.  

What is the team like?  

At InMobi Enterprise team, we are transforming the way finance, sales, marketing, people, and legal functions operate. We’re driving transformative work across Sales, Marketing, People, and Finance with ML and Agentic AI—and we’re looking for passionate innovators to join us!

What you will be doing

We are looking for a Data Scientist who can operate at the intersection of classical machine learning, GenAI and modern agentic AI systems with a strong engineering appetite. You will build and deploy intelligent AI/ML systems that will help transform the business process across the core. This role blends deep ML craftsmanship with forward-looking innovation in autonomous/agentic systems.

Your responsibilities will include:

• Lead the experimentation, design and build of agentic AI systems that autonomously observe model performance, trigger experiments, tune hyperparameters, improve ranking policies, or orchestrate ML workflows with minimal human intervention.
• Apply LLMs, embeddings, retrieval-augmented architectures, and multimodal generative models for semantic understanding, content classification, and user preference modeling.
• Design intelligent agents that can automate repetitive decision-making tasks—e.g., candidate generation tuning, feature selection, or context-aware content curation.
• Support POCs/ Pilots projects to explore and validate innovative technologies and solutions in ML/AI space.

• Build and operate machine learning models on diverse, high-volume data sources for forecasting, classification and prediction.
• Develop rapid experimentation workflows to validate hypotheses and measure real-world business impact.
• Design pipelines for data preparation, model training, evaluation, and deployment in collaboration with engineering counterparts.
• Define best practices for monitoring ML model performance using statistical techniques; identifying drifts, failure modes, and improvement opportunities.
• Contribute to ML/AI thought leadership—blogs, case studies, internal tech talks, and industry conferences.
• Thrive in a multi-functional, highly collaborative team environment with engineering, product, business, and creative teams.
• Interface with stakeholders across Product, Business, Data, and Infrastructure to align ML initiatives with strategic priorities.
• Document designs, development processes, and best practices to promote knowledge sharing and operational efficiency.

What do we expect from you?

We are seeking candidates with deep expertise in ML and hands-on experience building GenAI and Agentic AI systems.

Skillset: AI/ML Engineering, Python, LLM, RAG, Agentic AI, MCP, REST API

You should have experience with:

• Design, build, and deploy building LLM-powered applications, chatbots, and multi-agent systems to improve efficiency, accuracy, and decision-making.
• Implement and customize GenAI and multi-agent systems using AI frameworks such as LangChain, LangGraph, CrewAI, or similar.
• Create, refine, and maintain prompt templates, chains, and workflows.
• Integrate vector databases (MilvusDB, FAISS, Pinecone, Chroma, etc.) for semantic search and memory.
• Classical ML and deep learning techniques across NLP, clustering, and time series.
• Proficient in deploying ML workflows/models in production system. Continuous monitoring, evaluation, and retraining of models to ensure sustained performance. Ensuring model explainability, accuracy, compliance.
• Big data processing (Spark, distributed data systems) and cloud computing.
• Designing end-to-end ML solutions—from prototype to production.
• Stay updated with emerging AI/ML technologies and recommend relevant innovations for the finance domain.
• Drive in design reviews, sprint planning, and technical discussions.
• Collaborating effectively with diverse stakeholders, including technical teams and business leaders.
• Adapting to fast-paced environments and evolving priorities with high energy and autonomy.

We value curiosity, problem-solving ability, and a strong bias toward experimentation and production impact.

 Qualifications

• Bachelor’s/Master’s in Computer Science, Statistics, Mathematics, Electrical Engineering, Operations Research, Economics, Analytics, or related fields. PhD is a plus.
• 8+ years of industry experience in ML/Data Science, with deep proficiency in Python and one or more frameworks: PyTorch, TensorFlow, Scikit-learn.
• Solid understanding of ML fundamentals, regression, tree-based models, clustering, and time series.
• Hands-on experience with LLMs, retrieval systems, generative models, or agentic/autonomous ML systems is highly desirable.
• Experience building Agentic frameworks such as LangGraph, AutoGen, CrewAI, n8n or ReACT-style agents.
• Expertise with algorithms in NLP, Time Series, and Deep Learning, applied on real-world datasets.
• Strong experience with the big data ecosystem (Spark, Hadoop) and cloud platforms (Azure, AWS, GCP/Vertex AI).
• Experience with Model Context Protocols (MCPs) — building or integrating MCP tools, servers, or capabilities.
• Comfortable working in cross-functional teams.
• Strong problem-solving and analytical skills with the ability to work in agile environments.
• Good understanding of data pipelines, APIs, and cloud platforms (Azure/GCP/AWS).
• Excellent communication skills with the ability to simplify complex technical concepts.

Reports To

NOC Delivery Manager

What You Will Be Doing

• NOC Support Engineers will provide technical expertise in resolving and fixing the problems arising in the ChargePoint's array of Charging stations.
• You have to define and diagnose resolutions for the issues and drive resolution of charging stations faults, and systematically reduce critical outages and improve overall uptime of the Charging Stations.
• You will be analyzing, diagnosing, isolating and resolving problems and documenting your analysis in the case notes.
• You will also work collaboratively with other services teams.
• You learn and understand the various technological aspects of different models of the ChargePoint Charging stations and will handle the situation and drive towards faster resolution.
• You will work in a ITIL service lifecycle Framework and ensure MTTR and MTBF are achieved.
• You will Create technical documentation and make improvements to processes and procedures.
• Perform proactive preventative maintenance for both hardware and software.
• You will work in a Shift that spans 24 hours in various shift timings and you will work from the Office all 5 days of the week.

