Senior Ml Science Manager Jobs

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day. Smartsheet is hiring a Senior Machine Learning Operations Engineer to architect our machine learning production lifecycle. Your mission is to maintain and deploy ML models to a scalable, reliable, and secure production environment. You will design and maintain the infrastructure, automation, and monitoring systems that ensure our AI products are high-performing and cost-effective. You will report to our Director, Analytics Engineering & Data Governance and work from our Bangalore, India office. You Will: Model and Pipeline Automation Automate the deployment and retraining of ML models, from training through to production inference, by building and managing complete CI/CD/CT (Continuous Training) pipelines, adhering to MLOps best practices. Build, fine-tune, or use pre-trained LLMs, deep learning models or traditional machine learning models. Evaluate and recommend AI or ML solutions for the product using any combination of vendor solutions and/or custom-built models. Governance & Compliance Implement model versioning, lineage tracking, and auditing to ensure compliance with security and ethical standards. Performance Monitoring Continuously monitor the health and performance of production machine learning models, proactively identifying and correcting model drift, staleness, and performance degradation. Incorporate user feedback for iterative improvements and manage necessary model retraining cycles. Cross-Functional Collaboration Act as the "glue" between Data Scientists (who build models

P-1428 At Databricks, we run the world's largest AI platform. We believe the next breakthroughs will come from models trained on domain-specific enterprise data, not from bigger general-purpose models alone. That's why we built AI Runtime , a serverless product that makes GPU training of any size feel simple, so companies can train their own models the way frontier labs do. Customers across industries already use it to train models on governed data, from automotive AI at Rivian to Merck's TEDDY foundation models for single-cell biology and drug discovery, all the way to agents like KARL from Databricks AI Research. It takes a lot of engineering to make that work, and we've written about it on our blog: how we keep GPUs reliable , make training fault-tolerant , and speed up fine-tuning with fused kernels and smart sequence packing. As a Senior Software Engineer, you'll work on the systems behind all of this, solving hard technical problems and helping shape how AI Runtime evolves. The impact you will have: Build core parts of AI Runtime and contribute to Databricks' technical direction in AI training infrastructure. Develop reliability and observability features for a fleet that keeps growing in scale and complexity. Improve AI Runtime's developer experience, so the largest training jobs feel as simple as a first experiment. Help prepare AI Runtime for new GPU generations and workload types. Take on hard multi-node training problems, from orchestration and GPU scheduling to data loading and checkpointing. Make training faster and cheaper by raising GPU efficiency across model architectures and hardware generations. Work with research, product, and customers on what we build next and own features from design through production. Champion engineering excellence, raise the bar, and help the engineers around you grow. What we look for: 5+ years in large-scale distributed systems, with experience in GPU training, high-performance computing, or ML systems. Experience traini

About Outreach Outreach, founded in 2014, is the only complete agentic AI platform for revenue teams. Outreach infuses agentic AI, conversation intelligence, and assistive AI to power hundreds of use cases across revenue motions. From new logo prospecting to expansions, deal acceleration, driving retention, and forecasting, Outreach AI automates workflows and frees sellers to focus on more strategic conversations and actions. Revenue leaders benefit from connected account visibility, performance insights, and higher forecasting accuracy across every GTM team. World leading enterprise organizations use Outreach to power their revenue teams, including Databricks, SAP, Siemens, and Verizon to name a few. About the Team The Dialog Understanding and Generation team is building the industry's most advanced agentic AI capabilities for revenue orchestration, the end-to-end coordination and execution of sales and customer engagement workflows. Our mission is to transform raw customer interactions and CRM data into structured intelligence that powers the entire revenue lifecycle. We operate at the intersection of machine learning, LLM-powered systems, and cloud-scale engineering, driving both current and next-generation AI initiatives at Outreach. We are a team of applied data scientists who thrive on solving complex scientific and engineering challenges. The work is roughly an even split between applied ML research and production engineering, so you should be excited about both. You'll collaborate closely with exceptional engineers and product managers in a fast-moving, high-impact environment, applying your creativity and expertise to build innovative solutions that push the boundaries of agentic AI. Your Daily Adventures Will Include: <li aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="5" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":

