Model Behavior Engineer Jobs

AI only answers correctly when it can trust the data underneath it. This role owns two connected parts of how Sigma shows up in that world: where Sigma's experience lives outside its own product (MCP, a CLI, the Claude and ChatGPT marketplaces, integrations like Slack, Teams, and Glean), and the semantic layer that makes every one of those surfaces trustworthy. The first mandate is Sigma's AI ecosystem: defining Sigma's approach to MCP, giving external agents structured, governed access to Sigma's data model; owning the CLI, giving developers a fast way to work with Sigma outside the UI; and building Sigma's presence in the Claude and ChatGPT marketplaces, plus integrations for Slack, Teams, Glean, and similar surfaces, so people can reach Sigma's data wherever they already work. This is some of the most visible, fastest-growing surface area in the product. The second mandate is the semantic layer underneath it all. Every agent, chat answer, and integration is only as reliable as the data model behind it — get a metric definition wrong here, and every surface built on top inherits the mistake. This includes setting the roadmap for how semantic views connect across data platforms and how the model evolves as new AI capabilities emerge. What you'll do Define Sigma's approach to MCP, giving external agents and tools structured, governed access to Sigma's data model. Own the CLI roadmap, giving developers a fast, scriptable way to work with Sigma outside the UI. Build Sigma's presence in the Claude and ChatGPT marketplaces, along with integrations for Slack, Teams, Glean, and similar surfaces, so people can reach Sigma's data wherever they're already working. Set the roadmap for Sigma's data modeling strategy, including how semantic views connect across data platforms and how the semantic layer evolves as new AI capabilities emerge. Partner with engineering and design to ship integration and semantic modeling capabilities that hold up at enterprise scale.

Location : Hamburg or Berlin! At Freenow by Lyft, we are on a mission to empower smarter mobility decisions, helping people to move freely and cities to thrive. We are looking for a Senior Commercial Finance Manager to drive financial planning, business casing, and performance tracking across our dynamic growth segments and key strategic initiatives. In this role, you will act as a core financial business partner to the segment leaders and business owners accountable for driving these growth levers, providing the analytical rigor needed to guide investment decisions and strategy. Your scope extends beyond our core marketplace model to encompass B2B SaaS solutions and other business models. Beyond commercial FP&A, you will play an active role in M&A activities by supporting buy-side due diligence and post-acquisition financial integration. YOUR DAILY ADVENTURES WILL INCLUDE: Lead structured financial modeling and business casing for growth segments and strategic projects in close collaboration with business owners. Act as a key finance partner to segment leaders and business owners, providing objective analysis to inform senior leadership discussions on resource allocation and major investment decisions. Establish continuous ROI tracking frameworks and conduct routine performance reviews to ensure capital efficiency across all growth levers. Work alongside the Data team to define and implement consistent KPI tracking, reporting frameworks, and performance dashboards. Collaborate with the broader Commercial Finance team to build and maintain sustainable "shadow P&L" reporting for growth segments (prospectively utilizing Anaplan). Support buy-side M&A projects by assessing target financials and evaluating business models within the context of our own operational data to complement external due diligence advisory work. Structure and drive the post-acquisition financial integration of newly acquired businesses from an FP&A perspective, ensuring

About the Role REMOTE IN INDIA We're looking for a software engineer to build the Kubernetes-native control plane that provisions and runs our GPU inference fleet. You'll design a manifest-driven API where the inference team declares what they need, whether that's a cluster, a model deployment, or a capacity change, and our controllers handle the reconciliation, provider/runtime selection, and lifecycle management underneath, so the inference team never has to know or care which specific serving stack, scheduler, or hardware pool is doing the work. You'll also build the systems that keep the fleet efficient, not just running, including defragmentation and rebalancing logic that consolidates scattered workloads back into contiguous capacity, and scheduling/bin-packing improvements that push GPU utilization up without hurting latency. The core value we're after is decoupling the people building on top of the platform from the operational and runtime complexity underneath, while squeezing more usable capacity out of the same hardware. You'll build the controllers, reconciliation loops, and self-service surface (API/CLI, not tickets) that make that decoupling real, plus the event-driven health, remediation, and utilization systems that keep it running and efficient without a human in the loop. Strong candidates have hands-on experience with Kubernetes controller/CRD patterns, have built or operated a platform API that abstracts multiple backends behind one interface, understand GPU scheduling and capacity efficiency (fragmentation, bin-packing, right-sizing), and think about GPU infrastructure as software to be engineered. A product mindset - you've built internal platforms or APIs consumed by other engineering teams and care about the developer experience of what you ship. You build it, you own it. You are not only responsible for delivering the software but also for operating and supporting it in production. Responsibilities Build the provisioning state machine