What You Will Bring to ChargePoint

• Tenacious commitment to customer focused support
• Comfort with making complex decisions and excited by "thinking outside the box"
• Must have exceptional troubleshooting and advanced analytical thinking skills for complex issue resolution between hardware/software components
• Ability to accurately convey complex concepts in easily digestible communications both verbally and in writing
• Proven experience in driving initiatives, cross-functional collaborations, and holding others accountable to expectations
• Account management skills including investigating and understanding current and historical issues for specific accounts
• Maintains effectiveness when experiencing major changes in personal work tasks or work environment; adjusts effectively to work within new work structures, processes, requirements, or cultures
• Ability to work effectively and complete tasks/projects with minimal supervision
• Proactively identify areas of product improvement including design enhancements, failure analysis, and quality control
• Focuses and guides self in accomplishing work objectives
• Deals effectively with others in antagonistic situations, using appropriate interpersonal styles and methods to reduce tension or conflict
• Actively appreciates and includes the diverse capabilities, insights, and ideas of others and working effectively and respectfully with individuals of diverse backgrounds, styles, abilities, and motivation
• Sets high standards of performance for self; assuming responsibility and accountability for successfully completing assignments or tasks; self-imposing standards of excellence rather than having standards imposed

Requirements

• Minimum of 6 years in a technical support environment for complex hardware and software solutions
• Working experience, certifications, or bachelor’s degree in technical field
• The ideal candidate should be from any of the following experience - Network engineer - Systems engineer – Linux, Unix or Windows engineer - Backup or Storage engineer - Electrical or Electronics engineer - Technical Support engineer who has ability to understand and diagnose the technical aspect in any technological domains.
• The ideal candidate should have previous exposure and working knowledge into CLI interface of more or more flavors of Linux (ex: RedHat, Ubuntu etc). The candidate can also have working experience in Unix.
• The ideal candidate is well versed in Industry standards practices and solutions.
• The candidate should have good exposure and understanding of the Operational processes of Incident Management, Change Management and Problem Management.
• Understand the ITIL or any other operations Service lifecycle frameworks or Project management or quality frameworks.
• Desirable to have candidates with well versed knowledge in Microsoft Word, Excel and Powerpoint.
• Its desirable if the candidate has knowledge on Power distribution and/or power electronics fundamentals, familiarity with AC/DC EV charging protocols: CCS, CHAdeMO, J1772 etc.
• The candidate should be an excellent communicator with skills in Documentation and creating knowledge articles.
• Desirable to have worked in CRM tools like Salesforce, Servicenow, Jira etc.
•  

Location

Bangalore, India

Title: Lead Firmware QA Engineer, Astera Labs, Bengaluru, India. 
 
Astera Labs Inc., a leader in purpose-built connectivity solutions for data-centric systems, is seeking Lead Software QA Engineer for their Bengaluru (India) Design Center. Partnering with leading processor and GPU vendors, cloud service providers, world-class manufacturing companies, Astera Labs is helping data-centric system designers remove performance bottlenecks in compute-intensive workloads such as Artificial Intelligence and Machine Learning.

Basic Qualifications:

• Bachelor’s degree in electrical engineering (EE) or Computer Science is required; a master’s or PhD in EE is preferred with minimum 4 years of experience.

Required Experience:

• Experienced and detail oriented PCIe switch test engineer with solid understanding of PCIe protocol.
• Estimate work, identify dependencies and develop schedules.
• Responsible for designing and executing functional, performance, interoperability and stress tests.
• Work closely with  Silicon team, architecture team, FW development team to understand the design and develop test strategies.
• Responsible for manual and automation testing.
• Strong knowledge of scripting ( Python ) is must.
• Responsible for automation development and manual testing.
• Perform Signal integrity and protocol level validation.
• Closely work with development teams to triage and debug issues.
• Knowledge of collecting PCIe trace using PCIe analyzer and analyzing the trace to triage issues.
• Experience in PRBS testing, loopback and margining tests.
• Good understanding of OOB testing and Protocols like MCTP, I2C, SPDM etc.
• Experience is PCIe compliance testing is a plus.
• Regularly working with Hyperscale's and Tier 1 OEMs to communicate plans and status, address escalations, deliver on SLAs.
• Knowledge in validation of NIC controllers and storage controllers is preferred.
• Requirements management and traceability through software development phases.
• Mentoring and coaching team members to help them excel in their jobs.
• Power user of Git, Jira and Confluence.

What you will do

• Architect & Scale: Design and maintain high-throughput detection systems and backend services, ensuring low latency and high reliability.
• Data Orchestration: Lead the development of robust data pipelines using Spark and Airflow to supply detection teams with high-fidelity data for ML model training and evaluation.
• Technical Leadership: Partner with Engineering Managers to define technical roadmaps, mentor junior/mid-level engineers, and drive best practices in code quality and system design.
• AI-Driven Innovation: Lead the team in adopting AI productivity initiatives, leveraging tools like Claude, OpenAI, and GitHub Copilot to accelerate the development lifecycle and automate internal workflows.
• Cross-functional Collaboration: Work closely with TPMs, Product Managers, Data Scientists, and Security Researchers to translate complex business needs into scalable technical solutions.
• Operational Excellence: Manage and optimize cloud-native infrastructure on AWS/EKS, ensuring systems are cost-effective, secure, and performant.

Must Haves

• 8+ years of professional experience: Proven track record in production-level backend development with Python or Golang.
• Data Engineering Expertise: Expert-level proficiency in building and optimizing distributed data pipelines using Spark and Airflow (or equivalent orchestration tools).
• Cloud & Infrastructure: Extensive experience managing and scaling cloud-native applications on AWS (highly preferred), GCP, or Azure, with a proven track record of hands-on container orchestration using EKS.
• Leadership & Mentorship: Experience leading technical projects, mentoring engineers, and contributing to the long-term technical strategy of a team.
• System Design: Strong ability to design complex integrations and handle significant throughput/latency challenges.
• Problem Solving: A methodical approach to performance debugging, benchmarking, and resolving bottlenecks in large-scale systems.
• Database Proficiency: Strong experience with Postgres or similar relational databases at scale.
• Education: BS in Computer Science, Applied Sciences, or a related engineering field.