About the position Showpad is building a Revenue Intelligence engine that transforms raw signals—CRM data, email threads, transcripts, and content engagement—into prescriptive AI guidance. We are looking for a technical Product Manager to own the unified data intelligence layer that powers this engine. You will define how we capture and model data, and critically, determine the best retrieval and inference strategies (Search, RAG, Knowledge Graphs, or Vector Similarity) for every use case. Key Responsibilities Data Modeling & Architecture : Define the canonical model for entities (deals, contacts, skills) and establish the retrieval strategy—choosing between keyword, dense vector, hybrid, or graph traversal based on latency and accuracy. Search & RAG Ownership : Own the end-to-end RAG pipeline (chunking, embeddings, indexing). Write technical specs for retrieval layers in Meeting Prep and Roleplay AI, and define evaluation metrics (Recall@k, MRR). Revenue Intelligence Roadmap : Drive the vision from "trusted foundation" to "prescriptive AI." Own the product logic for Deal Health and Winning Behavior models. Platform Enablement : Act as the internal PM champion for the data platform, providing semantic primitives, event taxonomies, and data contracts to other product squads. Discovery & Strategy : Conduct deep customer discovery and stay ahead of LLM/Search trends, translating fuzzy business questions into scoped ML problems. Required Skills Must haves 5+ years in PM: at least 2 years specifically owning data platforms, search, analytics, or ML products. Retrieval Expertise: Hands-on experience shipping RAG pipelines or search systems. You understand the trade-offs between BM25, vector search, and reranking. Data Technicality : Fluency with Datalake architectures (Delta Lake, BigQuery, etc.) and strong entity-relationship modeling skills. ML Fluency : Ability to define features, label designs, and evaluation metrics. You can review a technic

Everpure (NYSE: P) has evolved from storage pioneer to data platform, closing fiscal 2026 with $3.7 billion in revenue, its first billion-dollar quarter, and accelerating growth into FY27. Our strategic agenda spans the companies defining the next era of technology - hyperscalers, AI labs, the AI hardware supply chain, data platform providers, and the broader AI ecosystem. This type of work—work that changes the world—is what the tech industry was founded on. So, if you're ready to seize the endless opportunities and leave your mark, come join us. THE ROLE Our Senior Applied AI Engineer builds and operate production-grade AI systems that extract meaning from large-scale unstructured document collections, enabling enterprise data discovery classification, and governance. This role owns the full lifecycle of graph intelligence solutions — from problem definition and data modelling, to building and enriching knowledge graphs, and deploying ML- and LLM-assisted analytics in production. The focus is on semantic and contextual analysis of unstructured data to uncover relationships, patterns, and insights that support AI safety, security, and compliance requirements. WHAT YOU'LL DO Design, build, and deploy graph-based AI solutions, combining knowledge graphs , LLMs, and ML models applied to large-scale unstructured data Define and own data pipelines that extract, transform, and enrich entity relationships into production-grade knowledge graphs Integrate LLMs and ML models into text processing pipelines for classification, embedding generation, document similarity, and semantic analysis Design, deploy, and operate graph and vector databases to support retrieval, reasoning, and analytics Optimize models and inference pipelines for production constraints including latency, throughput, cost, and infrastructure Deploy, monitor, and iterate on ML systems in production environments ensuring reliability and continuous integration Drive architectural decisions and tech

Machine Learning Engineer – Biometrics (Computer Vision) We’re looking for a Staff/Senior Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You’ll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process. What You’ll Do Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition) Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions. Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps. Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS. Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team. What We’re Looking For Experience: 5+ years of industry experience in Machine Learning, with at least 3 years dedicated to Biometrics or Face Analysis. Deep expertise in computer vision and biometrics, especially face recognition. Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact. Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code. Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestra

