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we are looking for a passionate and detail-oriented Product Owner & Engineer to join our team. In this role, you will collaborate closely with development teams to define new features, contribute to functional requirement documentation (FRD), and ensure seamless UX design and implementation.
A core part of this role is maintaining close alignment with customers. You will actively engage with customers, gather feedback, understand real-world use cases, and translate their needs into clear product requirements. You will work closely with Support and QA to ensure our testing strategy and supportability reflect actual customer environments and workflows.
Beyond core development, you will assist in critical escalations, manage complex or high-visibility installations, and develop tools that enhance the overall user experience. You will leverage telemetry data, production insights, and direct customer feedback to continuously refine and improve our products, ensuring they deliver measurable value in real-world deployments.
Key Responsibilities
Collaborate closely with Product and Engineering teams to define, refine, and prioritize features that directly address real customer needs and business impact.
Translate customer requirements and field insights into clear, structured Functional Requirement Documents (FRDs), while actively contributing to UX discussions to ensure intuitive and seamless user experiences.
Work closely with QA and Support to align testing strategies and troubleshooting workflows with real-world customer environments, ensuring reliability, operability, and supportability at scale.
Serve as a technical and product focal point during critical customer escalations and high-visibility deployments, ensuring timely resolution and long-term improvements
Develop tools and scripts to enhance user experience and operational efficiency.
Leverage telemetry, usage analytics, and direct customer feedback to drive data-informed decisions and continuously improve product performance and adoption.
Proactively identify risks, gaps, and cross-team dependencies, removing roadblocks to ensure successful delivery and measurable customer outcomes.

LLM optimization is a young field with no settled playbook, so the optimizations you invent here are genuinely new, and they ship into production rather than a paper. And they matter: real enterprises run enormous volumes of LLM traffic and feel every wasted token, so the work you do lands on live customer requests and solves a problem they actually have. It's about as close to the frontier, and as close to the product, as engineering gets.

03What you'll do
Invent and ship optimizations. Design new strategies across every layer of a request: prompt and context compression, tool pruning, file and dev-command trimming, semantic caching, cache-prefix stabilization. Then take them all the way to the hot path. Each one has to cut tokens on real traffic without degrading output.
Prove every win. Build the evals and the token/quality instrumentation that separate a genuine saving from a plausible-sounding one. An optimization only ships when the numbers back it.
Live in the caching trade-offs. Provider prompt caches only pay off on byte-stable prefixes, and a naive rewrite can cost more than it saves. A lot of the job is knowing exactly when an optimization actually wins: deterministic transforms, per-session decision pins, semantic dedup.
Build and own the gateway that runs them. The real-time proxy that carries production LLM traffic, with streaming, retries and backpressure, executing every optimization on the live request path.
Keep it fast and deploy anywhere. Defend a tight latency budget on the hot path, and package the whole thing so it installs cleanly into any customer's cloud, whether that's AWS, GCP, Azure, containers or Kubernetes, and upgrades without drama.
Own systems end to end. From first commit to code serving live customer traffic, plus the spend-visibility and governance product built on top, with a direct line to the founders.

Connect is a Node CLI and a signed tray app for macOS, Windows and Linux. It points a developer's AI coding tools at our gateway by writing each vendor's own config, signs them in, keeps their credentials fresh, and repairs itself when something drifts. For IT, it ships the artifacts they need to roll it out across a fleet.

It's real, it's shipping, and customers are using it. You'd own it.

03What you'll do
Own Connect. The CLI, the tray app, updates, docs, and what ships next.
Write into other apps' config without breaking them. Back up what you touch, roll back cleanly, and never leave someone's setup worse than you found it.
Keep up with vendors. Config shapes change under us. Check before writing, and fall back to a manual path instead of guessing.
Stay clear of antivirus. Persistence and credential helpers are exactly what EDR watches for. Measure what actually trips a scanner instead of assuming.
Ship for managed fleets. Profiles, policy, credential helpers, and silent rollout. That's what an admin needs to push this to a few hundred laptops at once.
Make it debuggable from far away. Health checks, repair commands, and diagnostics that carry metadata only. Never prompts, never keys.