Nice to Haves

• Frontend Literacy: Familiarity with React and TypeScript to assist with internal tool visualizations.
• Data Ecosystems: Experience with Databricks, Snowflake, or similar data lakehouse architectures.
• Cybersecurity Domain: Background in threat detection, network security, or fraud prevention.
• Advanced Degrees: MS in Computer Science or Electrical Engineering.

#LI-AB2

Title: Staff Site Reliability Engineer, Product Area Focus
Location: Noida / Bangalore (Hybrid)

Summary of role

Own availability, the most important product feature, by continually striving for sustained operational excellence of Sumo’s planet-scale observability and security products.  Work alongside your global SRE team, executing on projects in your product-area specific reliability roadmap, to optimize operations, increase efficiency in our use of cloud resources and our developer’s time, harden security posture, and increase feature velocity of our developers  Work closely with multiple teams to optimize the operations of their microservices - and improve the lives of the engineers within your product area engineering team.

Responsibilities

• Support the engineering teams within your product area by maintaining and executing a reliability roadmap of opportunities for improvement for reliability, maintainability, security, efficiency, and velocity - and help for realizing those opportunities.
• Collaborate with development infrastructure, Global SRE, and your product area engineering teams to establish and continually refine your reliability roadmap.
• Participate in defining, evolving, and managing SLOs for several teams within your product area.
• Participate in on-call rotations within your product area to understand operations workload so you can continually work to improve the on-call experience and reduce operational workload for running microservices and related components.
• Complete projects to optimize and tune on-call experience for your engineering teams.
• Continually improve the lifecycle of microservices and architectural components from inception and design, through deployment, operation, and refinement.
• Write code and automation to reduce operational workload, increase efficiency, improve security posture, eliminate toil, and enable Sumo’s developers to deliver features more rapidly.
• Work closely with the developer infrastructure teams to expedite development infrastructure adoption of tools to advance your reliability roadmap by identifying needs for your supported engineering teams, and contributing back features and bug fixes when needed. 
• Scale systems sustainably through mechanisms like automation, and evolve systems by pushing for changes that improve reliability and velocity.
• Facilitate blame-free root cause analysis meetings for incidents to learn and drive improvement
• Participate in and continually improve our global IRC (incident response coordination) for all products.
• Drive root cause identification and issue resolution with the teams.
• Work inside of a fast-paced iterative environment.
• Hire & Mentor new team members.

Required Qualifications and Skills

• Cloud native application development experience leveraging best practices and design patterns
• Strong debugging and trouble-shooting skills across the entire technology stack
• Deep understanding of AWS Networking, Compute, Storage, and managed services.
• Competency with modern CI/CD tooling like Kubernetes, Terraform, Ansible & Jenkins
• Experience with full life cycle support of services, from creation to production support
• Versed in Infrastructure as Code practices using technologies like Terraform or Cloud Formation
• Ability to author production ready code in at least one the following: Java, Scala or Go.
• Experience with Linux systems and at home on the command line
• Understand and apply modern approaches to cloud-native software security
• Experienced with agile frameworks, such as Scrum and Kanban, and how to operate within these frameworks to continually deliver value.
• Flexible and willing to step into new roles and responsibilities
• Willingness to learn and use Sumo Logic products for solving reliability and security issues
• Bachelor’s or Master's Degree in Computer Science, Electrical Engineering, or another scientific or technical discipline
• 8+ years of professional experience in applied software security role

Desirable Skills

• Experience using Sumo Logic products or other observability products for reliability and security
• Experienced with planet scale product development
• Running and operating SaaS products on AWS Cloud with expert level proficiency
• Experience with streaming technologies like Kafka, Kafka Streams, or KSQL
• Expert level experience in one or more of: Java, Go, Scala, or Python
• Expert level experience in one or more of: Terraform, Jenkins, Kubernetes
• Extensive experience running and tuning JVM workloads at scale

About Us

Sumo Logic, Inc. helps make the digital world secure, fast, and reliable by unifying critical security and operational data through its Intelligent Operations Platform. Built to address the increasing complexity of modern cybersecurity and cloud operations challenges, we empower digital teams to move from reaction to readiness—combining agentic AI-powered SIEM and log analytics into a single platform to detect, investigate, and resolve modern challenges. Customers around the world rely on Sumo Logic for trusted insights to protect against security threats, ensure reliability, and gain powerful insights into their digital environments. For more information, visit www.sumologic.com.

Sumo Logic Privacy Policy. Employees will be responsible for complying with applicable federal privacy laws and regulations, as well as organizational policies related to data protection.

About Netradyne:

Netradyne provides AI-powered technologies for fleet management and safer roads. An award-winning industry leader in fleet safety and video telematics solutions, Netradyne empowers thousands of commercial fleet customers across North America, Europe, and Asia to enhance their driver performance, reduce risk, and optimize operations.

