Production Scheduler Enhancement Engineer Jobs

(P-1490) Databricks processes petabytes of data and billions of transaction events daily - every cluster launch, every query executed, every dollar billed flows through infrastructure that must never fail. When we process billions in billing transactions with 99.999% accuracy requirements, when we ingest terabytes per second across 100+ regions, when a five-minute outage costs millions in revenue and customer trust - infrastructure isn't just important, it's existential. The next phase of our growth demands disaster recovery systems that prove reliability rather than hope for it, testing frameworks that catch production-scale problems before deployment, correctness guarantees that make billing errors structurally impossible, and automation that scales operations sublinearly with growth. In this leadership opportunity, you will build the data infrastructure organization that makes Databricks' continued growth possible. You'll establish foundational teams in Bengaluru owning the bedrock systems that guarantee billing correctness, operational resilience, and zero-downtime recovery across our entire monetization stack, alongside multi-region data ingestion, developer platforms, and deployment automation that eliminate friction at petabyte scale. This isn't about maintaining what exists; it's about architecting the infrastructure that enables Databricks to scale while reducing operational burden. You'll define what world-class infrastructure looks like for the next decade of data platforms. You will pursue these challenges as a founding technical leader in our fastest-growing engineering hub and strategic partner to global infrastructure leaders. In addition to building world-class teams, you will shape architectural decisions that ripple across the company and champion infrastructure-as-product thinking that transforms infrastructure into force multipliers globally. You'll work in an engineering culture born from Apache Spark and open source, where technical depth matte

AI Engineer Role Overview: As an AI Engineer at Particle41 you will design, develop and deploy machine-learning and deep-learning models/solutions (including generative-AI) that address complex business problems. You will collaborate with data scientists, data engineers, software engineers and client stakeholders. You will also build scalable pipelines, ensure production readiness, monitor performance and iterate proactively. In This Role, You Will: Lead end-to-end development of AI/ML models: from data ingestion & preprocessing to model training, evaluation, deployment and monitoring. Build generative-AI solutions (RAG, Agentic Workflows, MCP Servers, Conversation AI Agents) aligned with business goals. Work closely with data engineering teams to build/maintain data pipelines, feature stores and orchestration frameworks. Develop and integrate AI models into production systems (APIs, microservices, cloud deployments). Optimize models/solutions for performance, scalability, robustness and cost-efficiency. Monitor models/ solutions’ performance in production (drift, bias, fairness, reliability) and implement remediation. Document model design, experiments, data provenance and solution rationale. Stay abreast of latest AI/ML research, frameworks, tools (e.g. LangChain, LangGraph, MCP Clients/Servers, Agents SDKs, LiveKit etc.) and propose innovative ideas. Work directly with clients to understand problem statements, translate business requirements into AI solutions, and clearly communicate results. Skills and Experience We Value: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field. 3+ years of hands-on experience in AI/ML model development and deployment. Strong programming skills in Python and experience with major ML/AI frameworks (TensorFlow, PyTorch, scikit-learn, LangChain, LangGraph). Experience with vector databases (e.g., Pinecone, FAISS), RAG and Agentic workflows. Experience building or fine-tuning Large Language

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role As a software engineer on the Scaling team, you’ll help build and optimize the low-level stack that orchestrates computation and data movement across OpenAI’s supercomputing clusters. Your work will involve designing high-performance runtimes, building custom kernels, contributing to compiler infrastructure, and developing scalable simulation systems to validate and optimize distributed training workloads. You will work at the intersection of systems programming, ML infrastructure, and high-performance computing, helping to create both ergonomic developer APIs and highly efficient runtime systems. This means balancing ease of use and introspection with the need for stability and performance on our evolving hardware fleet. This role is based in San Francisco, CA, with a hybrid work model (3 days/week in-office). Relocation assistance is available. In this role, you will: Design and build APIs and runtime components to orchestrate computation and data movement across heterogeneous ML workloads. Contribute to compiler infrastructure, including the development of optimizations and compiler passes to support evolving hardware. Engineer and optimize compute and data kernels, ensuring correctness, high performance, and portability across simulation and production environments. Profile and optimize system bottlenecks, especially around I/O, memory hierarchy, and interconnects, at both local and distributed scales. Develop simulation infrastructure to validate runtime b