What You Will Be Doing
Own the co-build agenda with AWS & GCP
Drive our active co-build initiatives with AWS/GCP product and engineering teams from scoping through launch.
Own the technical relationship with AWS/GCP product managers and engineers on the EKS/GKE, Karpenter, and container services teams. Be the person they call, and the person who can answer without scheduling a follow-up.
Track the EKS/GKE and AI/ML roadmap closely enough to tell us what it means. Dynamic Resource Allocation, MIG and time-slicing landing in Karpenter and Auto Mode/Auto Pilot, managed control plane work, and pod isolation all move the ground under our product. You will be the first person to see it coming.
Continuously validate that we run on where AWS/GCP are going, not just where it is. As AWS/GCP abstracts the node further, our agent, our scheduler, and especially our GPU device plugin need to keep working. Confirming that, early, is a standing part of the job.
Bring AWS/GCP requirements and constraints back into R&D as real engineering input, and represent our architecture back to AWS/GCP accurately enough that they design around it.
Look for opportunities to be a design partner and later a launch partner.
Technical depth on us and EKS/GKE
Be the person who can explain how we work on EKS/GKE to any technical audience, without an SE in the room: real-time pod right-sizing, HPA and replica management, intelligent pod placement, Karpenter and node optimization, Spot, and GPU fractionalization and right-sizing for inference workloads.
Hold your own with AWS/GKE Solutions Architects and customer platform engineers on scheduling, autoscaling, resource management, and where our approach differs from static MIG and time-slicing.

We're looking for a Researcher who thrives on exploring the technical frontier and translating complex insights into real-world impact. This is a hands-on role, combining deep research with prototyping and cross-team collaboration, with the goal of driving innovation in Kubernetes internals, cloud infrastructure, and performance optimization.

Conduct deep research into Kubernetes internals, scheduling, autoscaling, and container resource usage.
Explore cutting-edge strategies for cloud resource optimization, cost efficiency, and performance engineering.
Design and deliver hands-on POCs, simulations, and benchmarks to validate new approaches.
Work closely with product and engineering teams to translate research findings into features and enhancements.
Stay at the forefront of the cloud-native and FinOps ecosystems, surfacing trends and opportunities.
Contribute to technical blogs, white papers, and internal knowledge sharing.

We're looking for a DevOps / Platform Engineer to own our infrastructure end to end- both sides of it.
Side one is our multi-tenant SaaS on AWS: you build it, you run it, you keep it fast, secure, and up. Side two is the part that makes this role unusual - our enterprise customers deploy Avon AI inside their cloud (and sometimes fully air-gapped, on-prem environments). You'll own that too: packaging the platform, running the installation with the customer's infrastructure and security teams, and being the person on the call when their environment doesn't look like anyone's reference architecture.
This is a customer-facing engineering role. You'll be in rooms with enterprise IT, security, and platform teams- walking them through our architecture, answering security review questions, and unblocking deployments. If you want a pure behind-the-scenes infra job, this isn't it. If you like owning infrastructure and being the trusted technical voice in front of a customer, read on.
You'll be our first dedicated DevOps hire, which means you inherit real systems in production and a lot of room to shape how they evolve.
Key Responsibilities
EKS, networking, data stores, CI/CD, cost, and uptime. Build and evolve it with Infrastructure as Code.
Maintain and improve our Helm charts and container distribution so Avon AI installs cleanly in a customer's Kubernetes cluster.
Plan and execute installations in customers' private clouds and on-prem/air-gapped environments, working hands-on with their infrastructure teams from kickoff through go-live.
Support security reviews and architecture discussions, and translate enterprise IT constraints back into our roadmap.
IAM, network segmentation, secrets management, WAF, and the controls enterprise customers audit us on.
Metrics, logs, tracing, and alerting for our SaaS, and self-contained monitoring for customers who won't ship telemetry outside their network.
CI/CD pipelines, ephemeral PR sandboxes, release automation, and local development so engineers ship quickly and safely.
For our SaaS tenants and for deployed customer installations, including on-call ownership of what you build.