Netradyne sets the standard among transportation technology companies for enhancing and sustaining road safety, with an industry-leading 25+ billion miles vision-analyzed for risk and an industry-first driver scoring system that reinforces safe behaviors. Founded in 2015, Netradyne is headquartered in San Diego with offices in San Francisco, Nashville, the UK and Bangalore. For more details visit: www.netradyne.com

Job Overview:

As a Staff Data Engineer – ML at Netradyne, an award-winning industry leader in fleet safety and video telematics solutions, you will take senior technical ownership of the machine learning and data platforms that underpin our AI-powered fleet analytics. This role is responsible for designing, building, and scaling production-grade ML pipelines, generative AI capabilities, and real-time data streaming systems across cloud and edge environments, delivering actionable insights that make fleet operations safer and more efficient. You will collaborate closely with machine learning engineers, data scientists, and product teams to integrate advanced AI technologies into Netradyne's platform, including on-device edge intelligence and natural-language-driven capabilities. Your work will directly shape the data infrastructure behind Netradyne's Physical AI vision, where AI continuously interprets multimodal vehicle data to understand drivers, vehicles, and road environments as an integrated system.

Key Responsibilities:

• You will be embedded within a team of machine learning engineers and data scientists, responsible for building and productising generative AI, deep learning, and data engineering solutions. You will:
• Design, develop and deploy production-ready, scalable solutions that utilise generative AI, traditional ML models, data science workflows, and ETL/ELT pipelines on AWS cloud infrastructure (and hybrid edge-cloud environments).
• Build and manage real-time and batch data streaming pipelines to handle high-volume, high-velocity data from fleet devices, leveraging technologies such as Apache Kafka and Amazon Kinesis for low-latency processing and near real-time insights.
• Collaborate with cross-functional teams (Product, Data Science, ML Engineering, Operations) to integrate AI-driven solutions into business operations and customer-facing products.
• Design and implement generative AI solutions using large language models (LLMs), including prompt engineering, fine-tuning, and retrieval-augmented generation (RAG) patterns, to enable intelligent analytics, natural-language interfaces, and context-aware insights across fleet and safety data.
• Build and scale agentic AI systems by developing autonomous and semi-autonomous agents that orchestrate data retrieval, reasoning, and actions across ML pipelines and services, ensuring these agentic workflows are production-grade, observable, and aligned with safety, reliability, and governance requirements.
• Implement MLOps best practices including CI/CD pipelines for model training, automated testing, model versioning, continuous monitoring, and modern workflow orchestration (e.g. Apache Airflow, MLFlow) to achieve reliable, reproducible model deployments at scale.
• Champion data quality and governance by implementing data contracts, rigorous validation, and monitoring to ensure that analytical pipelines meet defined SLAs and that downstream models and business users can trust the data.
• Conduct research and stay current with the latest advancements in generative AI, vector search techniques, LLM frameworks, orchestration platforms, and related technologies.

Mandatory Skills:

• Experience with data visualisation and BI tools (e.g. Tableau, Grafana, Plotly Dash).
• Exposure to real-time data streaming and messaging systems (e.g. Apache Kafka, Amazon Kinesis, Amazon SQS).
• Knowledge of containerisation and orchestration (e.g. Docker, Kubernetes/EKS)
• Strong understanding of generative AI system design, including prompt engineering, RAG architectures, vector search, and evaluation techniques for LLM-based applications in production environments.
• Experience with agentic AI patterns, such as designing, orchestrating, and monitoring autonomous or semi-autonomous agents that coordinate tools, data sources, and ML services with appropriate safety, observability, and governance controls.
• Experience with modern workflow orchestration platforms such as Apache Airflow, Prefect, or Dagster for scheduling and managing complex ETL/ML pipelines. These tools are now standard for data pipeline engineering.
• Proficiency with at least one Python web framework (e.g. FastAPI, Django, Flask) for developing data or model-serving APIs.
• Experience with rapid prototyping tools (e.g. Streamlit, Gradio, Dash) for demonstrating ML solutions to stakeholders.
• Familiarity with modern ML frameworks and libraries such as PyTorch, TensorFlow, and Hugging Face Transformers
• Background in large-scale data processing and lakehouse or data warehousing technologies (e.g. Snowflake, Amazon Redshift, Apache Spark/EMR, Delta Lake).

Qualifications and Education Requirements:

• B.Tech, M.Tech or PhD in Computer Science, Data Science, Electrical Engineering, Statistics, Mathematics, Operations Research, or a related field (or equivalent professional experience).
• 5–8 years of industry experience in data engineering, machine learning engineering, or related roles, with a track record of building and scaling data/ML solutions in production.
• Strong programming skills in Python and SQL, with solid fundamentals in algorithms, data structures, and object-oriented programming.
• Experience building end-to-end solutions on AWS cloud infrastructure including developing data pipelines (ETL/ELT), deploying ML models at scale, and utilising cloud services (e.g. S3, Lambda, EKS).
• In-depth understanding of data storage systems: proficient with relational databases (SQL), NoSQL datastores, and modern vector databases, including schema design and query optimisation for large-scale datasets.
• Hands-on experience with generative AI tools and workflows, and practical work with large language models (LLMs), including building LLM-powered applications and utilising RAG techniques to incorporate external knowledge.
• Working knowledge of MLOps principles and tools — e.g. model lifecycle management, CI/CD pipelines for ML, automated data/feature pipelines, and monitoring/alerting for deployed models.
• Strong analytical and problem-solving skills with keen attention to detail.
• Solid foundation in statistics, probability, and estimation theory.
  
  

Equal Opportunity Employer Statement:
Netradyne is an equal opportunity employer and are committed to creating an inclusive environment for all employees.

EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge’s robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today’s best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems.