About Scale Scale's mission is to develop reliable AI systems for the world's most important decisions. As the leading AI data foundry, we provide the high-quality data and full-stack technologies that power the world's most advanced models — fueling breakthroughs in generative AI, defense, and autonomous vehicles. We partner with leading enterprises and governments to bring AI into production that performs when it matters most, combining rigorous evaluation with full-stack deployment so our customers can build AI they can trust. About the Team Applied Intelligence Systems is Scale's team focused on pushing the frontier of what agentic applications can do. We build the infrastructure and tooling that power agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale enterprises and governments demand. We're growing fast, with increasing traction across both commercial and public sector customers, and we're just getting started — this team will define what dependable, production-grade agentic AI looks like. About the Role As Engineering Manager for Agent Oversight, you'll lead the team building our platform for monitoring, evaluating, and improving agentic applications across enterprise and government customers. Agent development tooling is still nascent industry-wide, and we're building the platform to close that gap — covering deployment, monitoring, evaluation, ML-driven improvement, and continuous learning, so agents get better over time. You'll manage the engineering team, drive technical delivery, and work cross-functionally with customers and internal partners to bring these capabilities to production, with the ambition of setting the industry standard for how agentic applications are monitored and improved. Our platform already powers agent deployments for multiple enterprise and government customers. This is a US-based team with members across New York and San Francisco, and yo

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 Seer runs agents against real production problems for Sentry's customers. That means long-running jobs, lots of tool calls, messy real-world data, and a cost bill that matters. We need a Senior engineer to build the platform those agents run on, so product teams can ship new AI features without rebuilding the plumbing every time. In this role you will Build state-of-the-art agentic AI platforms to triage, debug, and solve real production issues Make every run debuggable: traces, cost and latency per run, and enough context to explain a bad answer Build eval and rollout tooling so we can swap models without guessing Keep it flexible enough that next quarter's model or protocol is an upgrade, not a rewrite Leverage Sentry’s novel (and massive) dataset of errors, spans, and profiles Own major AI initiatives, building alongside developers and product teams You'll love this job if you Are driven by impact and enjoy working on high-stakes, high-visibility projects Enjoy building things. You will have the opportunity to join the AI/ML team as one of its foundational members Thrive in cross-functional teams and enjoy building features alongside developers and product teams Qualifications You have 5+ years building production systems, including LLM-backed products, and can point to one people relied on You write solid production Python and TypeScript You have good distributed-systems instincts: timeouts, idempotency, failure modes Model training experience is a plus, not a requirement The base salary range (or hourly wage range, if applicable) that Sentry reasonably expects to pay for this position is $155,000 to $400,

En tant que Directeur Technique Généraliste de Contenu sur l'un de nos projets à Highdive Games Montréal, vous jouerez un rôle essentiel dans la direction de notre équipe technique locale centrée sur les données de Codev afin d'offrir des expériences de jeu de haute qualité. Grâce à votre vaste expérience dans le développement de jeux, en particulier avec Unreal Engine, vous fournirez des conseils techniques, du mentorat et du leadership à notre talentueuse équipe de développeurs. Vous veillerez à ce que nos équipes locales appliquent correctement les directives techniques et de production spécifiques aux données, à ce qu'elles surmontent les difficultés et à ce que notre projet soit mené à bien. Principales responsabilités : 1. Leadership et mentorat : Assurer un leadership fort à l'équipe technique, en l'inspirant et en la motivant pour qu'elle atteigne l'excellence. Encadrer et coacher les membres de l'équipe, en favorisant leur croissance et leur développement. Travailler et communiquer étroitement avec l'équipe technique principale du contenu située en Chine. Apporter également son soutien au mentorat et au partage des connaissances, le cas échéant. Cultiver un environnement de travail positif et collaboratif où la créativité se développe. 2. Direction technique : Contrôler en permanence la cohérence (qualité) des données que nous construisons côté codev, guider nos équipes techniques et de production locales. Assurer la faisabilité technique et l'évolutivité des exigences du projet. 3. Collaboration et communication : Collaborer étroitement avec les équipes interfonctionnelles, y compris l'art, la conception et la production, afin d'assurer l'alignement et l'intégration des solutions techniques et de l'exécution dans les données en cours de construction. Communiquer efficacement avec les parties prenantes pour les tenir informées des progrès techniques, des défis et des solutions. Servir de pont entre les équipes techniques et non techniques, en facilita

