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we are looking for a Business Program Manager.
As a Program Manager, youll be focused on driving operational excellence, leading complex, cross functional initiatives in a dynamic environment and continuously improving productivity across our product portfolio.
You will own the delivery of product roadmap across all products. Make sure key initiatives are planned well, delivered on time, and released with high quality. Work with Product, R&D, Delivery, CS, Marketing, and Sales to track progress, remove blockers, and keep everyone aligned.
Were looking for someone with a can-do approach and great interpersonal skills - someone who thrives on collaboration, cuts through complexity, and keeps things moving in a fast-paced environment.
you will do:
Drive the planning, execution, and delivery of high-impact programs across multiple teams.
Partner with product, engineering, marketing, and customer success to align on priorities and timelines
Build and maintain an integrated delivery plan for all products and major initiatives (quarterly + rolling 8-12 weeks), including milestones, dependencies, owners, and delivery confidence
Develop and maintain program roadmaps, tracking milestones and key deliverables.
Identify inefficiencies and drive process improvements for speed, quality, and predictability.
Lead our release process, ensuring high‑quality releases on time and clear communication
Build a bug management model with clear severity definitions, SLAs, and forums
Create executive and org‑wide visibility with data

It starts with you - an AI Engineering Team Lead responsible for leading a team building production-grade agentic systems. This role combines hands-on technical leadership with people management, ownership of architecture, delivery, and quality across agents, MCP tools, agentic workflows, RAG systems, and evaluation.
If you want to grow your skills building AI products for mission-critical AI, join mission - this role is for you.
:Responsibilities
Lead and mentor a team of AI Engineers building agentic systems and MCP tools.
Own technical direction for agent architectures, workflows, and LLM evals.
Review designs and code; ensure production readiness, reliability, and security.
Drive adoption of frameworks such as LangChain, LangGraph, or equivalent.
Set standards for RAG pipelines, vector databases, and retrieval quality.
Partner with product and platform teams on roadmap, scope, and delivery.
Balance hands-on contribution with people management and execution.
Build a culture of strong engineering practices, measurement, and iteration.

We are on an expedition to find you, someone who is passionate about creating intuitive, out-of-this-world production-grade AI systems and ML pipelines to join our AI group. You'll be responsible for designing, building, deploying, and maintaining production-grade AI systems and ML pipelines. Youll work closely with data scientists to translate research into scalable solutions and manage model deployment in both cloud and on-prem GPU environments.
:Responsibilities
Design, build, and deploy production-grade ML models, AI agents, and end-to-end pipelines across cloud and on-prem GPU environments.
Maintain and optimize ML systems for performance, scalability and reliability, including model validation, inference speed, and resource efficiency.
Develop monitoring and observability tools such as alerts and performance metrics to ensure system stability in production.
Create and integrate APIs for ML services within microservice-based architectures.
Drive adoption of best practices for CI/CD, observability, and reproducibility in ML systems.

It starts with you - an engineer who cares about building data pipelines and models that deliver reliable, trusted data. You value data quality, clean transformations, and making data accessible to those who need it. Youll work alongside experienced engineers to build ETL/ELT pipelines, maintain dimensional models, and implement quality checks that turn raw data into actionable intelligence.
If you want to grow your skills building data products for mission-critical AI, join mission - this role is for you.
:Responsibilities
Build and maintain ETL/ELT pipelines using platform tooling - workflows that extract from sources, apply transformations, and load into analytical stores.
Develop and maintain data models - fact/dimension tables, aggregations, and views that serve analytics and ML use cases.
Implement data quality checks - validation rules, tests, and monitoring for data freshness and accuracy.
Maintain documentation and lineage - keeping data catalogs current and helping consumers understand data sources and transformations.
Work with stakeholders to understand data requirements and implement requested data products.
Troubleshoot pipeline failures - investigating issues, fixing bugs, and improving reliability.
Write clean, tested, well-documented SQL and Python code.
Collaborate with Data Platform on tooling needs; work with Datastores on database requirements; partner with ML, Data Science, Analytics, Engineering, and Product teams on data needs.
Design retrieval-friendly data artifacts - RAG-supporting views, feature tables, and embedding pipelines - with attention to freshness and governance expectations.