• End-to-End Ownership: Drive RTL-to-GDSII implementation, including floorplanning, placement, CTS, routing, and physical verification (LVS/DRC/ERC).
• Performance Optimization: Execute high-performance design closure focusing on PPA (Power, Performance, Area) targets in advanced process nodes ( 3nm or below)
• Static Timing Analysis (STA): Perform comprehensive timing closure, including signal integrity, crosstalk analysis, and CPPR (Common Path Pessimism Removal) optimization.
• Power Integrity: Conduct IR-drop analysis (Static/Dynamic) and EM (Electromigration) checks to ensure robust power delivery.
• Innovation: Develop and integrate AI/ML-based automation scripts to optimize the physical design flow and reduce turnaround time.

• Education: B.Tech/M.Tech in Electrical/Electronics Engineering from a Top Tier University.
• Experience: 2–4 years of hands-on experience in Physical Design within the semiconductor industry.
• Tool Expertise: Expert-level proficiency in industry-standard tools (Preferred Innovus).
• Technical Depth: Strong understanding of multi-corner multi-mode (MCMM) closure, low-power design techniques (UPF/CPF), and physical verification.
• Mindset: A proven "go-getter" who is diligent, detail-oriented, and capable of taking complete ownership of blocks under tight deadlines.
• The "Plus": Proficiency in Python/Tcl and experience using AI/ML models for EDA tool automation or predictive PPA analysis.
• Location: Bangalore

Job Overview:

Netradyne is looking for talented engineers to join our Analytics team comprised of graduates from IITs, IISC, Stanford, UIUC, UCSD etc. As a Senior Staff Research Engineer at Netradyne, you will lead the development of cutting edge AI solutions to enable drivers and fleets realize unsafe driving scenarios in real-time to prevent accidents from happening and reduce fatalities/injuries. You will collaborate with cross-functional teams to build and deploy scalable and reliable AI solutions in a fast-paced environment.

Key Responsibilities:​

You will help design, implement and commercialize driver monitoring and driver assistance algorithms. You will have access to very large datasets and will get to deploy new algorithms/models into thousands of vehicles. As a machine learning research engineer, you may 

• Design and commercialize algorithms characterizing driving behavior. 
• Design, implement and track key metrics; and architect data-driven solutions. 
• Improve machine learning infrastructure for scalable training and inference. 
• Innovate and develop proof of concept solutions showcasing novel capabilities. 

Mandatory Skills:​

• B Tech, M. Tech or PhD in computer science, electrical engineering or a related area. 
• Excellent programming skills – Python (required) and C++ (desired). 
• 7+ yrs with Research or industry experience within computer vision and machine learning models. 
• Ability to take abstract product concepts and turn them into reality. 
• Ability to innovate on cutting-edge problems without documented solutions.
• Hands-on experience in handling edge deployments targeting various HW platforms.

 Preferred Skills:​

• Experience with image, video and time-series data
• Experience with Camera calibration, multi-view geometry, 3D reconstruction, SLAM
• Experience with SOTA multi-camera BEV and Occupancy prediction algorithms.
• Experience with road scene understanding (objects, lanes, intersections, signs etc.) 
• Experience with successfully applying machine learning to solve a real-world problem.  
• Experience with conducting successful statistical experiments.
• Experience with radar perception or radar-camera fusion.

About Netradyne:

Netradyne provides AI-powered technologies for fleet management and safer roads. An award-winning industry leader in fleet safety and video telematics solutions, Netradyne empowers thousands of commercial fleet customers across North America, Europe, and Asia to enhance their driver performance, reduce risk, and optimize operations.

Netradyne sets the standard among transportation technology companies for enhancing and sustaining road safety, with an industry-leading 25+ billion miles vision-analyzed for risk and an industry-first driver scoring system that reinforces safe behaviors. Founded in 2015, Netradyne is headquartered in San Diego with offices in San Francisco, Nashville, the UK and Bangalore.

For more details visit: www.netradyne.com

Job Overview:

Netradyne is looking for talented engineers to join our Analytics team comprised of graduates from IITs, IISC, Stanford, UIUC, UCSD etc. As a Staff Research Engineer at Netradyne, you will lead the development of cutting edge AI solutions to enable drivers and fleets realize unsafe driving scenarios in real-time to prevent accidents from happening and reduce fatalities/injuries. You will collaborate with cross-functional teams to build and deploy scalable and reliable AI solutions in a fast-paced environment.

Key Responsibilities:​

You will help design, implement and commercialize driver monitoring and driver assistance algorithms. You will have access to very large datasets and will get to deploy new algorithms/models into thousands of vehicles. As a machine learning research engineer, you may 

• Design and commercialize algorithms characterizing driving behavior. 
• Design, implement and track key metrics; and architect data-driven solutions. 
• Improve machine learning infrastructure for scalable training and inference. 
• Innovate and develop proof of concept solutions showcasing novel capabilities. 

Mandatory Skills:​

• B Tech, M. Tech or PhD in computer science, electrical engineering or a related area. 
• In-depth understanding of Machine learning (Deep learning & Classic ML) and computer vision concepts
• Excellent programming skills – Python (required) and C++ (desired). 
• 6.5+ yrs with Research or industry experience within computer vision and machine learning models. 
• Ability to take abstract product concepts and turn them into reality. 
• Ability to innovate on cutting-edge problems without documented solutions.
• Hands-on experience in handling edge deployments targeting various HW platforms.

Preferred Skills:​

• Experience with time-series data
• Experience with Camera calibration, multi-view geometry, 3D reconstruction, SLAM.
• Experience with road scene understanding (objects, lanes, intersections, signs etc.) 
• Experience with successfully applying machine learning to solve a real-world problem.  
• Experience with conducting successful statistical experiments. 

About Netradyne:

Netradyne provides AI-powered technologies for fleet management and safer roads. An award-winning industry leader in fleet safety and video telematics solutions, Netradyne empowers thousands of commercial fleet customers across North America, Europe, and Asia to enhance their driver performance, reduce risk, and optimize operations.