ABOUT THIS ROLE GHX connects healthcare suppliers and hospitals across North America, processing over 2.2 million Purchase Order (PO) documents per year through its ADM (Automated Document Management) platform. The platform processes the majority of documents automatically; documents the system cannot process with sufficient confidence are routed to a human review queue. The Document Review Specialist verifies those flagged documents and corrects extraction errors at the field level. Every correction feeds directly into model retraining — you are not maintaining a status quo, you are actively improving the platform’s accuracy over time. This is a precision operations role, not conventional data entry. KEY RESPONSIBILITIES Document Verification & Field Correction — 95% Verify documents like purchase orders, invoices, etc. flagged by the platform as requiring validation Compare extracted field values against the source document image and correct every discrepancy at the field level Verify and correct core fields, including vendor name, PO/Invoice number, line item descriptions, quantities, unit prices, delivery dates, and ship-to addresses Sustain throughput of ~30 documents per hour at a field accuracy rate of 99% or above Self-audit daily output before shift-end submission to catch and correct errors before they enter the system Flag ambiguous, damaged, or edge-case documents per the team escalation protocol; do not attempt to resolve documents outside defined parameters Calibration & Continuous Improvement — 5% Participate in team calibration sessions led by the Supervisor to maintain consistent field interpretation standards Surface recurring error patterns to the Supervisor (e.g., consistent misread of a specific supplier’s PO template) through the team escalation channel Complete structured onboarding and participate in refresher sessions as document types and field standards evolve REQUIRED QUALIFICATIONS Bachelor’s degree in any discipline 1–3 years of

ABOUT THIS ROLE GHX connects healthcare suppliers and hospitals across North America, processing over 2.2 million Purchase Order (PO) documents per year through its ADM (Automated Document Management) platform. The platform processes the majority of documents automatically; documents the system cannot process with sufficient confidence are routed to a human review queue. The Document Review Specialist verifies those flagged documents and corrects extraction errors at the field level. Every correction feeds directly into model retraining — you are not maintaining a status quo, you are actively improving the platform’s accuracy over time. This is a precision operations role, not conventional data entry. KEY RESPONSIBILITIES Document Verification & Field Correction — 85% Verify documents like purchase orders, invoices, etc. flagged by the platform as requiring validation Compare extracted field values against the source document image and correct every discrepancy at the field level Verify and correct core fields, including vendor name, PO/Invoice number, line item descriptions, quantities, unit prices, delivery dates, and ship-to addresses Sustain throughput of ~30 documents per hour at a field accuracy rate of 99% or above Self-audit daily output before shift-end submission to catch and correct errors before they enter the system Flag ambiguous, damaged, or edge-case documents per the team escalation protocol; do not attempt to resolve documents outside defined parameters Process Documentation & Reporting — 10% Create and maintain Standard Operating Procedures (SOPs) for document review workflows, and update them as document types and field standards change Build and maintain process maps for end-to-end document review flows, including exception and escalation paths Perform basic analytics — collate production and quality data, and pull routine and ad-hoc reports on throughput, accuracy, and error trends as required Calibration & Continuous Improvement —