About the Team: OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role OpenAI is developing custom silicon to power the next generation of frontier AI models. We’re looking for experienced Design Verification (DV) Engineers to ensure functional correctness and robust design for our cutting-edge ML accelerators. You will play a key role in verifying complex hardware systems—ranging from individual IP blocks to subsystems and full SoC—working closely with architecture, RTL, software, and systems teams to deliver reliable silicon at scale. In this role you will: Own the verification of one or more of: custom IP blocks, subsystems (compute, interconnect, memory, etc.), or full-chip SoC-level functionality. Define verification plans based on architecture and microarchitecture specs. Develop constrained-random, directed, and system-level testbenches using SystemVerilog/UVM or equivalent methodologies. Build and maintain stimulus generators, checkers, monitors, and scoreboards to ensure high coverage and correctness. Drive bug triage, root cause analysis, and work closely with design teams on resolution. Contribute to regression infrastructure, coverage analysis, and closure for both block- and top-level environments. You might thrive in this role if you have: BS/MS in EE/CE/CS or equivalent with 3+ years of experience in hardware verification. Proven success verifying complex IP or SoC designs in industry-standard flows Proficient in SystemVerilog, UVM, and common simulation and debug tools (e.g., VCS, Questa, Verdi). Strong knowledge

We built Bubble with a clear mission: to empower everyone to create software. Our AI visual development platform lets anyone, from first-time entrepreneurs to enterprise teams, take an idea from prompt to fully-functional, scalable app across web, iOS, and Android. With over 6 million users in more than 100 countries, Bubble is breaking down the barriers to entrepreneurship and innovation worldwide. Our Product Bubble is the only fully visual AI app builder that lets you vibe code without the code to go beyond prototypes and launch real apps to real users. Chat with AI when you want speed, edit directly when you want control. Bubble's visual editor lets you fine-tune any detail, from the design to privacy rules and programming logic, so you're never stuck, even if AI hits its limits. Everything you need comes built in: a unified web and native mobile editor, enterprise-grade hosting, security, database management, and automatic scaling that grows with your business. You can build just about anything on Bubble, and our community is living proof. Mailead grew a $10K investment into a $2M valuation, and Faceless.video went from zero to $1M+ ARR in under a year. People aren't just launching products on Bubble, they're building real businesses. See how Bubble builders are shipping apps that change industries, solve problems, and shape the future here: Inspiring builders, breakthrough apps . Why Join Bubble Now? The rise of AI-generated software has validated everything Bubble has been building toward for over a decade. But pure AI-generated code is fragile, hard to debug, and rarely production-ready. Bubble bridges that gap, combining the speed of AI with a structured visual platform that produces stable, scalable, secure software. The people who join Bubble right now will help define what that means for millions of builders around the world. If you've ever wanted to work on something that genuinely changes who gets to build, this is your moment. About the Team: We’re ex

EviSmart Senior Full Stack Developer "Most developer roles put you one layer from the product. This one puts you at the build layer of a platform that 2,000+ dental labs depend on — and your code ships to 28 countries." 📍 BGC, Taguig City, Philippines • On-site • Full-Time WHAT EVISMART IS BUILDING EviSmart is the leading dental Autopilot system operating across 28 countries in a $40B healthcare industry — and the Manila team builds and ships it. Not an MVP. Not a pilot. Live, in production, with thousands of dental labs and dentists depending on the platform every day to run their business. We have multiple in-house AI teams building the next generation of dental design automation — and Manila is where it gets operationalized. We are one of the few genuinely AI-first companies in the Philippines. The models are being built here. The product is scaling here. The opportunity is here. WHY THIS ROLE, WHY NOW The AI layer for the dental industry has not been built yet. The product is proven, but the sprint team building it is growing — and this hire is the build layer that makes the rest of the pipeline work. The person who joins now as Full Stack Developer doesn't inherit a finished queue. You will be the engineer who sets the standard for what "done" looks like in a sprint — building the features that internal operations teams and 2,000+ dental labs depend on daily, and doing it to the quality gate, sprint after sprint. The habits and practices you establish here become the baseline for how the engineering team scales. Early joiners shape the standard. That is not a slogan — it is how every senior role here got built. WHY EVISMART Leading dental Autopilot system in a $40B global healthcare industry — operating across 28 countries, live in production Multiple in-house AI teams building proprietary models — not just consuming APIs. Claude, Cursor, Luvable, and LLM-powered workflows are part of the daily stack Manila is not a back office. It is the

