Senior Ai Ml Data Model Architect Jobs

Why RoboForce RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. The company's robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability. We are looking for a Senior / Staff AI Research Engineer, Data Infrastructure to build the data and learning engine behind RoboForce's Physical AI stack. In this role, you will own the full pipeline — from raw teleoperation and UMI device data collection through curation, annotation, and storage, to post-training infrastructure that scores demonstrations, identifies failure patterns, and closes the loop back into model retraining. Responsibilities Design and maintain end-to-end data collection pipelines ingesting multimodal demonstration data from teleoperation devices and UMI hardware, including synchronization, versioning, and distributed storage at scale. Build annotation tooling and data curation workflows — quality filtering, deduplication, episode scoring, and domain reweighting — to produce high-quality training datasets for robot policy learning. Develop post-SFT reinforcement learning infrastructure: implement reward scoring on demonstrations, mine and categorize failure patterns, and feed curated failure data back into the retraining loop. Build evaluation and test infrastructure to log policy rollouts on-robot, capture structured results, and surface actionable diagnostics for the research team. Collaborate with ML researchers to define data schemas, episode formats, and pipeline interfaces that support rapid iteration on VLA and manipulation policy training. Architect scalable storage and retrieval systems for heterogeneous robot data (vision, proprioception, action, language) across both cloud and on-prem environments. Requirements Bachelor's or Master's degree in Computer Science, Robotics, or related field with 5+ years of experience. Strong proficiency in Python and experience build

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

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

DeepIntent is the leading healthcare marketing platform, purpose-built to help marketers plan, activate, and optimize data-driven campaigns with speed and precision. Trusted by the world’s top healthcare brands and their agencies, DeepIntent uniquely unites media, identity, and real-world clinical data to power privacy-safe, omnichannel marketing across every screen. Backed by patented technology and proven outcomes, DeepIntent’s platform delivers measurable audience quality and script lift at scale. Learn more at www.deepintent.com . What You'll Do: DeepIntent is seeking an MLOps Engineer to join our European Data Infrastructure team. The MLOps Engineer will partner with our Data Science and AI teams to build and scale a high-performance machine learning and AI platform spanning our on-prem data centers and GPU resources - built on Python, Spark, Kubernetes, Docker, and modern orchestration frameworks (Argo Workflows, Airflow). Partner with Data Science and AI Engineering teams to adopt MLOps best practices and migrate training/inference workloads onto the platform Implement and maintain model tracking, versioning and experiment management (MLflow) with observability into model performance and drift Build CI/CD pipelines purpose-built for ML/AI artifacts (model registries, container image pipelines, automated retraining triggers) Continuously improve platform reliability, cost-efficiency and maintainability of the underlying codebase Establish monitoring and observability for ML/AI systems (Prometheus, Grafana) covering GPU utilization, model latency, throughput and custom ML metrics Design and operate ML/AI deployment infrastructure, including GPU cluster architecture, model serving and tool selection across training and inference workloads Build and maintain infrastructure for LLM and generative AI workloads, including model fine-tuning pipelines, vector databases, RAG architectures and inference optimization Collaborate with business units and Product on ML/AI-d

As a Forward Deployed Engineer (FDE) you will work with customers to build and productionize solutions to their data & AI challenges using the Databricks platform. You will own the architecture, lead design decisions, and implement end-to-end systems spanning data engineering, AI, and application development. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations. FDEs deliver with customer empathy, integrating with client systems, training, and other technical needs to help customers get most value out of their data. This is a hands-on, customer-facing role for builders who thrive at the intersection of technology and business impact. The ideal candidate combines engineering expertise with adaptability, curiosity, and a passion for working with customers and teammates to solve complex problems that drive measurable outcomes. FDEs are billable and know how to complete projects according to specification with exceptional customer empathy. The impact you will have: Production Solution Delivery: Lead impactful customer technical projects by delivering production-grade systems, designing and building reference architectures, custom applications and data ingestion and ML/AI model integration Transformational Impact: Guide strategic customers as they implement transformational big data projects including end-to-end design, build and deployment of industry-leading big data and AI applications. Work with engagement managers to scope technical delivery work with input from the customer Empower Customers: Guide customers on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks. Own the Architecture: Lead architecture and design decisions, ensuring solutions are secure, scalable, and aligned with both customer needs and Databricks best practices. Work with the Databricks technical t