It starts with you - a technical leader whos passionate about database systems and growing high-performing teams. You care about query performance, uptime, data durability, and operational excellence. Youll lead the Datastores team in operating, tuning, and scaling the database engines that serve the platform - from PostgreSQL to Elasticsearch, Redis to vector databases, across cloud and on-prem environments.
If you want to lead a team that keeps the database engines running for mission-critical AI systems, join mission - this role is for you.
:Responsibilities
Lead and grow the Datastores team - hiring, mentoring, and developing engineers while fostering a culture of operational excellence.
Own database reliability and availability - ensuring systems meet demanding SLAs for government and national-scale customers.
Drive performance tuning and optimization - query analysis, index strategies, configuration tuning, and resource optimization across all database engines.
Establish operational practices - backup/recovery procedures, disaster recovery, replication strategies, and failover automation.
Plan and execute capacity management - monitoring growth, forecasting needs, and scaling databases ahead of demand.
Lead incident response for database issues - troubleshooting, resolution, and post-incident improvements.
Enable new capabilities - evaluating, deploying, and operating new database technologies including vector databases for AI workloads.
Partner with Data Platform, Data Engineering, Engineering, and Security teams to align database operations with platform needs.
Define and uphold database SLAs that support retrieval paths, feature stores, and embedding durability; coordinate on schema evolution, partitioning, and safe replay/backfills.
Integrate with catalog and lineage systems - surfacing ownership, change history, and impact analysis for critical datasets and collections.
Collaborate with Data Platform, Data Engineering, Engineering, Security, Product, AI/ML, Data Science, and Analytics to prioritize performance, durability, and evolution of data stores across workloads.

We are seeking a highly impactful individual who excels in individual contributions but also significantly elevates the team's overall performance. An Engineer with a can-do attitude who has what it takes to ship state of the art applications using cutting-edge technologies.
As an iOS Engineer, youll be joining a diverse team of professionals with some of the brightest, sharpest, and analytical minds.
You will have a huge impact on our products, taking full ownership of features E2E, and having a great influence inside the team, on both the rider and driver apps.
What you can expect from us:
You'll work with the latest technologies, including TCA, Swift UI, Swift Concurrency, and more
Work with a product team that maintains high UX/UI standards and a robust design system, making it easy to build elegant, consistent, and user-friendly features
Our apps heavily utilize RxSwift, and our team is at the forefront of RxSwift and Functional Reactive Programming (FRP)
We maintain very high standards of quality and stability, with our crash-free session rate typically at an impressive 99.98%
We automate a lot of tedious tasks like signing, releasing, localizing, and testing, freeing you to focus on developing awesome features
We foster a culture where self-initiative is encouraged, and developers are empowered to push their own ideas, not just work on planned tasks.

We are on an expedition to find you, someone who is passionate about creating intuitive, out-of-this-world production-grade AI infrastructure. This group builds scalable, high-performance AI systems for internal users and external customers, designed to run seamlessly across cloud and on-premise environments using the latest hardware advancements.
:Responsibilities
Design and optimize LLM serving infrastructure using inference engines (vLLM, TensorRT-LLM, Triton Inference Server)
Implement and tune distributed inference strategies including tensor parallelism, pipeline parallelism, and multi-node serving
Develop and apply model compression techniques to optimize cost, latency, and memory footprint while maintaining model quality
Build self-service fine-tuning platforms that enable data scientists to run experiments (LoRA, QLoRA, full fine-tuning) in a standardized, reproducible, and governed manner
Optimize inference performance through batching strategies, KV-cache tuning, and speculative decoding
Develop reusable APIs, abstractions, and platform services for model deployment, scaling, and lifecycle management
Collaborate with AI researchers and product teams to productionize models and meet latency/throughput requirements
Evaluate and benchmark new model architectures, compression methods, and serving frameworks

It starts with you - ML Engineering Team Lead responsible for leading a team delivering end-to-end ML systems. This role combines people management with ownership of model training, evaluation, deployment, and ML platform standards
If you want to grow your skills building AI products for mission-critical AI, join mission - this role is for you. :
: Responsibilities
Lead and mentor ML Engineers delivering production ML systems.
Own technical direction for model architectures, training pipelines, and evals.
Review designs and code; ensure scalability, reliability, and data quality.
Define standards for experimentation, reproducibility, and monitoring.
Partner with product and platform teams on roadmap and prioritization.
Balance hands-on technical contribution with people management.
Build a culture of measurement, rigor, and continuous improvement.