Netradyne sets the standard among transportation technology companies for enhancing and sustaining road safety, with an industry-leading 25+ billion miles vision-analyzed for risk and an industry-first driver scoring system that reinforces safe behaviors. Founded in 2015, Netradyne is headquartered in San Diego with offices in San Francisco, Nashville, the UK and Bangalore.

For more details visit: www.netradyne.com

Job Overview:

Netradyne is looking for talented engineers to join our Analytics team comprised of graduates from IITs, IISC, Stanford, UIUC, UCSD etc. As a Senior Staff Research Engineer at Netradyne, you will lead the development of cutting edge AI solutions to enable drivers and fleets realize unsafe driving scenarios in real-time to prevent accidents from happening and reduce fatalities/injuries. You will collaborate with cross-functional teams to build and deploy scalable and reliable AI solutions in a fast-paced environment.

Key Responsibilities:​

You will help design, implement and commercialize driver monitoring and driver assistance algorithms. You will have access to very large datasets and will get to deploy new algorithms/models into thousands of vehicles. As a machine learning research engineer, you may 

• Design and commercialize algorithms characterizing driving behavior. 
• Design, implement and track key metrics; and architect data-driven solutions. 
• Improve machine learning infrastructure for scalable training and inference. 
• Innovate and develop proof of concept solutions showcasing novel capabilities. 

Mandatory Skills:​

• B Tech, M. Tech or PhD in computer science, electrical engineering or a related area. 
• In-depth understanding of Machine learning (Deep learning & Classic ML) and computer vision concepts
• Excellent programming skills – Python (required) and C++ (desired). 
• 9+ yrs with Research or industry experience within computer vision and machine learning models. 
• Ability to take abstract product concepts and turn them into reality. 
• Ability to innovate on cutting-edge problems without documented solutions.
• Hands-on experience in handling edge deployments targeting various HW platforms.

Preferred Skills:​

• Experience with time-series data
• Experience with Camera calibration, multi-view geometry, 3D reconstruction, SLAM.
• Experience with road scene understanding (objects, lanes, intersections, signs etc.) 
• Experience with successfully applying machine learning to solve a real-world problem.  
• Experience with conducting successful statistical experiments. 

EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge’s robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today’s best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems.

• Timing Convergence Leadership: Drive full-chip SOC timing closure across all corners and modes (func, shift, capture, etc.).
• Cross-Functional Coordination: Partner with Architecture to validate timing feasibility, RTL to optimize logic structures, and DFT to streamline test-mode timing.
• Left-Shift Strategy: Proactively identify and resolve structural timing issues, congestion, and high-latency paths early in the RTL/Synthesis phase to ensure predictable physical design execution.
• Advanced Analysis: Perform complex timing tasks including CPPR/CRPR analysis, NoC (Network-on-Chip) timing, and constraint validation (SDC).
• Automation & Agility: Architect and build sophisticated automation utilities (Tcl, Python, Perl) to accelerate timing sign-off, reduce manual audits, and improve schedule agility.
• PPA Optimization: Drive best-in-class PPA by guiding physical design teams on floorplanning, clock tree synthesis (CTS) strategies, and placement optimizations.

• Experience: 9 to 14 years of hands-on experience in STA and Timing Closure on advanced process nodes (7nm, 5nm, or below).
• Critical Thinking: Proven ability to debug complex timing violations, identify root causes (e.g., data-to-clock skew, crosstalk), and propose architectural or physical solutions.
• Technical Mastery: * Expertise in industry-standard sign-off tools (e.g., PrimeTime, Tempus). Preferable Tempus tool.
• Deep understanding of Multi-Mode Multi-Corner (MMMC) challenges and Hierarchical vs. Flat timing closure.
• Proficiency in SDC/CDC constraints.
• Soft Skills: Strong leadership presence with the ability to influence cross-functional teams and drive execution in a fast-paced environment.
• Education: B.Tech/M.Tech in Electrical/Electronics Engineering or a related field.

What you will be doing

We are looking for a Data Scientist who can operate at the intersection of classical machine learning, large-scale recommendation systems, and modern agentic AI systems.

You will design, build, and deploy intelligent systems that power Glance’s personalized lock screen and live entertainment experiences. This role blends deep ML craftsmanship with forward-looking innovation in autonomous/agentic systems.

Your responsibilities will include:

Classical ML & Recommendation Systems

• Design and develop large-scale recommendation systems using advanced ML, statistical modeling, ranking algorithms, and deep learning.
• Build and operate machine learning models on diverse, high-volume data sources for personalization, prediction, and content understanding.
• Develop rapid experimentation workflows to validate hypotheses and measure real-world business impact.
• Own data preparation, model training, evaluation, and deployment pipelines in collaboration with engineering counterparts.
• Monitor ML model performance using statistical techniques; identify drifts, failure modes, and improvement opportunities.

Agentic Systems & Next-Gen AI

• Build and experiment with agentic AI systems that autonomously observe model performance, trigger experiments, tune hyperparameters, improve ranking policies, or orchestrate ML workflows with minimal human intervention.
• Apply LLMs, embeddings, retrieval-augmented architectures, and multimodal generative models for semantic understanding, content classification, and user preference modeling.
• Design intelligent agents that can automate repetitive decision-making tasks—e.g., candidate generation tuning, feature selection, or context-aware content curation.
• Explore reinforcement learning, contextual bandits, and self-improving systems to power next-generation personalization.