About Snorkel Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler! The Role We are seeking a highly technical and strategic Senior Product Manager with deep AI/ML expertise to lead development across our agentic data systems, synthetic data systems, evaluations, and data-acceleration initiatives. This role is ideal for someone with a core ML/AI product background, strong familiarity with model development workflows, and demonstrated success delivering product experiences that improve the speed, quality, and scalability of data operations. You will work closely with Research, Engineering, Data Operations, and Design to define product vision, roadmap, and execution plans for intelligent workflow systems that optimize data pipelines, improve task quality, and enable faster and more efficient delivery of AI-ready datasets. This is a highly cross-functional, technically deep IC role with broad surface area and significant impact. You will lead initiatives spanning multiple teams, balancing near-term execution with long-term platform evolution. What You’ll Do Own the product vision, strategy, and roadmap for the task and Expert Contributor Evaluations product supporting internal and external platform users. &nbsp

Scale GP (Scale Generative AI Platform) is an enterprise-grade Generative AI platform providing APIs for knowledge retrieval, inference, evaluation, and more. We are seeking a strong Senior Full-Stack Engineer to help us build, scale, and refine our rapidly growing product. The ideal candidate is deeply grounded in software engineering best practices and experienced in developing and scaling modern web applications end-to-end. You will work across the stack—from React/TypeScript frontends to Python-based backends—while integrating with LLMs and machine learning systems. You will solve complex challenges in scalability, reliability, and product experience while owning significant product areas in a fast-paced environment. What You’ll Do Own major full-stack product areas , driving features from design through production deployment. Build modern frontend experiences using React and TypeScript, ensuring performance, usability, and responsiveness. Develop reliable backend services in Python, working with distributed systems, data pipelines, and ML/LLM components. Integrate with LLMs, vector databases, and AI infrastructure to power intelligent product experiences. Deliver experiments and new features quickly , maintaining high quality and tight feedback loops with customers. Collaborate across product, ML, and infrastructure teams to shape the direction of Scale GP. Adapt quickly —learning new technologies, frameworks, and tools as needed across the stack. Ideal Experience 5+ years of full-time engineering experience , post-graduation. Strong experience developing full-stack applications using React, TypeScript, and Python . Experience scaling or shipping products at high-growth startups . Familiarity with LLMs, vector databases, embeddings, or other modern AI tooling (tinkering or production experience welcome). Proficiency with SQL and modern API development. Experience with Kubernetes , containerization, and microservice architectures. Experience working with at leas

About the Role Anyscale is seeking a Senior / Staff Product Manager to lead Ray Data, our scalable data processing library for ML and AI workloads. This is a uniquely challenging role that requires balancing open source growth with commercial differentiation - driving rapid adoption in the open source Ray Data ecosystem while building compelling proprietary features for Anyscale RunTime, our high-performance commercial engine. You'll own the entire Ray Data product roadmap in a competitive landscape, working closely with the engineering team, the field team, enterprise customers, and the open source community. Success requires: Deeply ingraining yourself into the end-user experience to understand the nature of the product and its gaps and tradeoffs Working closely with customers and open source users to draw the subtle line between growth and commercialization Strategic thinking about which parts of the ML/Data lifecycle to focus on, identifying opportunities where our architectural strengths create the most value. Thinking deeply about and clearly articulating the product strategy to stakeholders Key Responsibilities Drive the Ray Data product roadmap - Balance open source Ray Data feature development with Anyscale Runtime commercial differentiation to ensure that both Ray Data becomes the open source standard for AI data processing and Anyscale Runtime remains sufficiently compelling. Drive open source Ray Data adoption - Focus on community growth, developer experience, and ecosystem integrations Market Positioning & Enablement - Work closely with Product Marketing on strategic market positioning, field enablement, and competitive analysis to maintain differentiation. Customer engagement - Drive key customer engagements assisting sales and field engineering teams. Required Qualifications 4+ years of product management experience with technical products Strong technical background in distributed systems, ML infrastructure, or data processing Experience working