We are on an expedition to find you, someone who is passionate about creating intuitive, out-of-this-world production-grade AI infrastructure. This group builds scalable, high-performance AI systems for internal users and external customers, designed to run seamlessly across cloud and on-premise environments using the latest hardware advancements.
:Responsibilities
Design, build, and maintain scalable Kubernetes-based infrastructure for ML workloads across on-premise and cloud environments
Architect hybrid infrastructure solutions enabling seamless model flow from on-premise training environments to cloud-based inference deployments
Implement model registry and artifact management strategies that support cross-environment synchronization, versioning, and governance
Design secure, efficient data and model transfer mechanisms between on-premise and cloud (networking, storage replication, caching strategies)
Implement and manage GPU scheduling, resource allocation, and cluster autoscaling for heterogeneous compute environments
Build and maintain CI/CD pipelines for ML systems, including model versioning, testing, and promotion across environments
Develop observability solutions (logging, monitoring, alerting) for ML infrastructure across hybrid deployments
Collaborate with ML Engineers to define infrastructure requirements and SLAs for training and serving workloads

Were looking for an experienced AI Project Manager to lead and coordinate cross-functional projects within our AI Group. You will partner with research, data science, and product leaders to translate strategy into action, managing timelines, dependencies, and priorities for our most advanced AI initiatives.
:Responsibilities
Lead the planning, execution, and delivery of multiple AI projects across research and production teams.
Partner with AI scientists, ML engineers, and product stakeholders to define goals, success metrics, and execution plans.
Establish and maintain clear project roadmaps, ensuring alignment with the AI Groups strategic priorities.
Manage communication across technical and non-technical stakeholders, providing visibility into progress and blockers.
Drive agile processes within AI teams to support experimentation, iteration, and scalable delivery.
Identify and mitigate project risks, ensuring smooth collaboration across research, data, and engineering functions.
Promote a culture of technical rigor, transparency, and innovation within the AI Group.

It starts with you - an engineer driven to build modern, real-time data platforms that help teams move faster with trust. You care about great service, performance, and cost. Youll architect and ship a top-of-the-line open streaming data lake/lakehouse and data stack, turning massive threat signals into intuitive, self-serve data and fast retrieval for humans and AI agents - powering a unified foundation for AI-driven mission-critical workflows across cloud and on-prem.
If you want to make a meaningful impact, join mission and build best-in-class data systems that move the world forward - this role is for you.
:Responsibilities
Build self-serve platform surfaces (APIs, specs, CLI/UI) for streaming and batch pipelines with correctness, safe replay/backfills, and CDC.
Run the open data lake/lakehouse across cloud and on-prem; enable schema evolution and time travel; tune partitioning and compaction to balance latency, freshness, and cost.
Provide serving and storage across real-time OLAP, OLTP, document engines, and vector databases.
Own the data layer for AI - trusted datasets for training and inference, feature and embedding storage, RAG-ready collections, and foundational building blocks that accelerate AI development across the organization.
Enable AI-native capabilities - support agentic pipelines, self-tuning processes, and secure sandboxing for model experimentation and deployment.
Make catalog, lineage, observability, and governance first-class - with clear ownership, freshness SLAs, and access controls.
Improve performance and cost by tuning runtimes and I/O, profiling bottlenecks, planning capacity, and keeping spend predictable.
Ship paved-road tooling - shared libraries, templates, CI/CD, IaC, and runbooks - while collaborating across AI, ML, Data Science, Engineering, Product, and DevOps. Own architecture, documentation, and operations end-to-end.

we are looking for a Senior Data Scientist - AI Research Group.
:Responsibilities
Conduct hands-on research and development of state-of-the-art models, algorithms, and systems.
Lead research projects end-to-end from problem formulation, ideation, and experimental design to prototyping and transition into production.
Explore new methodologies and develop creative approaches to solve complex challenges.
Identify ambitious research directions and invent novel approaches to solve complex, high-impact problems.
Shape the research roadmap and influence long-term technical strategy across the organization.