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Reinforcement learning (RL) is how Claude learns to reason, write code, and act autonomously over long horizons. This role sits on the team that builds and owns the RL training system: the system that trains our production models and that researchers across Anthropic run their experiments on. The team works closely with research teams across the company on the science and engineering of making RL work at scale. As a Research Engineer on the team, you'll work at the center of RL at Anthropic. You'll have a direct view of how RL training behaves at the frontier because the system you own sits underneath both production and research runs. You will use it with collaborators across research teams to understand what is working, what is fragile, and where the next improvements are. Key responsibilities Build, own, and improve the core RL training system that serves Anthropic's production and research runs Work across the stack (orchestration, environments, training, inference, evaluation) wherever the system needs it Study how RL training behaves at scale and contribute to the research that improves it, in collaboration with teams across Anthropic Implement new training methods as stable, fast, well-tested code Improve the speed and efficiency of RL training and evaluation through profiling, optimization, and benchmarking Make the system easier for researchers to build on, through clean abstractions, clear APIs, and automated testing Debug hard problems across the stack, from a run that has quietly drifted to a distributed systems failure that only shows up at scale Communicate results clearly, in writing and in discussion Min

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: Baseten’s Model Performance (MP) team is responsible for ensuring the models running on our platform are fast, reliable, and cost‑efficient. As part of this team, you’ll focus on Model APIs — the infrastructure powering our hosted API endpoints for the latest open‑source models. This work spans distributed systems, model serving, and developer experience. You’ll join a small, high‑impact team operating at the intersection of product, model performance, and infra, helping to define how developers interact with AI models at scale. RESPONSIBILITIES: Design, build, and operate the Model APIs surface with focus on advanced inference capabilities: structured outputs (JSON mode, grammar-constrained generation), tool/function calling and multi-modal serving Profile and optimize TensorRT-LLM kernels, analyze CUDA kernel performance, implement custom CUDA operators, tune memory allocation patterns for maximum throughput and optimize communication patterns across multi-GPU setups Productionize performance improvements across runtimes with deep understanding of their internals: speculative decoding implementations, guided generation for structured outputs, custom scheduling and routing algorithms for high-performance serving Build comprehensive benchmarking frameworks that measure real-world performance across different model architectures, batch sizes, sequence lengths, and hardware configurations Productionize performa

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 ship AI products. THE ROLE As a Cloud Platform Engineer, you'll envision and build robust systems and processes that ensure our infrastructure is scalable, reliable, and efficient. This can range from automating deployments and monitoring systems to optimizing performance and managing incidents. We all work closely with our users, learning from their past struggles in operationalizing ML, onboarding them onto our platform, and turning our learnings into ideas for improving Baseten. EXAMPLE INITIATIVES You'll get to work on these types of projects as part of our Infrastructure team: Multi-cloud capacity management Inference on B200 GPUs Multi-node inference Fractional H100 GPUs for efficient model serving RESPONSIBILITIES Build and maintain scalable infrastructure to support the deployment and operation of machine learning models. Establish standards and best practices for reliability and performance across the infrastructure. Automate processes when relevant, particularly for managing CI/CD pipelines. Own products and projects end-to-end, functioning as both an engineer and a project manager, with a focus on user empathy, project specification, and end-to-end execution. Collaborate with cross-functional teams to understand project requirements and translate them into technical solutions. Mentor junior team members and contribute to knowledge sharing within the organization. Navigate ambiguity and exercise good judgment on tradeoffs and to