CSQ427R228 As a Senior Forward Deployed Engineer (FDE) you will work with customers to build and productionize solutions to their data & AI challenges using the Databricks platform. You will own the architecture, lead design decisions, and implement end-to-end systems spanning data engineering, AI, and application development. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations. FDEs deliver with customer empathy, integrating with client systems, training, and other technical needs to help customers get most value out of their data. This is a hands-on, customer-facing role for builders who thrive at the intersection of technology and business impact. The ideal candidate combines engineering expertise with adaptability, curiosity, and a passion for working with customers and teammates to solve complex problems that drive measurable outcomes. FDEs are billable and know how to complete projects according to specification with exceptional customer empathy. The impact you will have: Production Solution Delivery: Lead impactful customer technical projects by delivering production-grade systems, designing and building reference architectures, custom applications and data ingestion and ML/AI model integration Transformational Impact: Guide strategic customers as they implement transformational big data projects including end-to-end design, build and deployment of industry-leading big data and AI applications. Work with engagement managers to scope technical delivery work with input from the customer Empower Customers: Guide customers on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks. Own the Architecture: Lead architecture and design decisions, ensuring solutions are secure, scalable, and aligned with both customer needs and Databricks best practices. Work with the Data

P-1620 As a Senior Forward Deployed Engineer (FDE) you will work with customers to build and productionize solutions to their data & AI challenges using the Databricks platform. You will own the architecture, lead design decisions, and implement end-to-end systems spanning data engineering, AI, and application development. We work cross-functionally with engineering, product, and developer relations to shape long-term strategic priorities and initiatives. FDEs deliver with customer empathy, integrating with client systems, training, and other technical needs to help customers get the most value out of their data. This is a hands-on, customer-facing role for builders who thrive at the intersection of technology and business impact. The ideal candidate combines engineering expertise with adaptability, curiosity, and a passion for working with customers and teammates to solve complex problems that drive measurable outcomes. FDEs are billable and know how to complete projects according to specifications with exceptional customer empathy. The impact you will have: Production Solution Delivery: Lead impactful customer technical projects by delivering production-grade systems, designing and building reference architectures, custom applications, and data ingestion and ML/AI model integration Transformational Impact: Guide strategic customers as they implement transformational big data projects including end-to-end design, build and deployment of industry-leading big data and AI applications. Work with engagement managers to scope technical delivery work with input from the customer Empower Customers: Guide customers on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks. Own the Architecture: Lead architecture and design decisions, ensuring solutions are secure, scalable, and aligned with both customer needs and Databricks best practices. Work with the Databri

Req ID: CSQ128R10 Location: London, United Kingdom - Hybrid Job Role: Senior Forward Deployed Engineer - Architect All levels Recruiter: Francesca Paternoster As a Forward Deployed Engineer (FDE) you will work with customers to build and productionize solutions to their data & AI challenges using the Databricks platform. You will own the architecture, lead design decisions, and implement end-to-end systems spanning data engineering, AI, and application development. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations. FDEs deliver with customer empathy, integrating with client systems, training, and other technical needs to help customers get most value out of their data. This is a hands-on, customer-facing role for builders who thrive at the intersection of technology and business impact. The ideal candidate combines engineering expertise with adaptability, curiosity, and a passion for working with customers and teammates to solve complex problems that drive measurable outcomes. FDEs are billable and know how to complete projects according to specification with exceptional customer empathy. The impact you will have: Production Solution Delivery: Lead impactful customer technical projects by delivering production-grade systems, designing and building reference architectures, custom applications and data ingestion and ML/AI model integration Transformational Impact: Guide strategic customers as they implement transformational big data projects including end-to-end design, build and deployment of industry-leading big data and AI applications. Work with engagement managers to scope technical delivery work with input from the customer Empower Customers: Guide customers on architecture and design; bootstrap or implement customer projects which leads to a customers' successful understanding, evaluation and adoption of Databricks. Own the Architecture: Lead architecture an

About the opportunity We are seeking a hands-on, strategic Senior Product Manager - Ops Automation & Enablement (OAE) with a passion for building the intelligence and systems behind world-class banking operations. This is a true builder’s role: you will lead the strategy, design, delivery, and enterprise-scale adoption of Agentic AI, hyperautomation, process mining, and internal tooling to reshape how complex, control-intensive Banking Operations are executed. You will work cross-functionally with product managers, engineers, data scientists, and operational leaders to ensure N26 can scale aggressively across markets and product offerings without proportional headcount growth—all while maintaining the strict regulatory and quality standards our customers expect. In this role, you will: Lead Enterprise Transformation & Agentic AI Adoption: Architect AI Workflows: Design, deploy, and govern E2E, multi-step Agentic AI solutions and automations that target high-volume processes, cross-team friction, and manual back-office tasks. AI Champion: Act as the primary AI Champion for Banking Operations: translating complex ML/AI concepts into practical, working systems rather than just talking about them. Change Management & Value Realization: Identify structural and behavioral barriers to adoption; design frameworks that embed new AI capabilities into day-to-day operations and team decision-making. End-to-End Operating Model & Process Intelligence: Process Mining Re-Engineering: Lead structured process capture, process mining, and workflow mapping. Evaluate opportunities to completely redesign end-to-end workflows rather than merely automating existing inefficiencies. Core CRM & Platform Integration: Partner with Conversational Banking and Platform owners to optimize Salesforce (our core case-management backbone) and integrate AI agents with our central architecture and AWS infrastructure. Operational Efficiency & ROI: Define and track clear metrics for

