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we are looking for a Staff Technical Program Manager.
The Managed Inference platform is where customers run production LLM workloads without managing low-level infrastructure, and it is one of fastest-growing product areas. The Staff TPM for Managed Intelligence connects model engineering, IaaS, product, and data center operations to deliver a reliable, scalable inference platform.
You will own end-to-end program delivery across multi-quarter roadmaps, model onboarding, inference optimization, and production readiness for new model versions. Deep familiarity with the model layer -- including how LLMs are served, optimized, and evaluated in production -- is essential to being effective in this role.

abra professional services is seeking for a Board Design Engineer to Join a leading global technology company developing advanced mission-critical systems and cutting-edge solutions across a wide range of defense applications. As part of our multidisciplinary engineering teams, you will take part in the development of advanced hardware platforms while working with cutting-edge technologies and complex engineering challenges In this role you will Support the development lifecycle of printed circuit boards (PCBs), from requirements capture and board-level architecture definition through design, integration, and testing of advanced digital hardware systems Work closely with Software, FPGA, Mechanical, and Environmental Engineering teams throughout the development process Lead PCB layout reviews, high-speed design activities, and signal integrity considerations Perform debugging, validation, and engineering problem-solving activities during development and integration phases Collaborate across teams and contribute to technical discussions and engineering solutions

As a Technical Project Manager, you will drive the planning, execution, and delivery of key cross-domain initiatives across Product and R&D. As part of our Product organization, you will act as a key connector between Product, R&D, QA, and Operations, ensuring teams are aligned and execution stays on track. You will navigate complex dependencies, remove blockers, and drive initiatives from early-stage 0-to-1 products through large, multi-quarter projects in a dynamic and fast-paced environment.

What youll do:

Lead Cross-Domain Initiatives: Own end-to-end execution of complex initiatives across multiple Product and R&D teams, managing dependencies, risks, priorities, and timelines.
Define the Solution: Take an active part in defining the solution together with the Product Lead and R&D Lead, so the plan is shaped with the people who will build it and own it.
Plan & Align: Take an active role in quarterly planning - OKRs, capacity, and roadmaps - and partner closely with Product and R&D leaders on execution plans.
Run the Delivery Discipline: Apply project management practice consistently across your programs: progress tracking, planning and timeline management (including Gantt views), capacity planning, risk assessment, and scope and change control.
Own External Commitments: Track and protect the commitments that carry an outside deadline - a client at risk, a launch date agreed with a partner - and surface their real status early enough for the organization to act on it.
Drive Execution: Turn strategy into action, resolve complex bottlenecks, and balance business priorities against technical constraints so teams can make clear, practical decisions.

Were looking for a hands-on AI Solutions Manager who combines deep practical knowledge of generative AI tools with strong business understanding. This role sits at the intersection of technology and impact - identifying real organizational needs and translating them into scalable, AI-driven solutions.

This role is perfect for someone who lives and breathes AI technology, stays ahead of the rapidly evolving landscape, and knows how to turn cutting-edge capabilities into real-world solutions.

What Youll Do

Collaborate with cross-functional teams to identify opportunities where AI can solve complex business and internal problems
Evaluate and select the right AI tools and models (Claude, Gemini, OpenAI, etc.) based on use case, limitations, cost, performance, and scalability
Design and implement AI-powered solutions and internal automations using LLM APIs and workflow platforms (e.g., N8N, Workato)
Build and optimize prompts, RAG pipelines, and AI agents while understanding model limitations and trade-offs
Architect scalable AI workflows and integrations across systems (e.g., Salesforce, Jira, Confluence, , etc.)
Lead hands-on projects from ideation to rollout, iterating rapidly to deliver value
Stay up to date with emerging AI capabilities and continuously evaluate new tools and frameworks
Educate and guide the organization how to adopt and use wisely AI tools and models

At Infinidat, a Lenovo company, we empower enterprises and service providers with cutting-edge storage platforms that redefine performance, reliability, and cyber resilience at scale. We are looking for a Hardware Integration Engineer to join our expanding R&D team. In this role, you will collaborate with off-the-shelf component vendors to evaluate new technologies for integration into our products, qualify new hardware and firmware releases, and lead debugging efforts on complex technical issues. You will also provide hardware-focused support to internal teams, including Level 3 support, Manufacturing, R&D, QA, and IT.

Responsibilities:

* Demonstrate deep expertise in storage hardware products, including servers, drives, enclosures, and power components
* Evaluate and qualify new hardware components, actively participating in their integration into the product
* Collaborate with global vendors and cross-functional internal teams to enhance product quality and resolve complex technical issues
* Lead failure analysis investigations- both internally and with suppliers and drive corrective and preventive actions
* Manage and lead new and existing hardware projects, continuously exploring technologies and methodologies
* Maintain and update the hardware knowledge base using industry-standard tools (e.g., Jira, Google Sheets, etc.).