P-1285 About This Role As a staff software engineer for GenAI inference, you will lead the architecture, development, and optimization of the inference engine that powers Databricks Foundation Model API.. You’ll bridge research advances and production demands, ensuring high throughput, low latency, and robust scaling. Your work will encompass the full GenAI inference stack: kernels, runtimes, orchestration, memory, and integration with frameworks and orchestration systems. What You Will Do Own and drive the architecture, design, and implementation of the inference engine, and collaborate on model-serving stack optimized for large-scale LLMs inference Partner closely with researchers to bring new model architectures or features (sparsity, activation compression, mixture-of-experts) into the engine Lead the end-to-end optimization for latency, throughput, memory efficiency, and hardware utilization across GPUs, and accelerators Define and guide standards to build and maintain instrumentation, profiling, and tracing tooling to uncover bottlenecks and guide optimizations Architect scalable routing, batching, scheduling, memory management, and dynamic loading mechanisms for inference workloads Ensure reliability, reproducibility, and fault tolerance in the inference pipelines, including A/B launches, rollback, and model versioning Collaborate cross-functionally on Integrating with federated, distributed inference infrastructure – orchestrate across nodes, balance load, handle communication overhead Drive cross-team collaboration: with platform engineers, cloud infrastructure, and security/compliance teams Represent the team externally through benchmarks, whitepapers, and open-source contributions What We Look For BS/MS/PhD in Computer Science, or a related field Strong software engineering background (6+ years or equivalent) in performance-critical systems Proven track record of owning complex system components and driving architectural decisions end-to-end Deep understa

About the Role As a Research Engineer at Metis, you’ll work on building the next generation of autonomous post-training systems that leverage our Mantis platform. You’ll operate at the intersection of cutting-edge ML research and scalable engineering, designing, implementing, and deploying algorithms that improve how AI agents learn from feedback, synthetic data, and real-world interactions. You’ll move seamlessly between papers and production, leading large-scale experiments, creating optimized training pipelines, and helping shape the future of post-training autonomy. You’ll have significant ownership, high compute budgets, and the mandate to push the state of the art in applied reinforcement and preference optimization. What You'll Do Research and help build an autonomous post-training agent leveraging the Mantis platform Design and execute large-scale experiments on synthetic data generation and algorithmic architecture Develop and refine methods for reinforcement learning, reward modeling, and human feedback integration Collaborate cross-functionally with Core and Platform Engineering to deploy and evaluate models in production settings Publish or contribute to leading-edge research in the post-training domain Use tooling and compute efficiently to iterate on experimental pipelines and accelerate research velocity Requirements Deep experience in machine learning, preferably reinforcement learning, post-training, or alignment research Demonstrated research contributions; ideally published papers (ICML, NeurIPS) or public implementations Strong proficiency in Python and ML frameworks (PyTorch, JAX, or TensorFlow) Comfort with distributed training, high-throughput data pipelines, and large-scale experiment management Ability to reason independently, formulate hypotheses, and run experiments from idea → insight → product impact Compensation & Benefits Base: $200,000–$1,000,000 Significant Equity Full medical, dental, and vision Wellness & L&D stipend Eq

About the Team Security is at the foundation of OpenAI’s mission to ensure that artificial general intelligence benefits all of humanity. The Security team protects OpenAI’s technology, people, and products. We are technical in what we build but are operational in how we do our work, and are committed to supporting all products and research at OpenAI. Our Security team tenets include: prioritizing for impact, enabling researchers, preparing for future transformative technologies, and engaging a robust security culture. About the Role OpenAI is seeking a Security Engineer to join our Infrastructure Security (InfraSec) team. InfraSec protects the foundations of OpenAI’s research and production environments, spanning GPU supercomputing clusters, multi-cloud infrastructure, datacenters, networking, storage, and the critical services that power our frontier AI models. Our charter includes securing everything from bare-metal hardware and firmware, to Kubernetes clusters and service meshes, to data storage and access pathways for highly sensitive model weights and user data. In this role, you will: Design and build security controls across diverse layers (e.g., physical hardware, firmware/BMC, OS, Kubernetes, networks, and CI/CD) to defend against sophisticated adversaries and insider threats. Collaborate with engineering and security teams to drive deployment of security enhancements and control changes across broad-scale infrastructure. Tackle high-impact projects such as checkpoint encryption, network isolation, secret management, and machine identity, while continuously raising the security bar for emerging AI workloads. Take a generalist approach to building security controls, balancing a mix of security expertise and broad technical skillsets to adapt to evolving challenges. You will thrive in this role if you have: Deep understanding of security principles, best practices, and common vulnerabilities. A proactive mindset, with the ability to identify and address secu