Description: Graviton is a privately funded quantitative trading firm striving for excellence in financial markets' research. We are seeking a Quantitative Researcher for our team in Gurgaon. This team trades across a multitude of asset classes and trading venues using a gamut of concepts and techniques ranging from time series analysis, filtering, classification, stochastic models, pattern recognition to statistical inference analysing terabytes of data to come up with ideas to identify pricing anomalies in financial markets. Responsibilities: Develop new or improve existing trading models using in-house platforms Use advanced mathematical techniques to model and predict market movements Analyse large financial datasets to identify trading opportunities Provide real time analytical support to experienced traders Requirements: Possess a degree in a highly analytical field, such as Engineering, Mathematics, Computer Science from IITs schools Quantitative bent of mind A working knowledge of Linux/Unix Programming experience, preferably in C++ or C No prior knowledge of financial markets is needed but must have a strong interest in learning about financial markets. Have a strong work ethic Hard Working Benefits: Our open and collaborative work culture gives you the freedom to innovate and experiment. Our cubicle free offices, non-hierarchical work culture and insistence to hire the very best creates a melting pot for great ideas and technological innovations. Everyone on the team is approachable, there is nothing better than working with friends! Our perks have you covered. Competitive compensation Annual international team outing Fully covered commuting expenses Best-in-class health insurance Delightful catered breakfasts and lunches A well-stocked kitchen 4 week annual leaves along with market holidays Gym and sports club memberships Regular social events and clubs After work parties

AI/ML – Investment Services A Career with Point72's AI/ML – Investment Services Team The AI/ML – Investment Services team at Point72 spearheads the development of cutting-edge AI solutions that seek to transform our business processes and enhance enterprise intelligence. The team aims to bridge the gap between business challenges and technological innovation, collaborating with stakeholders across the firm and leveraging expertise in generative AI, data engineering, and machine learning. WHAT YOU'LL DO Build and scale core backend services and platforms that power generative AI applications and data infrastructure used across the firm’s investment workflows Design and implement high-throughput, low-latency data pipelines to ingest, normalize, and serve both structured and unstructured data Develop robust APIs and microservices to support model inference, feature serving, and downstream applications Integrate generative AI tools and model-serving workflows into production, including embedding stores, retrieval components, and fine-tuning pipelines Optimize system performance, cost, and reliability through profiling, capacity planning, and architectural improvements Implement automated testing, continuous delivery pipelines, monitoring, and incident response practices to maintain production health Partner with data scientists, AI engineers, product owners, and operations to translate models and prototypes into scalable, production-grade solutions Mentor engineers, lead code reviews, and establish engineering best practices for maintainability, security, and observability Own end-to-end delivery, operational runbooks, and metrics-driven measurement of feature impact and system reliability WHAT'S REQUIRED Bachelor’s degree in computer science, software engineering, or a related technical field Minimum 5+ years of professional experience building backend systems and production services Demonstrated experience designing and operating large-scale data engineering pipelines

Position Overview: We are excited to introduce a newly created Senior HR Business Partner role, partnering directly with the Senior Leadership Team (SLT) to drive organizational effectiveness, leadership capability, and strategic workforce planning across the business. Central to the role is holding senior leaders accountable for their own growth as leaders: coaching them with the same rigor they are expected to apply to their teams, so that as leaders level up, their team’s level up alongside them. You will help drive effective people practices by balancing business priorities with employee experience and will play a key role in evolving Diligent’s HR operating model toward more strategic, AI-enabled ways of working. The ideal candidate is collaborative, business-minded, and solutions-oriented, with strong judgment, communication skills, and the ability to build trusted relationships with senior stakeholders – while being direct enough to hold them to account. Key Responsibilities Serve as a senior strategic HR partner to the Senior Leadership Team (SLT) for an assigned business unit or regional pod, leading org design, talent planning, succession planning, and coaching. Develop the people strategy for the business unit or function you support, in partnership with senior leadership, translating business strategy and objectives into a people plan that delivers maximum impact. Lead strategic workforce planning for the business unit — including how work is organised across people and AI agents — and navigating the balance between building AI capability and developing the human skills needed for the future in an AI-first organisation. Partner with the HR Director on implementation and functional roll-out of the broader people strategy. Lead the business unit’s talent and performance cycles, including goal-setting/OKR cadence, SLT-level calibrations, compensation review for the SLT-and-below population, and 9-box grid review. Drive turning workforce, engageme