We are looking for a Ground Systems Integration Engineer to join a multidisciplinary team responsible for the integration and operation of satellite ground systems, with a strong focus on RF and communication technologies, including antennas, satellite modems and ground station equipment.
Responsibilities:

* Integrate and support satellite ground system components, including RF, hardware, software and network elements.
* Collaborate with multidisciplinary engineering teams.
* Troubleshoot system and RF -related issues.
* Create and maintain technical documentation.
* Support project execution and on-site activities abroad.

We are looking for a Senior Data Science Researcher to join Honeycomb's AI team, the team behind underwriting decisions, quote-time risk scoring, and portfolio analytics.

In this role, you will own entire research directions: framing the problem, deciding what's worth measuring, designing the approach from first principles, and turning
the result into production signal. The work is production-oriented applied research on hard, real-world datasets where standard recipes don't apply and the right method
often has to be invented for the problem.

What You'll Do

- Own open-ended research directions end-to-end - from a vague business question to a deployed, validated signal.
- Work across the team's core research areas - risk modeling, environmental and catastrophe modeling, and behavioral modeling - translating complex real-world processes
into reliable predictive signal.
- Deeply investigate existing production models - understand what they actually learn, where they break, and where the headroom is; identify and ship optimizations
grounded in that understanding.
- Design approaches from first principles when off-the-shelf methods don't fit the data.
- Build rigorous evaluations and own the result in production, not just the notebook.
- Document research clearly so findings are reproducible, auditable, and compound over time.

We are seeking a AI Engineer to join our team and implement advanced AI solutions in our travel and expense management products. The ideal candidate will have strong backend engineering expertise and enthusiasm for working with cutting-edge AI technologies.
You'll be responsible for designing and implementing backend services that integrate AI capabilities including large language models (LLMs), retrieval-augmented generation (RAG), and conversational interfaces into our consumer-facing products. This is an opportunity to expand your expertise into AI while leveraging your strong engineering foundation.
What Youll Do:
Design and develop scalable, high-performance backend services and APIs.
Implement integrations with AI platforms and services.
Work with AI specialists to translate AI capabilities into production-ready features.
Ensure high reliability, performance, and security of AI-powered systems.
Collaborate with product and engineering teams to gather requirements and deliver solutions.

We are looking for a Forward Deployed Egngineer with a strong performance marketing background to lead the deployment and success of our solution for a small number of enterprise customers.
You will own the full post-sale lifecycle, from implementation through ongoing optimization, working directly with growth, data, and engineering teams at some of the world's leading consumer apps. Your focus is on signal quality, experiment design, and driving measurable impact on paid acquisition performance.
This is a high-ownership role for someone who thinks in terms of ROAS, CPA, and bidding strategy, and can also get deep into data and systems to make it happen. You will be the primary point of contact for your accounts, lead engagements end-to-end, and translate performance data into clear decisions.
Success means turning your customers' best predictive signals into compounding growth on Google and Meta.
Responsibilities:
Performance and experimentation:
Design and structure pilots across Google Ads and Meta, including campaign selection, budget approach, bidding strategy, and success criteria
Evaluate experiment results and iterate based on performance signals
Help customers understand performance drivers, attribution, and how to scale what works
Monitor model and signal performance; identify issues early and intervene
Implementation and signal quality:
Lead end-to-end implementation across a small portfolio of enterprise customers
Adapt solution design and data strategy to each customer's ad tech stack and measurement setup
Ensure strong match rates, data accuracy, and signal reliability across conversion APIs and MMPs
Troubleshoot integration and data issues in collaboration with internal teams
Customer partnership and execution:
Own day-to-day execution across multiple engagements
Lead ongoing customer communication with both technical and senior growth stakeholders
Coordinate across product, engineering, and go-to-market teams
Build long-term trust through reliability and strong outcomes.

Were looking for a talented AI-native Software Engineer to join our innovative R&D team!
We help our customers acquire better users and spend less doing it - and our core team owns the engine behind it. We turn raw customer data into ad-network conversions, end-to-end: through large-scale ML and data pipelines, event-driven delivery services on AWS, and the UI that ties it together. There's no narrow lane here - you own problems wherever they live in the stack.
We're an AI-native team: we treat AI and agentic tooling as a first-class part of how we build, and it's how a small team credibly owns this much surface area. We're looking for a high-ownership generalist who works this way (or is hungry to), and who thrives in a correctness-critical domain where a bug means real customer ad-spend going the wrong way.
Responsibilities:
Turn raw customer data into ad-network conversions end to end, building across the whole stack: ML and data pipelines, delivery services, and the customer-facing UI.
Take features from idea to production largely on your own, designing, shipping, monitoring, and iterating, using AI and agentic tooling as a force multiplier, and owning your systems in production where mistakes translate directly into customer spend.
Collaborate closely with the team lead, product, and data scientists to take models and ideas from prototype to reliable, scaled-up production.