Software Engineer Machine Learning Salary India in United States

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 122,000 colleagues serve people in more than 160 countries. JOB DESCRIPTION: Position Overview The AI Platform Engineer builds and operates the machine learning and generative AI platform used by teams across Abbott Cancer Diagnostics. You'll own the full model lifecycle in production — data and feature pipelines, training and experimentation, evaluation and promotion, serving, and monitoring — along with the platform services, compute and tooling underneath it. This is hands-on infrastructure work backed by solid platform engineering practice: making inference fast and cheap, making the path from experiment to production repeatable and auditable, and shipping interfaces other engineers can build on — in support of software that ultimately reaches patients. Essential Duties Include, but are not limited to, the following: Build and maintain data, feature, and training pipelines for ML and LLM workloads — ingestion, transformation, fine-tuning, distributed training, and reproducible experiment execution with lineage tracked from dataset and code to resulting model. Implement automated evaluation and promotion gates — performance benchmarks, regression checks, and validation criteria that determine whether a model advances toward production. Automate the model lifecycle end to end through CI/CD and GitOps: packaging, promotion across environments, progressive rollout, and rollback. Build and operate production model-serving infrastructure for LLMs and predictive models, including inference optimization, autoscaling,

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE ROLE As an OS / K8s Systems Engineer at Baseten, you’ll build the automation and systems that turn raw GPU hardware into production-ready compute. From provisioning to orchestration, you’ll own the software layer that makes our infrastructure reproducible, scalable, and reliable across data centers. This is a senior, hands-on role focused on building systems not operating them. You’ll work close to the metal designing OS images, building provisioning pipelines, and automating cluster bring-up from scratch. Your work will define how quickly we can turn new capacity into usable compute. EXAMPLE INITIATIVES Zero-to-cluster automation Build workflows that take new hardware from unprovisioned to fully operational cluster. Provisioning systems Design PXE-based or equivalent systems for imaging and lifecycle management. Reproducible infrastructure — Ensure clusters deploy consistently across data centers. RESPONSIBILITIES Own the end-to-end automation of cluster bring-up and lifecycle management. Build and maintain OS images, provisioning systems, and configuration pipelines. Deploy and operate cluster orchestration platforms (Kubernetes, Slurm, or similar). Design systems for reproducibility across sites and hardware generations. Automate upgrades, rollouts, and failure recovery. Optimize system performance, including GPU utilization and networking. Partner with hardware and network teams to validate and improve system b

At ClickUp, we're building the future of work: the first truly converged AI workspace unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context-driven AI. We are an AI-native company. Every team member is expected to leverage AI daily, and we evaluate AI fluency as part of our hiring process. Join us and help redefine what's possible. 🚀 Job Summary We are looking for a GTM DevOps Engineer to join our Business Systems team and own the reliability, automation, and delivery infrastructure behind our Go-To-Market (GTM) technology stack. This role sits at the intersection of platform reliability and CI/CD engineering, ensuring that our critical business systems — including Salesforce, NetSuite, MuleSoft, Workato, and an expanding portfolio of AI-powered workloads — are deployed consistently, operate resiliently, and scale with the business. You will partner closely with Business Systems developers, architects, and business stakeholders to build and maintain the pipelines, monitoring frameworks, and operational standards that keep our GTM systems healthy and our release cycles fast and predictable. As our team builds and deploys AI agents across GCP Cloud Run and AWS Bedrock AgentCore, you will serve as the infrastructure and deployment owner for these workloads — bringing engineering discipline to an environment where AI-generated code is increasingly entering production. This is a hands-on engineering role for someone who thrives in complexity, takes ownership of platform uptime, and brings a software engineering mindset to business application operations — directly supporting GTMSOE's broader mission of operational excellence across the GTM org. Key Responsibilities CI/CD & Release Engineering Design, build, and maintain CI/CD pipelines for Salesforce (SFDX/Salesforce CLI), NetSuite (SuiteScript/SuiteBundler), MuleSoft (Anypoint Platform), and Workato; establish branching strategies, environment promotion standards, and release gatin