About the opportunity We are seeking a hands-on, strategic Senior Product Manager - Ops Automation & Enablement (OAE) with a passion for building the intelligence and systems behind world-class banking operations. This is a true builder’s role: you will lead the strategy, design, delivery, and enterprise-scale adoption of Agentic AI, hyperautomation, process mining, and internal tooling to reshape how complex, control-intensive Banking Operations are executed. You will work cross-functionally with product managers, engineers, data scientists, and operational leaders to ensure N26 can scale aggressively across markets and product offerings without proportional headcount growth—all while maintaining the strict regulatory and quality standards our customers expect. In this role, you will: Lead Enterprise Transformation & Agentic AI Adoption: Architect AI Workflows: Design, deploy, and govern E2E, multi-step Agentic AI solutions and automations that target high-volume processes, cross-team friction, and manual back-office tasks. AI Champion: Act as the primary AI Champion for Banking Operations: translating complex ML/AI concepts into practical, working systems rather than just talking about them. Change Management & Value Realization: Identify structural and behavioral barriers to adoption; design frameworks that embed new AI capabilities into day-to-day operations and team decision-making. End-to-End Operating Model & Process Intelligence: Process Mining Re-Engineering: Lead structured process capture, process mining, and workflow mapping. Evaluate opportunities to completely redesign end-to-end workflows rather than merely automating existing inefficiencies. Core CRM & Platform Integration: Partner with Conversational Banking and Platform owners to optimize Salesforce (our core case-management backbone) and integrate AI agents with our central architecture and AWS infrastructure. Operational Efficiency & ROI: Define and track clear metrics for

Req ID - FEQ427R59 Location: London The Role As a Sr. Solutions Engineer, you will independently lead technical engagements for customers, owning discovery, solution design, and platform demonstrations. You are a builder who can code, architect, and present—combining technical depth with customer-facing skills to drive Databricks adoption. You will own frontline customer relationships and work with your Account Executive to develop technical strategies that expand platform usage. The Impact You Will Have Independently lead technical discovery and solution design for customer workloads spanning data engineering, analytics, and machine learning Build and deliver compelling proofs-of-concept and live demos on the Databricks Platform that drive technical wins Own frontline technical relationships with customer engineers, data teams, and technical leads Develop account-level technical strategies in partnership with your Account Executive to grow platform consumption Navigate competitive landscapes by articulating Databricks differentiation through hands-on demonstrations Contribute reusable technical assets (notebooks, solution accelerators, reference architectures) to the broader SA community What We Look For 4+ years in data engineering, solutions architecture, technical pre-sales, or a hands-on consulting role Proficient in Python and SQL with demonstrated ability to debug, optimize, and write production-quality code — live coding is a required interview stage Hands-on experience designing and implementing data solutions on at least one public cloud platform (AWS, Azure, or GCP) Working knowledge of distributed data systems: Apache Spark™, Delta Lake, or equivalent (Hadoop, Kafka, Flink) Experience leading technical customer conversations — discovery, whiteboarding, architecture reviews Familiarity with one or more: data engineering (ETL/ELT, medallion architecture, streaming), data science/ML (model training, MLOps), or SQL analytics Strong presentation and dem

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . About the Role: We are seeking a Senior Staff Machine Learning Engineer to define and accelerate how we leverage agentic AI to transform machine learning models and modeling workflows. This high-impact role focuses directly on modeling leverage —enhancing model quality and velocity—rather than building general infrastructure. As a senior staff engineer, you will operate as a technical leader across the entire modeling loop, utilizing modern AI-assisted development systems (such as Claude, Cursor, and comparable tools) to make model development faster, more rigorous, and significantly more effective across the organization. Why Join Us? This role offers a domain-flexible platform to redefine how ML engineering is done. If you are passionate about combining deep ML fundamentals with the vanguard of agentic AI to multiply team impact, we’d love to talk to you. What you’ll do: Develop advanced ML models and the agentic workflows that help teams design, train, evaluate, and improve them. Use tools such as Claude, Cursor, and comparable AI-assisted development systems to investigate model behavior, generate and test hypotheses, automate repetitive analysis, and improve code and experiments. Build reliable loops for data and label analysis, feature and embedding development, architecture comparison, hyperparameter optimization, error analysis, and experiment readouts. Design evaluation harnesses that measure both model outcomes and agent/workflow quality, including correctness, reproducibility, regressions, cost, a