Senior Machine Learning Engineers at Thoughtworks build, maintain and test the architecture and infrastructure for managing machine learning applications. They are involved in supporting and contributing to the design of the end-to-end applications and products. They are responsible for building core capabilities including technical and functional machine learning systems and applications, being the anchor for functional streams of work and are accountable for timely delivery. As a senior machine learning engineer, you will work on the latest tools, frameworks and offerings while also being involved in enabling credible and collaborative problem solving to execute on a strategy. Due to the nature of the projects and specific client security clearance, this role requires the successful candidate to be a Singapore Citizen or PR of Singapore. Job responsibilities You will contribute to design and drive the development of robust scalable architectures and infrastructure for deploying and managing machine learning (ML) applications, ensuring high availability, performance and security You will collaborate with data scientists and engineers to translate business needs into effective and efficient ML systems and applications You will own the development and maintenance of core functionalities within ML applications, including ML pipelines, model training and deployment, and monitoring and evaluation You will drive the functional stream of work by providing technical expertise, handling team discussions and ensuring timely delivery of assigned tasks You will stay ahead of the curve by actively exploring and implementing the latest tools, frameworks and offerings in the ML landscape You will facilitate collaborative problem solving within the team by actively listening, communicating effectively and mentoring other engineers You will contribute to the development and execution of the team's overall ML strategy, aligning technical capabilities with business objectives You wil

About the Role The Senior AI Evals Test Engineer is a hands-on technical role responsible for defining, developing, and executing robust evaluation frameworks and test strategies for AI agents built using the Google Agent Development Kit (ADK) or similar sort of agentic frameworks . This role bridges AI development and reliable deployment — ensuring our agents are safe, ethical, effective, and meet high-quality performance standards. A significant focus of the role is on Responsible AI and Safety Evals, including adversarial testing, red-teaming, bias detection, and toxicity measurement . The engineer will design scalable evaluation pipelines , build synthetic test environments, and work closely with AI/ML Engineers, Researchers, and policy teams to ensure agents are production-ready and compliant with regulatory and ethical standards. The role is 70% Automation and 30% Manual Testing , and sits within the Applied AI: Test Engineering team as a key technical authority. Key Responsibilities Design and build agent evaluation frameworks — create end-to-end infrastructure for evaluating LLM-based agents across correctness, latency, cost, safety, and task completion rate Build synthetic testing environments and simulation strategies to stress-test AI agents under diverse real-world conditions, with specific focus on agents built on Google ADK and related Google AI/ML services (e.g., Vertex AI, Gemini models) Build golden datasets and test harnesses — curate domain-specific evaluation sets for Rubrik's product verticals (data security, SaaS protection, cyber recovery, compliance) Establish eval-as-a-service — build and enhance centralized evaluation platform that any IT team members can self-serve to test their agents against standardized and custom benchmarks Automate regression and drift detection — implement CI/CD-integrated eval pipelines that catch regressions on every model or prompt change before production deployment Define eval taxonomies and rubrics — est

What if the work you did every day could impact the lives of people you know? Or all of humanity? At Illumina, we are expanding access to genomic technology to realize health equity for billions of people around the world. Our efforts enable life-changing discoveries that are transforming human health through the early detection and diagnosis of diseases and new treatment options for patients. Working at Illumina means being part of something bigger than yourself. Every person, in every role, has the opportunity to make a difference. Surrounded by extraordinary people, inspiring leaders, and world changing projects, you will do more and become more than you ever thought possible. Senior Data Engineer Role Overview The Databricks Platform Engineer is the technical owner of our Databricks Lakehouse platform — the shared foundation on which enterprise data products, analytics, reporting and AI/ML workloads are built. This is a hands-on individual-contributor role responsible for the reliability, scalability, security, automation, governance and cost efficiency of the Databricks platform. The successful candidate will have production experience operating Databricks as an enterprise platform, not just building on it. They will work AI-natively and set that standard for the team. You will own the Data Platforms domain within a cross-skilled Data & Analytics Platforms engineering team. You will establish platform engineering standards, design and build infrastructure automation, create reusable reference architectures and engineering patterns, and ensure the platform can be operated reliably at enterprise scale. You will also mentor engineers and help build platform engineering capability within the India team. Key Responsibilities Platform ownership Own workspace- and accoun