Become a part of our caring community Humana is seeking a self-driven and collaborative Lead Engineer to join our Interactive Voice Response (IVR) team. In this role, you will deliver innovative IVR solutions and develop robust omnichannel APIs for our enterprise platforms. You will have the opportunity to drive the success of a high-impact, customer-facing application within a Fortune 50 company, working closely with multiple teams throughout the software development lifecycle (SDLC). Lead Engineer –Omnichannel Humana is seeking a self-driven and collaborative Lead Engineer to join our Omnichannel team. In this role, you will design, develop, secure, and enhance enterprise APIs that support high-impact, member facing, applications across Humana's digital and voice channels. This role offers the opportunity to modernize and strengthen existing API capabilities while helping deliver resilient, scalable, and secure omnichannel solutions within a Fortune 50 organization. Key Responsibilities Design, develop, and maintain scalable Omnichannel APIs that support enterprise applications and customer-facing capabilities. Enhance the security, resiliency, performance, and reliability of existing APIs through modernization, improved architecture, observability, testing, and operational controls. Apply AI and AI-assisted engineering practices to accelerate development, improve quality, automate testing, enhance documentation, and identify opportunities for optimization. Partner with architecture, security, cloud, product, engineering, and operations teams to deliver secure, resilient, and enterprise-aligned API solutions. Collaborate with agile teams to plan, track, and deliver API enhancements, platform improvements, and cloud-based capabilities. Develop proofs of

SUMMARY STATEMENT We are looking for a Solution Architect to design the technical solutions behind our client engagements and give delivery teams a clear, workable path from concept to production. You will work across enterprise data, software applications and GenAI - translating complex business problems into practical architectures that delivery teams can build and scale. This could include architecting an agentic workflow for clinical operations, a conversational analytics product grounded in enterprise data, or an AI-enabled decision platform for commercial teams. You will work directly with clients, define the architecture, test the most important technical decisions yourself and establish the foundations for successful delivery. This is an architecture-first role with meaningful hands-on engineering: you will stay close enough to implementation to prove the architecture works and support it through production delivery, without becoming the primary engineer for every component. You will also help shape the reusable patterns, technical standards and accelerators behind Lynx’s growing AI-native life sciences practice. KEY RESPONSIBILITIES Solution Architecture Own the end-to-end solution architecture for client engagements, including data models, system design, integration patterns and technology choices. Translate business requirements into clear technical designs and implementation paths that delivery teams can build from. Design solutions spanning enterprise data, APIs, applications, cloud platforms and GenAI capabilities. Lead technical discovery with clients: understand requirements, assess existing systems and identify dependencies, constraints and delivery risks. Present architectural options and trade-offs clearly to technical teams, business stakeholders and senior leaders. Make pragmatic decisions across build speed, cost, scalability, security and maintainability. Review key implementation decisions and remain

NVIDIA is seeking a Senior System Architect: Heterogeneous EDA Systems to solve a complex challenge in accelerated computing: Failure Attribution at Scale. As EDA or equivalent experience workloads scale across thousands of heterogeneous nodes, a single failure can cause massive resource waste. We need an engineer to develop and build an automated framework. This framework will ingest telemetry from CPU and GPU clusters to identify the root cause of job failures in real-time. It will distinguish between hardware faults, infrastructure instability, and software defects. What you'll be doing: Architect Failure Attribution Frameworks: Build a scalable "flight recorder" for EDA jobs that captures high-fidelity state across the CPU, GPU, and Fabric at the moment of failure. Build automated diagnostics that correlate GPU XID errors, PCIe bus failures, and CUDA memory exceptions. Connect these errors with system-level events such as OOM kills or NUMA-related hangs. Distributed Logging & Tracing: Implement low-overhead tracing mechanisms (using tracing tools or custom agents) that provide access to job execution across multi-node Slurm or Kubernetes clusters. Root Cause Automation: Develop heuristics and models based on machine learning to classify failures as "Hardware Fault," "Software Bug," or "Environment Issue." This reduces the Mean Time to Identify (MTTI) for R&D teams. Resiliency Engineering: Work closely with hardware and infrastructure teams to define "signals of impending failure," enabling proactive job migration or check-pointing before a crash occurs. What we need to see: Distributed Systems Mastery: BS, MS, or PhD in Computer Science or Electrical Engineering (or equivalent experience) with 6+ years in systems programming. Experience building automated

About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here . The Conversion Visibility Modeling team enables a performant ads marketplace and helps prove value to advertisers by connecting Pinterest onsite activity with conversions that happen offsite (both digital and physical) in a privacy-preserving way. As a Machine Learning Engineering Manager on this team, you will lead a hybrid team of ML engineers and backend software engineers to build end-to-end identity and conversion visibility solutions across modeling, serving, and data infrastructure, so advertisers retain accurate, privacy-aware performance visibility as signals fragment and degrade. You will set the technical direction for high-impact ML systems that feed ranking, bidding, measurement, and reporting across Pinterest’s ads stack. What you’ll do: Attract, hire, develop, and lead a hybrid team of ML engineers and backend softwar