we are looking for a Service Systems Engineer.
As a Service Systems Engineer (SSE), you will be responsible for ensuring that the CT Detector Module System (DMS) is designed for efficient installation, maintenance, troubleshooting, and repair throughout its entire lifecycle. In this role, you will collaborate closely with DMS systems engineering, development, service, manufacturing, quality, and field teams to drive serviceability and operational excellence. You will translate customer and field insights into service concepts, requirements, and design enhancements, leveraging service and reliability data to improve system availability, safety, and maintainability. In addition, you will work cross-functionally with other CT teams to manage scanner-level interfaces and dependencies that impact DMS service performance.
Your role:
Develop service concepts and features based on customer, field engineer and service organization needs.
Translate service needs into requirements, service CTQs and design inputs.
Assess serviceability for installation, calibration, troubleshooting, repair, preventive maintenance, and replacement of serviceable items.
Participate in architecture and trade-off studies to ensure service needs are considered in design decisions.
Review DMS designs to confirm they meet service requirements, including relevant hardware, software, electrical, mechanical and thermal aspects.
Contribute to service-related verification activities, including defining service scenarios and evaluating service procedures and tools.
Work with customers, field engineers, and engineering teams to investigate and resolve DMS-related field issues.
Analyze field feedback, maintenance data, reliability trends and service performance
Translate findings into recommendations for hardware, software and service improvements.
Support failure analysis and risk assessment activities, including DFMEA.
Prepare and maintain service documentation, including service procedures, troubleshooting guidance and technical information for field tams.
Share service knowledge with relevant teams and support field engineers as needed.
Coordinate with scanner-level teams on interfaces and integration topics that affect DMS service.

We are seeking a highly experienced, AI-obsessed AI Senior Backend Engineer to join our core Engineering department. In this newly created, cross-functional role, you will be the driving force behind the companys internal AI transformation. You will act as the responsible for artificial intelligence, architecting, building, and deploying custom AI and automation systems from scratch to empower various departments and streamline operations.
Key Responsibilities
Architect, develop, and deploy tailored AI systems and applications from the ground up to solve organizational challenges.
Identify process bottlenecks across different teams and design robust backend automation solutions to optimize daily workflows.
Lead the hands-on integration of modern Generative AI models, APIs, and frameworks into the company's internal infrastructure.
Build scalable, high-performance backend microservices and APIs to support heavy data processing and AI workloads.
Act as the technical AI focal point, partnering closely with internal stakeholders and department heads to gather requirements and deliver high-impact technological solutions.
Collaborate with product, DevOps, and other teams to ensure that AI solutions are aligned with user needs and business goals.

We are looking for a highly motivated and technically curious QA Engineer to join our Backend Infrastructure team on a 1-year fixed term contract. In this role, you will validate large-scale enterprise backend systems used in healthcare environments, ensuring quality, reliability, performance, and operational stability
Your role:
Design and execute functional, integration, system, regression, and end-to-end manual tests for backend infrastructure components and complex distributed systems.
Validate installation, upgrade, migration, configuration, backup, recovery, and deployment workflows across single-server, clustered, distributed, and cloud-based environments.
Work closely with software engineers, DevOps, automation teams, and product stakeholders to investigate failures, improve testing strategies, verify fixes, and contribute to reliable software releases.
Investigate Windows, IIS, networking, database, and service-level issues by analysing logs, diagnostics, system behaviour, and infrastructure dependencies.
Participate in reliability improvement initiatives, design reviews, and post-incident investigations to strengthen overall product quality throughout the development lifecycle.