Senior Product Manager, Provider and RWD Health Systems Applications Natera's RWD platform at the clinic connects an ecosystem of real-world evidence, molecular data, and clinical insights to deliver actionable information to physicians. We are seeking a Senior Product Manager to help build and scale physician-facing RWD applications. We seek to unlock the full potential of real-world data to improve treatment outcomes and make molecular results actionable in everyday treatment decisions. In this role, you'll own a physician-facing RWD application for a patient's molecular and clinical journey and workflow integrations behind it, partnering with cross-functional product teams, engineering, AI/ML teams, and clinical teams to deliver intuitive tools centered on enriched patient insights. What You'll Do Advance Data Integration and Intelligence Collaborate with clinical product management, EMR, user experience product management, marketing, medical and a broad cross functional team to drive product strategy and roadmap, Drive integration of genomic tests into the platform (Signatera/ctDNA, hereditary risk, tumor/normal and NGS profiling, liquid CGP, etc.) as part of a unified oncology digitized experience. Work closely with AI/ML teams to build applications for patient-level insights and clinic-level insights, including cohort comparison, clinical trial matching, and cohort analytics back to individual patient care. Partner with legal, privacy, and security so HIPAA obligations, PHI handling, and data-governance requirements are designed into the product. Elevate the User Experience Own the single- and multi-patient views that bring a patient's molecular results and clinical events together in one physician-facing application. Integrate AI-powered clinical trial matching that connects patients and physicians to the most relevant investigational therapies based on molecular, clinical, and patient criteria. Help reduce friction points at both the provider and patie

About the Team The Spark Platform team owns and operates DoorDash's Apache Spark ecosystem — the execution runtime, remote shuffle service, cluster scheduler, and reliability tooling that powers the company's data, analytics, and ML workloads. We run Spark across the company at significant scale and continue to expand the workloads, capabilities, and consumer base we serve. Orchestrating and operating thousands of Spark cluster deployments is a complex distributed system problem which the team invests heavily in runtime optimization, systems architecture, multi-tenant scheduling, and end-user tooling. About the Role As a Senior Software Engineer on Spark Platform, you will set the technical direction for our in-house Spark deployment and shape the architecture that will run DoorDash's data, analytics, and ML compute for the next five years and beyond. You will own the deep, cross-cutting problems that span the runtime, the shuffle service, the scheduler, and the overall service reliability — making the architectural calls that compound across the platform's lifetime. You will partner with the Engineering Manager on technical roadmap, hiring, and team shape, and act as the senior technical voice in cross-team partnerships with Data Engineering, ML Platform, and product engineering teams that depend on the platform. You must be located in San Francisco, Sunnyvale, Seattle, or New York City for this hybrid position. You will report into the Engineering Manager on our Spark Platform team. You're excited about this opportunity because you will… Set the multi-year technical direction for an in-house Spark-on-Kubernetes platform — runtime, shuffle, scheduler, reliability — and make the architectural calls that compound for years. Own the deepest distributed-systems problems on the team: shuffle architecture, multi-tenant scheduling, runtime performance, and the failure modes that only show up at scale. Partner with the Engineering Manager on technical roadmap, hiring, inte

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Micron’s Heterogeneous Integration Group (HIG) is shaping the future of AI and accelerated computing by developing advanced memory solutions! The team focuses on designing and optimizing High Bandwidth Memory (HBM) products for AI/ML, high-performance computing (HPC), and data-centric systems, collaborating across global engineering and product teams to deliver industry-leading performance, power efficiency, and reliability! HBM product family is growing due to high demand from industry-leading semiconductor companies. As an HBM Memory Design Engineer you will work closely with cross-functional teams including Design Engineering (DE), Product Engineering (PE), Process Integration, Packaging, and Technology Development (TD) to analyze and resolve silicon issues across HBM products . You will collaborate with architecture to understand new or updated specification requirements and contribute to the implementation, verification, and maintenance of design updates , supporting both silicon release and ongoing product lifecycle needs. This role requires strong AI driven technical execution and the ability to independently drive complex issues to closure. Responsibilities Design Support: Work on HBM product design and support sustaining design maintenance (ECOs/spec changes). Silicon Debug: Own end-to-end debug of silicon, system, and customer issues, driving root-cause analysis and resolution. Cross-Functional Collaboration: Work closely with DE, PE, TD,