It starts with you - an engineer driven to operate database systems at the highest level of reliability and performance. You care about query latency, uptime, data durability, and getting paged as little as possible. Youll operate, tune, and scale the database engines that serve the platform - from PostgreSQL to Elasticsearch, Redis to vector databases, across cloud and on-prem environments.
If you want to run the databases that power mission-critical AI at national scale, join Dreams mission - this role is for you.
The Dream-Maker Responsibilities
Operate and maintain database systems - PostgreSQL, Elasticsearch, Redis, MongoDB, vector databases, and others across cloud and on-prem.
Own database reliability - high availability configurations, replication, failover automation, and SLA adherence.
Drive performance tuning - query optimization, index design, configuration tuning, and resource profiling to meet latency and throughput targets.
Execute operational procedures - backup/recovery, disaster recovery testing, upgrades, migrations, and capacity scaling.
Lead incident response for database issues - troubleshooting production problems, root cause analysis, and implementing preventive measures.
Build monitoring and alerting - dashboards, metrics collection, slow query analysis, and proactive capacity alerts.
Enable new capabilities - deploying and tuning vector databases for AI workloads, evaluating new database technologies.
Collaborate with Data Platform, Data Engineering, Engineering, and Security teams on database operations and best practices.
Uphold database SLAs that support retrieval paths, feature stores, and embedding durability; coordinate safe schema evolution, partitioning, and replay/backfill practices.
Expose catalog and lineage signals - ownership, change history, and impact analysis - to improve trust and safe consumption for downstream users.
Collaborate with Data Platform, Data Engineering, Engineering, Security, Product, AI/ML, Data Science, and Analytics to balance performance, durability, and evolution across workloads.

It starts with you - an engineer driven to build resilient, automated infrastructure that enables teams to move fast with confidence. You care about operational excellence, developer experience, and reliability at scale. Youll architect and operate the compute and networking infrastructure that powers our AI platform - from CI/CD pipelines to Kubernetes clusters to observability systems - across cloud and on-prem environments.
If you want to build infrastructure that powers mission-critical AI systems at national scale, join mission - this role is for you.
:Responsibilities
Architect and operate Kubernetes-based infrastructure across AWS and on-prem environments, ensuring high availability, security, and performance.
Design and maintain CI/CD pipelines for application and service deployments with automated testing, security scanning, and rollback capabilities.
Drive infrastructure-as-code practices for compute and networking - building reproducible, auditable, and version-controlled infrastructure.
Own reliability and incident response - establish SLOs, build alerting systems, lead incident resolution, and drive post-incident improvements.
Enable AI-native operations - support agentic deployment pipelines, self-healing infrastructure, and secure sandboxing for model experimentation.
Build and maintain observability systems - metrics, logging, tracing, and dashboards that provide visibility into system health.
Optimize infrastructure cost and performance - right-size resources, implement auto-scaling, and identify efficiency opportunities.
Collaborate with Engineering, Data Platform, Data Engineering, and Security teams to align infrastructure with platform needs.
Shape infrastructure characteristics that support data freshness, correctness, and low-latency pathways for AI training/inference, retrieval, and agentic workflows.
Contribute paved-road tooling - reusable CI/CD patterns for services, IaC modules for compute and networking, and runbooks - that streamline delivery across teams.
Collaborate with Engineering, Data Platform, Data Engineering, Security, Product, AI/ML, Data Science, and Analytics to anticipate and meet cross-functional needs.

It starts with you - a senior ML engineer responsible for building, training, evaluating, and operating machine learning systems in production. The role focuses on data pipelines, model training, experimentation, evaluation, and scalable deployment.
If you want to grow your skills building AI products for mission-critical AI, join mission - this role is for you.
:Responsibilities
Design, train, and evaluate ML models for production use.
Build and maintain data pipelines for training, validation, and inference.
Own experimentation workflows: feature engineering, training runs, and comparison.
Implement model evals, monitoring, and drift detection.
Package and deploy models to production systems.
Optimize training and inference performance, cost, and reliability.
Collaborate with data, platform, and product teams.
Mentor engineers and promote ML engineering best practices.