Cross-functional impact

• Collaborate with Designers, UX Researchers, Product Managers, and Software Engineers to integrate ML and GenAI-driven features into Glance’s consumer experiences.
• Contribute to Glance’s ML/AI thought leadership—blogs, case studies, internal tech talks, and industry conferences.
• Thrive in a multi-functional, highly collaborative team environment with engineering, product, business, and creative teams.
• Plus: Interface with stakeholders across Product, Business, Data, and Infrastructure to align ML initiatives with strategic priorities.

The experience we need

We are seeking candidates with deep expertise in ML, recommendation systems, and a strong appetite for building agentic AI systems.

You should have experience with:

• Large-scale ML and recommendation systems (collaborative filtering, ranking models, content-based approaches, embeddings).
• Classical ML and deep learning techniques across NLP, sequence modeling, RL, clustering, and time series.
• Experience in deploying ML workflows/models in production system
• Big data processing (Spark, distributed data systems) and cloud computing.
• Designing end-to-end ML solutions—from prototype to production.
• Plus: Building or experimenting with LLMs, generative models, and agentic AI workflows (e.g., autonomous evaluators, self-improving pipelines, automated experiment agents).

We value curiosity, problem-solving ability, and a strong bias toward experimentation and production impact.

Qualifications

• Bachelor’s/Master’s in Computer Science, Statistics, Mathematics, Electrical Engineering, Operations Research, Economics, Analytics, or related fields. PhD is a plus.
• 6+ years of industry experience in ML/Data Science, ideally in large-scale recommendation systems or personalization.
• Experience with LLMs, retrieval systems, generative models, or agentic/autonomous ML systems is highly desirable.
• Expertise with algorithms in NLP, Reinforcement Learning, Time Series, and Deep Learning, applied on real-world datasets.
• Proficient in Python and comfortable with statistical tools (R, NumPy, SciPy, PyTorch/TensorFlow, etc.).
• Strong experience with the big data ecosystem (Spark, Hadoop) and cloud platforms (Azure, AWS, GCP/Vertex AI).
• Comfortable working in cross-functional teams.
• Familiarity with privacy-preserving ML and identity-less ecosystems (especially on iOS and Android).
• Excellent communication skills with the ability to simplify complex technical concepts.

Glance - An InMobi Group Company    

Founded in 2019, Glance is a consumer technology company that operates disruptive digital platforms including Glance, Roposo, and Nostra. Glance’s ’smart lock screen’ inspires consumers to make the most of every moment by surfacing relevant experiences without the need for searching and downloading apps. Glance Lock Screen is currently available on over 450 million smartphones worldwide. Roposo is a LIVE platform that is revolutionizing live experiences, through a unique, immersive, creator-led approach. Nostra is the largest gaming platform in India and Southeast Asia, offering gamers engaging ways to discover, play, watch, learn and compete.   
 
Headquartered in Singapore, Glance is an unconsolidated subsidiary of InMobi Group and is funded by Jio Platforms, Google, and Mithril Capital. For more information visit glance.com, nostra.gg, and roposo.com

What should you know about joining Glance?  

At Glance, we walk the talk – free yourself, dream big, and chase your passion! On joining, you’ll have opportunities to make an immediate impact on mission-critical projects, as you work with highly capable and ambitious peer groups.   

Be rewarded for your autonomy even as you collaborate. Ideate, innovate, and inspire by leveraging bleeding-edge tech to disrupt consumer experiences.   

While you work, we’ll take care of nourishing your body, mind, and soul. This includes daily meals, gym, trainings, tech tools, and regular unwind sessions. Also, feel free to bring your kids – even the furry ones – to the office!

What You’ll Do

As a Data Scientist – Recommendation Systems, you will own and drive high-impact ML systems powering personalization across Glance products.

• Design, build, and deploy large-scale recommendation and personalization models using diverse, high-volume data sources.
• Lead rapid experimentation—from hypothesis formulation to offline evaluation and online A/B testing—balancing speed, rigor, and business impact.
• Develop and productionize end-to-end ML pipelines, including data preparation, feature engineering, model training, evaluation, and monitoring.
• Partner closely with Product, Engineering, Design, UX, and Business teams to translate product goals into scalable ML solutions.
• Monitor model health and performance, applying statistical techniques to ensure robustness and long-term effectiveness.
• Explore and prototype new ML techniques to improve relevance, engagement, and monetization.
• Act as a technical interface for stakeholders, clearly articulating trade-offs, results, and next steps.
• Contribute to Glance’s thought leadership through blogs, case studies, and industry conference talks.

What We’re Looking For

Core Expectations

• Deep expertise in Machine Learning, Data Science, and Recommendation Systems at scale.
• Strong applied understanding of experimentation, metrics, and causal reasoning in real-world systems.
• Ability to take models from idea → prototype → production → business impact.

Experience & Skills

• 10+ years of industry experience in ML/Data Science building large-scale recommendation or personalization systems.
• Hands-on experience applying techniques from ML, Deep Learning, NLP, Reinforcement Learning, Time Series, and Statistics.
• Strong programming skills in Python with production-quality code practices.
• Experience with big data ecosystems, especially Apache Spark.
• Familiarity with cloud platforms such as AWS, GCP (Vertex AI), or Azure.
• Experience operating in identity-constrained / privacy-aware environments (e.g., iOS/Android, identity-less systems) is a plus.
• Excellent communication skills—able to explain complex technical ideas clearly to non-technical stakeholders.
• High curiosity, strong problem-solving ability, and a bias toward learning and experimentation.

Qualifications

• Bachelor’s or Master’s degree in a quantitative field such as Computer Science, Electrical Engineering, Statistics, Mathematics, Operations Research, Economics, Analytics, or Data Science.
• PhD is a plus, but not mandatory.
• We value diverse academic backgrounds—great data scientists come from many disciplines.

EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge’s robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today’s best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems.