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. The Executive Director Software Engineering – PCW will be responsible for business operations readiness, partner engagement, and ongoing operational excellence for the Retail Open Platform. This team is accountable for ensuring that platform capabilities are configured correctly, contracts are operationalized accurately, and partners are informed, enabled, and confident . This role leads a cross‑functional team of software engineers, responsible for developing core capabilities and compliance, regulatory, safety, and operational insight capabilities for the retail open platform serving Grocers and Independent Pharmacies. Key Responsibilities Deliver and maintain software ecosystem which adhere to highly regulated compliance, regulatory, and safety requirement Build platform which allows partner to increase revenue through cross sell and upsell Own data pipelines, semantic layers, and reporting products that provide operational and growth insights for pharmacy partners. Ensure solutions meet enterprise standards for security, auditability, and regulatory compliance. Translate VP‑level strategy into clear roadmaps, backlogs, and execution plans for the pillar. Recruit, develop, and retain engineering and data talent; drive best practices in SDLC, data quality, and analytics. Responsibilities Own end ‑

About Pinecone: Pinecone is the leading vector database for building accurate and performant AI applications at scale in production. Pinecone’s mission is to make AI knowledgeable. More than 109,000 customers across various industries have shipped AI applications faster and more confidently with Pinecone’s developer-friendly technology. Pinecone is based in New York and has raised $138M in funding from Andreessen Horowitz, ICONIQ, Menlo Ventures, and Wing Venture Capital. About the Role/Team: As a Technical PMM , you will help developers, software engineers, and machine learning scientists understand how Pinecone enables knowledgeable AI applications. You will accomplish this through messaging and positioning, GTM strategy, launch planning, and enablement for key product features. You will work closely with Product, Sales, Developer Relations, and Marketing teams to ensure our customers understand the value of our platform. Responsibilities: Product Launches & GTM Strategy: Develop launch plans for product releases, align with product teams, and manage GTM execution. Lead core launch teams with cross-functional members (documentation , communications, developer relations, product, growth marketing, etc). Measure customer impact and adoption post-launch, iterating on strategies as needed. Positioning & Messaging: Craft compelling, technically accurate messaging for AI-powered search and retrieval solutions. Build product narratives for enterprise and developer audiences that differentiate Pinecone from competitors. Develop and maintain positioning for new product capabilities Customer & Market Insights: Partner with customers to develop case studies, gathering business impact metrics and architectural insights. Engage in customer interviews and research to refine messaging and identify key value drivers. Content Development & Enablement: Develop internal and external enablement materials, including sales training, pitch decks, and GTM enablement sessi

About Ema Ema is building the world’s leading Agentic AI platform to transform enterprise productivity. We enable organizations to delegate repetitive tasks to Ema, the Universal AI Employee, delivering 10x gains in workforce efficiency, across functions. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs. We are backed by industry leading investors including Accel, Naspers/Prosus, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver, Ema is at the frontier of what Agentic AI can do in production — we ship real systems that run real business processes at scale. Who you are We're looking for an innovative and passionate Machine Learning Engineers to join our team. You are someone who loves solving complex problems, enjoys the challenges of working with huge data sets, and has a knack for turning theoretical concepts into practical, scalable solutions. You are a strong team player but also thrive in autonomous environments where your ideas can make a significant impact. You love utilizing machine learning techniques to push the boundaries of what is possible within the realm of Natural Language Processing, Information Retrieval and related Machine Learning technologies. Most importantly, you are excited to be part of a mission-oriented high-growth startup that can create a lasting impact. You will: Conceptualize, develop, and deploy machine learning models that underpin our NLP, retrieval, ranking, reasoning, dialog and code-generation systems. Implement advanced machine learning algorithms, such as Transformer-based models, reinforcement learning, ensemble learning, and agent-based systems to continually improve the performance of our AI systems. Lead the processing and analysis of large, complex datasets (structured, semi-structured, and