department. You will be responsible for gathering, analyzing, and reviewing system-level and sub-system requirements, coordinating system partitioning and total system integration using systems engineering practices.
Your role:
Contribute to system architecture, design, and integration of a complex CT subsystem.
Perform hands-on lab work, including system bring-up, debugging and root-cause analysis.
Own and maintain system-level documentation (requirements, specifications, interfaces, design rationales).
Support verification and integration activities across multiple CT platforms.
Lead technical topics and collaborate closely with HW, SW, Mechanics, Physics, Manufacturing, Quality, and Service teams.
Support field and production issue investigations and lifecycle improvements.

we are looking for a Senior Mechanical Engineer.
In this role, you will lead all mechanical aspects of the product in R&D projects, taking it from concept to full scale development while working with multidisciplinary teams that include system engineering, physics, electronics, software, project managers, and others. You will serve as a technical focal point for mechanical design decisions, balancing performance, manufacturability, reliability, and cost.
Your role:
Lead the mechanical design including 3D models, drawings, prototype manufacturing, and production transfer.
Provide solutions that optimize performance, service, manufacturability, and cost.
Perform required analysis, testing, troubleshooting, and documentation.
Work with local and global teams inside and outside Philips to answer design, service, and production challenges.
Own mechanical risk identification, mitigation plans, and design reviews
Operate in a multidisciplinary environment under conflicting constraints of performance, reliability, cost, and development schedule.

We're forming a new AI Group and looking for a Senior AI Engineer to help shape it from an early stage - a greenfield, long-term effort to evolve how decisions are made across our platform using AI-driven systems. You won't just integrate APIs or build demos; you'll build the AI brain that works alongside (and increasingly drives) our core automation engine, with real production impact from day one and room to grow into technical leadership as the group scales.

Agentic AI Architecture: Design and build autonomous AI agents that analyze infrastructure in real time and make intelligent decisions. Work with modern agentic frameworks (LangGraph, PydanticAI) and conversational AI to create multi-agent systems - including troubleshooting, optimization, FinOps, and how-to agents. Leverage core LLM capabilities (tool-use, memory, retrieval) to operate safely in production.
Platform Integration & Intelligent Decision Systems: Develop MCPs to expose our capabilities to AI agents that reason over infrastructure environments, metrics, configurations, and cost signals. Build integrations with tools like Slack, Jira, and AI-powered IDEs (Cursor, Windsurf) to deliver context-aware insights, from "why is this pod not scheduling?" to "how can we reduce costs by 30% safely?"
AI Model Development & MLOps: Build and deploy machine learning models that learn from infrastructure patterns - detecting the right resource policies for workloads, predicting optimal scaling triggers, and recommending GPU configurations. Own the complete ML pipeline from training to production, ensuring models are reliable, monitored, and continuously improving.
R&D AI Tools Development & Adoption: Build and embed internal AI tools to accelerate engineering, development, research, and support.
AI Tools for Business Impact: Develop AI-powered tools that help Sales and Support teams demonstrate value instantly - agents that analyze customer infrastructure, generate cost optimization reports automatically, and turn technical data into clear business recommendations.
End-to-End Ownership: Own AI systems from concept to production, ensuring they're fast (sub-2-second responses), reliable, safe, and cost-effective. Build evaluation frameworks to measure quality, implement security controls, and balance performance tradeoffs in production.
Technical Leadership: Define AI architecture and best practices as a founding member of the AI team. Make key technical decisions - choosing frameworks, designing multi-agent systems, establishing data governance - and shape how we evolve from AI-enhanced internal tools to customer-facing AI products.

We are seeking an experienced Technical Lead - Hardware Engineering to lead the technical development of advanced electronic systems and manage external hardware design contractors throughout the product lifecycle. The role is responsible for defining the hardware architecture, supervising outsourced development, ensuring engineering quality, and supporting projects from concept through production.
Key Responsibilities
Define hardware architecture and technical requirements. Lead and manage external hardware design contractors. Review and approve schematics, PCB layouts, and design documentation. Oversee High-Speed PCB design, SI/PI, DFM, and design reviews. Lead prototype bring-up, debugging, validation, and production support. Collaborate with system, FPGA, Firmware, Software, Mechanical, Manufacturing, and Quality teams. Drive technical problem solving and continuous improvement.