Staff/Senior Staff Physical Design Floorplan & PDN Lead

Experience: 10–14 Years

Location:  Bangalore/2 days to work

Department: RTL-to-GDSII / Physical Implementation

Role Overview

We are seeking a high-caliber Staff or Senior Staff Physical Design Engineer with a specialized mastery of Floorplanning and Power Delivery Network (PDN) design. You won’t just be pushing buttons; you will be the architectural bridge between RTL/Systems and the final GDSII. This role requires a visionary who understands how a single floorplan decision ripples through the entire PPA (Power, Performance, Area) spectrum.

As a technical lead, you will own the chip-top floorplan for complex, large-scale SoCs or Sub-chips, driving strategies that balance aggressive performance targets with robust power integrity.

Key Responsibilities

• Architectural Floorplanning: Lead chip-level floorplanning, including macro placement, pin assignment, and partition definition for multi-million gate designs.
• Strategic PDN Design: Architect and implement complex Power Delivery Networks. You will define the metal stack usage and grid density to support high-performance cores while minimizing routing congestion.
• EMIR Mastery: Perform exhaustive IR-drop (Static/Dynamic) and Electromigration (EM) analysis. You must be able to diagnose root causes and propose architectural or physical fixes that don't compromise timing.
• Cross-Functional PPA Trade-offs: Collaborate directly with RTL and Architecture teams. You will use "Design Thinking" to influence the micro-architecture, suggesting changes to bus widths, pipeline stages, or memory configurations to optimize physical outcomes. 
• Methodology Development: Establish best practices and automated flows for floorplanning and PDN synthesis to be used by the wider implementation team.

Technical Requirements

• Core Experience: 10+ years in Physical Design with a proven track record of multiple successful tape-outs at advanced nodes (7nm, 5nm, or 3nm).
• PDN Expertise:Ability to model complex power-up sequences and multi-voltage domains.
• Floorplanning Savvy: Deep proficiency in hierarchical physical design and top-level integration using industry-standard tools (Innovus or IC Compiler II).
• Analysis Skills: Strong understanding of thermal-aware PDN and the physical implications of high-current density paths.
• Scripting: Advanced proficiency in Tcl, Python, or Perl to automate complex floorplanning tasks and data analysis.

Behavioral Attributes

• Critical Thinker: You don't just follow a recipe. You question constraints and look for "hidden" PPA gains.
• Design Thinking: Ability to empathize with the challenges of upstream (RTL) and downstream (Sign-off) teams to create a holistic solution.
• Influence: You can articulate technical trade-offs to stakeholders, 

Education

• B.Tech/M.Tech in Electrical/Electronic Engineering or equivalent.

EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge’s robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today’s best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems.

We are looking for a visionary Principal Engineer to spearhead Encharge AI Timing leadership role.

This isn't just an execution role; we need a technical trailblazer who can navigate the complexities of advanced process nodes (2nm/3nm and beyond) while fostering a culture of innovation and rigorous critical thinking.

As the India Timing Lead, you will bridge the gap between architectural intent and silicon reality, ensuring our high-performance designs meet the most stringent timing, power, and reliability targets.

Key Responsibilities

• Technical Leadership: Drive the STA strategy for complex SoC and IP designs
• Innovation & Methodology: Develop and deploy "out-of-the-box" solutions for timing closure. This includes defining sign-off margins, variation modeling (POCV/LVF), and managing multi-corner multi-mode (MCMM) scenarios.
• Execution Excellence: Lead the team through full-cycle STA, from initial constraints validation to final GDS sign-off.
• Cadence Tempus Expert: Act as the primary subject matter expert for Cadence Tempus, optimizing tool flows for distributed processing, ECO generation, and power-aware timing analysis.
• Cross-Functional Collaboration: Partner with RTL, Synthesis, and Physical Design teams to influence floorplanning and clock tree synthesis (CTS) strategies for timing-friendly layouts.
• Mentorship: Elevate the technical caliber of the India team through active code reviews, white-boarding sessions, and fostering a "first-principles" approach to problem-solving.

Required Skills & Expertise

Mindset & Qualifications

• Critical Thinker: You don't just follow a checklist; you challenge existing flows to find efficiencies and hidden risks.
• Leadership Presence: 14+ years of experience in VLSI, with a clear track record of leading high-impact teams in India.
• Education: B.Tech/M.Tech in Electrical/Electronic Engineering or a related field.
• Communication: Ability to distill complex technical hurdles into actionable insights for global stakeholders.

Role Overview

You will be part of a team responsible for bringing a software stack to production for next-generation AI compute platforms. As a member of the Software QA team, you will design and build automated test frameworks for AI hardware and software components, and continuously enhance the test and qualification infrastructure to support evolving products.

This role involves leveraging CI/CD pipelines and Python-based test frameworks to drive both component-level and end-to-end validation. You will develop automation to support large language model accuracy, benchmarking, and robustness testing, ensuring software and hardware stacks meet high quality and performance standards.

You will collaborate closely with machine learning kernel teams, compiler and runtime engineers, hardware architects, and ML researchers across global teams throughout the qualification lifecycle. There will also be opportunities to contribute to emerging ML and AI technologies as part of the broader engineering effort.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field, with 7+ years of industry experience.

• Solid understanding of machine learning fundamentals and large language models.

• Strong proficiency in Python, including data structures, and experience working in a Linux environment.

• Self-driven team player with strong collaboration skills.

Preferred Experience

• Experience working in startups or small, fast-paced teams.

• Hands-on exposure to machine learning models and neural network layers.

• Experience testing deep learning frameworks such as TensorFlow, PyTorch, and transformer-based models.

• Familiarity with CI/CD systems, particularly GitLab-based pipelines.