Datadog AI Research — Scholars Program with Carnegie Mellon University Datadog AI Research (DAIR) is partnering with Carnegie Mellon University to support a small number of PhD students working on open research problems grounded by ongoing efforts at Datadog/DAIR. You will frame a problem, run your own experiments, and write up what you find, with compute and data at a scale most academic labs cannot provide. You will collaborate with colleagues working on the same questions. The Lab And The Research: DAIR is an industrial research lab motivated by practical challenges in observability and software operation: detecting and diagnosing failures, understanding complex production environments, and helping engineers operate software more effectively. The lab focuses on creating specialized foundation models, post-training and evaluating AI agents, and building frontier-scale machine learning systems. By combining fundamental research with Datadog's large-scale, real-world data and infrastructure, the lab develops new AI capabilities and translates them into practical systems with meaningful impact. Internship projects are shaped with your DAIR mentor and your CMU faculty advisor. You do not need prior experience with observability, monitoring, or infrastructure. What You'll Do: Own a research project end to end: framing the question, running the experiments, writing it up Work directly with a DAIR mentor engaged in the same problem, and stay connected to your advisor and lab Publish, and use the work toward your dissertation See research reach production, when it works Who You Are: Currently enrolled in a PhD program at Carnegie Mellon in machine learning, computer science, statistics, or a related field Depth in at least one area relevant to the research above Comfort running real experiments — training models, working with GPUs, reading and reimplementing recent papers Evidence you can do research: conference or workshop papers, preprin

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences – all created by our global community of developers and creators. At Roblox, we're building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We're on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you'll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. Why Safety AI Systems? As Senior Engineering Manager for Safety AI Systems at Roblox, you'll lead technical efforts and manage a team of experienced engineers to develop innovative AI solutions for language and text content safety. You'll oversee machine learning systems, data quality, training pipelines, and model performance to address challenges like real-time content understanding across Roblox's experiences

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. Why Safety AI Systems? As Senior Engineering Manager for Safety AI Systems at Roblox, you'll lead technical efforts and manage a team of experienced engineers to develop innovative AI solutions for multimodal content safety. You’ll oversee machine learning systems, constructing multimodal model architectures, improving data quality, training pipelines, and model performance to address challenges like real-time multi-verse content understanding and advanced moderation with large vision language models, spanning avatars, images, videos, audios, text, code / data models, and their composites. In close collaboration with product, policy, and Trust & Safety teams, you'll design large-scale systems to detect and mitigate abusive behavior before it harms the community. You'll own critical services at massive scale, balancing user freedom with platform civility to protect and empower our users. Your leadership will help ensure Roblox remains a safe, inclusive space for self-expression and shared experiences. You Will Own the vision, technical direction, and execution of machine learning solutions for the Multimodal Safety AI system, ensuring these systems effectively detect and prevent ha

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. With Roblox Ads business growing at a rapid rate, we are building large scale ads machine learning infrastructure to deliver effective performance ads to our users, and more business values to our advertisers. We’re looking for an EM to lead a team of exceptional ML infrastructure engineers, build scalable, reliable, and high-performance infrastructure that powers ML systems across our organization. You’ll operate at the scales of hundreds of billions of engagements, and redefine how we deliver performance ads to hundreds of millions of users. You Will: Lead strategic planning and roadmap execution of scalable production-ready ML systems including model training, data pipelines, feature engineering and model inference. Own the architecture, establish engineering best practices of scalability, reliability, and cost-effectiveness of ML infrastructure (e.g., training, serving, feature). Work closely with data scientists, ML engineers, platform teams, and product stakeholders to design, implement, and operate robust ML platforms that accelerate model development and deployment. Recruit, mentor, and grow a high-performing team of ML infrastructure engineers. You Have: 5+ years of experienc

About Sentry Software runs the world and the pace is faster than ever. Sentry helps developers fix errors and performance issues before users notice, so teams can spend less time firefighting and more time building. Trusted by 200,000+ organizations, Sentry is today’s application monitoring standard and our team is building its AI-native future. About the role AI and machine learning are reshaping how developers debug, monitor, and ship software, and Sentry is uniquely positioned to lead that shift. We sit on a novel and massive dataset of real production errors, spans, and logs from tens of thousands of engineering organizations — the kind of signal that makes ML genuinely useful, whether it's a clustering model that groups related issues, a ranking system that surfaces the right alert at the right time, or an agent that proposes a fix. We're looking for an Engineering Manager to lead and grow our Machine Learning Engineering team. This team owns the full spectrum of ML at Sentry: classical techniques like clustering, ranking, anomaly detection, and embeddings that quietly power core product surfaces today, alongside the LLM-based and agentic systems shaping where the product is headed. You'll partner closely with product, design, and engineering leaders to decide where ML belongs in our products, what kind of ML actually fits the problem, and how we translate that work into experiences millions of developers rely on every day. In this role you will Set technical direction across the team's full ML surface area — from classical models for clustering, ranking, and anomaly detection to LLM-based and agentic systems — and make sharp calls about which approach fits each problem Define how the team evaluates and monitors ML systems in production, from offline metrics to online experimentation to model and agent observability Stay hands-on enough to review code and model designs, contribute to architecture discussions, and unblock engineers on complex ML problems Define