About Scale AI At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. Scale Frontier Data is the organization behind the training and evaluation data that frontier labs depend on. We build the systems, tooling, and expert workflows that turn hard human expertise into signals that models can learn from, across reasoning, coding, agentic tool use, and domain expertise. Reinforcement learning environments are now the center of gravity for that work: the difference between a model that demos well and a model that reliably completes long-horizon work is almost always the quality of the environments and reward signals it was trained against. Responsibilities As a Staff Software Engineer, RL Environments, you'll own the technical foundation for how Scale builds, runs, verifies, and delivers RL environments at scale. An RL environment is a real piece of software: a containerized world with real dependencies, real state, real tools, and a grader that has to be correct even when the agent is creative about breaking it. Building one is a full-stack engineering problem. Building thousands of them reproducibly, cheaply, with trustworthy reward signals and throughput measured in millions of rollouts is a systems problem that very few people have solved. You'll work on both. You'll design the platform: sandboxed execution, environment packaging and versioning, rollout orchestration, trajectory capture, verifier frameworks, and the authoring surfaces that let engineers and domain experts produce environments without reinventing infrastructure each time. And you'll go deep on the environments themselves by instrumenting real applications, designing task suites that expose specific capability gaps, and building graders that

About Scale AI At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI) and building upon our prior model evaluation work with enterprise customers and governments to deepen our capabilities and offerings for public and private evaluations. About Data Engine Our Generative AI Data Engine powers the world’s most advanced LLMs and generative models through world-class RLHF (Reinforcement Learning with Human Feedback), human data generation, model evaluation, safety, and alignment. The data we produce is some of the most critical work for how humanity will interact with AI. About Our FDE Team Generating high-quality data is the core problem our business solves. We aim to make producing and delivering high-quality data seamless and efficient for operators and customers. Our Team is building customer and operator-specific infrastructure to provide high-quality data with low turnaround time. You'll be exposed to the cutting edge of the Generative AI industry while directly interfacing with the leading model-building organizations in the space, including the top AI research labs and government agencies. Join us in shaping the future of Artificial General Intelligence. As a Forward Deployed Engineer, you'll be at the forefront of providing the critical data infrastructure that powers the most advanced AI models, directly influencing how humanity interacts with AI. You will work with the world’s leading AI companies and government agencies to solve their most complex AI data-related problems. Responsibilities: Drive Impact: Directly contribute to the advancement of AI by delivering critical data solutions for leading AI innovators and

About Scale AI At Scale AI, our mission is to accelerate the development of AI applications. For 10 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI) and building upon our prior model evaluation work with enterprise customers and governments to deepen our capabilities and offerings for public and private evaluations. About Data Engine Our Generative AI Data Engine powers the world’s most advanced LLMs and generative models through world-class RLHF (Reinforcement Learning with Human Feedback), human data generation, model evaluation, safety, and alignment. The data we produce is some of the most critical work for how humanity will interact with AI. About Our FDE Team Generating high-quality data is the core problem our business solves. We aim to make producing and delivering high-quality data seamless and efficient for operators and customers. Our Team is building customer and operator-specific infrastructure to provide high-quality data with low turnaround time. You'll be exposed to the cutting edge of the Generative AI industry while directly interfacing with the leading model-building organizations in the space, including the top AI research labs and government agencies. Join us in shaping the future of Artificial General Intelligence. As a Forward Deployed Engineer, you'll be at the forefront of providing the critical data infrastructure that powers the most advanced AI models, directly influencing how humanity interacts with AI. You will work with the world’s leading AI companies and government agencies to solve their most complex AI data-related problems. Responsibilities: Drive Impact: Directly contribute to the advancement of AI by delivering critical data solutions for leading AI innovators an

Director of Engineering, Physical AI Role Overview The Director of Engineering will report to the General Manager of Physical AI, and will be responsible for leading a multi-disciplinary engineering organization. In this senior leadership role, you will own the execution of the Physical AI Data Engine — the platform powering the next generation of Physical AI/Embodied AI. You will collaborate closely with Operations and GTM to guide product direction and help solve the data bottleneck that stands between today's robotics research and real-world deployment. This role requires significant ownership in a fast-paced environment and you will motivate internal teams to set the pace for business growth. Travel will come into play. Key Responsibilities: Set and drive the technical vision across data collection infrastructure, teleoperation systems, ML training pipelines, model evaluation frameworks, annotation tooling, and research Lead a multidisciplinary engineering organization—spanning engineering managers, software engineers, ML engineers, and ML research scientists—while designing the organizational structure, talent strategy, and culture required to scale rapidly without compromising on quality or strategic alignment Maintain exceptional technical and operational excellence by deeply understanding team deliverables, asking incisive questions, identifying slipping standards early, and knowing precisely when to step in Drive cross-functional alignment across Engineering, Operations, and GTM on platform architecture, release processes, and shared priorities Collaborate with researchers and clients to architect and deliver scalable, production-grade data infrastructure tailored for complex robotics workloads Required Qualifications: Bachelor's degree in Engineering, Robotics, Computer Science, or a related technical field 8+ years of engineering experience in fast-paced environments, including 4+ years direct people management demonstrated history of recruiting, mentorin