About the role Chime’s Machine Learning Platform (MLP) team builds and operates the infrastructure, tooling, and developer experience that powers machine learning across the company. We enable data scientists and ML engineers to develop, train, deploy, and monitor models reliably and efficiently. As a Senior Software Engineer on the Machine Learning Platform team, you will design and build scalable systems spanning traditional machine learning and emerging AI workloads, including model training, feature computation, real-time inference, foundation-model access, evaluation, and agentic orchestration. You’ll work at the intersection of distributed systems, cloud infrastructure, applied machine learning, and AI product engineering. This role focuses on creating secure, reliable, and reusable platform capabilities that help teams choose the right approach, from conventional predictive models to LLM-powered and multi-step agentic systems, while maintaining strong standards for evaluation, observability, governance, privacy, and cost efficiency. In this role, you can expect to Design, build, and operate scalable ML and AI infrastructure on AWS. Design and operate shared platform capabilities for LLM and agentic workloads, including model access, prompt and configuration lifecycle, retrieval, tool integration, state management, and workflow orchestration. Build evaluation frameworks for non-deterministic AI systems, including offline benchmarks, regression testing, online quality signals, human feedback, and failure analysis. Establish observability, reliability, and governance for models and agents, covering traces, model and prompt versions, tool calls, latency, token usage, quality, safety, privacy, and cost. Help teams make principled architecture decisions across traditional ML, LLM-powered applications, and agentic workflows, and contribute to the platform’s technical roadmap. Build distributed training, batch inference, and large-scale processing systems using

Senior Machine Learning Engineer Description - We are looking for a Senior MLOps Engineer to design, build, and operate the infrastructure that enables machine learning models and large language models to be deployed safely, reliably, and at scale. In this role, you will create the end-to-end capabilities required to move models from experimentation into production, expose them through secure and highly available endpoints, and enable users and applications to interact with AI-powered services. You will work across AWS and Databricks to establish robust CI/CD pipelines, model-serving infrastructure, observability, governance, rollback mechanisms, and operational standards. You will partner closely with data scientists, machine learning engineers, software engineers, security teams, and platform engineers. The ideal candidate combines strong cloud and DevOps engineering skills with a practical understanding of machine learning systems, LLM deployment patterns, and production reliability. Key Responsibilities MLOps Platform and Architecture Design and implement a scalable MLOps platform using AWS and Databricks. Define reference architectures and reusable deployment patterns for traditional machine learning models, deep learning models, and large language models. Build standardized workflows that move models from development and validation into staging and production. Develop self-service capabilities that allow data scientists and ML engineers to deploy models without manually managing infrastructure. Establish clear separation between development, testing, staging, and production environments. Design multi-region or multi-availability-zone architectures where required by business continuity and availability objectives. CI/CD and

P-1504 The Applied AI team at Databricks sits at the forefront of advancing GenAI-powered products. Over the past years, we’ve launched Databricks Assistant , AI/BI Genie , and Agent Bricks working with product teams, and made significant strides in LLM quality for these products. These products are used by 100s of thousands of Databricks users every day. We are tackling challenging problems like code suggestion, error detection and correction, text-to-sql generation, automatic pipeline generation, knowledge QA and many others. As our GenAI products continue to evolve, we are seeking multiple GenAI Engineers from junior levels to more senior levels to drive the next phase of development. In 2025, we will focus on enhancing LLM quality, expanding GenAI capabilities across Databricks products, and strengthening our platform architecture to enable seamless AI interactions at scale. Key Responsibilities Shape the direction of our applied AI areas and intelligence features in our products . Drive the development and deployment of state-of-the-art AI models and systems that directly impact the capabilities and performance of Databricks' products and services (e.g., Databricks Assistant and AI/BI Genie). Develop novel data collection, fine-tuning, and LLM technologies that achieve optimal performance on specific tasks and domains. Design and implement ML pipelines for data preprocessing, feature engineering, model training, hyperparameter tuning, and model evaluation, enabling rapid experimentation and iteration. Work closely with cross-functional teams, including AI researchers, ML engineers, and product teams, to deliver impactful AI solutions that enhance user productivity and satisfaction. Build scalable, reusable backend systems to support GenAI products across the company. Develop robust logging, telemetry, and evaluation harnesses to ensure reliable model performance. What We’re Looking For 2-8 years of machine learning engineering expe