At Scale, we believe that AI will dramatically improve the world, and our mission is to accelerate the development of AI. We’re looking for a Field Marketing and Events leader to continue to build and manage Scale’s robust Gen AI and Research field marketing and events program, including but not limited to Scale hosted executive events, Scale hosted practitioner events and meetups, research conferences, and our annual flagship conference, AI Leadership Summit. You will join a rapidly growing team with the opportunity to manage and execute events from start to finish, drive lead generation and pipeline growth, and plan event programming with the largest names in AI. The successful candidate will have a solid understanding of technology, model builder, and research markets, strong project management skills, a strategic mindset, ability to manage and grow a team, and a passion for AI & technology. You will: Own annual planning for all Gen AI and Research events including strategy and budget Establish event activities in line with sales goals and deal acceleration, prioritizing goals from the go-to-market leadership and business development teams on event location and audience Manage team of field marketing and events managers who will execute on planning and logistics. This role should have a player-coach mentality and ability to also execute on nuts to bolts planning for all executive dinners, meetups, happy hours, sponsored trade shows, and hosted conferences Manage contractor relationships including event production firms and outside vendors, and event budgets Align with growth marketing on marketing campaigns and marketing qualified lead (MQL) reporting Track event campaign performance, measuring ROI, results, and metrics through Salesforce and own any course correction for events that are not performing Own all aspects of Scale hosted conference, Scale AI Base Camp, including venue selection, vendor management, logistics, speaker

About Scale At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments, to deepen our capabilities and offerings for both public and private evaluations. About the ACE team The Agent Capabilities & Environments (ACE) team, part of Scale’s Research organization, brings together customer-facing Researchers and Applied AI Engineers. Our core mission includes research on agent environments and RL reward signals, benchmarking autonomous agent performance across real-world scenarios and environments, creating robust data programs to improve Large Language Models (LLMs) agentic capabilities and building foundational tools and frameworks for evaluating models as agents. ACE focuses on autonomous agents that dynamically interact with diverse external environments, including code repositories, GUI interfaces, browsers, and more. About This Role This role is at the intersection of cutting-edge AI research and practical application, with a focus on studying the data types essential for building state-of-the-art agents, such as browser and SWE agents. The ideal candidate will explore the data landscape needed to advance intelligent, adaptable AI agents, guiding the data strategy at Scale to drive innovation. This position requires not only expertise in LLM agents and planning algorithms but also creativity in addressing novel challenges related to data, interaction, and evaluation. You will contribute to impactful research publications on agents, collaborate with customer researchers, and work alongside the engineering team to translate t

About Scale At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments, to deepen our capabilities and offerings for both public and private evaluations. About Data Engine Our Generative AI Data Engine powers the world’s most advanced LLMs and generative models through world-class RLHF (Reinforcement Learning with Human Feedback), human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI. Our Approach As part of the interview process, you’ll be considered for opportunities across several teams within the GenAI Engineering organization, based on your interests, expertise, and business needs. Potential team placements include Allocation, Growth, Frontier Data, Trust & Safety, Pay, Operator, or Tasking Experience. Together, these teams power Scale’s AI data operations - from building high-impact datasets that push the boundaries of LLM capabilities, to optimizing contributor onboarding and incentives, to safeguarding data integrity through advanced trust, safety, and security measures. They work at the intersection of ML, operations, and analytics to ensure we deliver the highest-quality data at scale. Responsibilities: Design, build, and maintain robust, scalable systems across the full stack, including front-end, back-end, and infrastructure layers Implement high-impact features using modern technologies such as TypeScript, React, Node.js, MongoDB, Elasticsearch, and Temporal Collaborate closely with internal operators (your use