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Were looking for a Senior Backend Software Engineer to help develop our hyperscaling platform.
We have a lot of tasks on Golang and little less on Java and Python.

In this position, your responsibility will be to:
Developing fault-tolerant, reliable cloud services: cloud infrastructure, managed databases, managed Kubernetes, billing, monitoring systems, ML service and more.

We are looking for a Senior Data Engineer to own substantial parts of our data platform and deliver complex data products end to end. You will turn ambiguous business needs into pragmatic technical solutions, make architecture and implementation trade-offs, and improve the reliability, scalability, and usability of our data ecosystem.

You will work closely with product, platform, and business teams, helping them use data effectively while ensuring that our systems remain maintainable and trustworthy grows.

Your responsibilities:


Own the design, delivery, and operation of complex data pipelines, datasets, and platform components.
Translate business and analytical requirements into scalable data models, reliable data products, and clear technical plans.
Design and evolve data architecture, storage, processing, and orchestration patterns for large-scale workloads.
Improve data quality, observability, lineage, and incident response for critical datasets and pipelines.
Investigate and resolve challenging performance, reliability, and data-correctness issues in production.
Establish reusable tools, conventions, and automation that improve engineering productivity and reduce operational risk.
Work with product teams and business stakeholders to define data contracts, priorities, and success criteria.
Contribute to technical direction through design reviews, thoughtful trade-offs, and documentation.
Support and mentor other engineers through reviews, pairing, and knowledge sharing.
Participate in the on-call rotation and take ownership of improving the operational health of the systems you support.

We are currently looking for senior- and staff-level ML engineers to work on research in areas such as:

Guided search and reinforcement learning for agentic systems
Reinforcement learning for reasoning models
Web-scale problem collection for training agents
Efficient model distillation
Some examples of what your responsibilities might include are:

Conducting experiments to figure out efficient ways to train a large language model on traces of interactions with various environments
Exploring methods of guided generation and search in the trajectory space
Coming up with ways to mine relevant data at web scale and figuring out efficient ways to use this data in model post-training
Conducting experiments with different reinforcement learning configurations in verifiable domains
Exploring methods to train AI agents on tasks with non-verifiable reward signals

We are building an inference & fine-tuning platform that makes every kind of foundation model - text, vision, audio, and emerging multimodal architectures - fast, reliable, and effortless to train & deploy at massive scale.

Some directions we currently working on and which you can be a part of:
Advanced Fine-Tuning: Enhancing fine-tuning methodologies - both LoRA-based and full-parameter - for cutting-edge LLMs (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-4.7), focusing on both model quality and training efficiency.

Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. This involves building model training and evaluation pipelines in JAX for speculative decoding, experimenting with architectures (dense/MoE, auto-regressive/parallel), and deriving scaling laws to guide resource allocation.
Low Precision Training & Inference: Investigating low-precision (FP8, NVFP4/MXFP4) methodologies for supervised fine-tuning and reinforcement learning - spanning both inference and training - optimized for modern hardware

We are building a high-performance inference and fine-tuning platform designed to push foundation models to their hardware limits. Our mission is to maximize throughput, minimise latency, and optimise cost-per-token across tens of thousands of GPUs.



Some directions we are currently working on, and which you can be a part of:

Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. Squeezing the maximum performance for a wide range of LLM architectures at scale (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-5).
Inference engines support: Implement novel speculative decoding architectures, optimise components of various LLM designs (dense/MoE, autoregressive/parallel), and contribute to open-source inference engines.
Low Precision Training & Inference: Design and productionise low-precision (FP8, NVFP4/MXFP4) training and inference pipelines with measurable gains in throughput and cost-efficiency.

We are looking for a Senior Product Manager to join our Agentic Search team, building the search and retrieval infrastructure that powers AI agents and LLM-based applications at scale.

This is a hands-on IC role with real ownership. You will take full end-to-end responsibility over a core product area, from defining strategy through shipping and iterating based on real usage data. You will work within a small PM team reporting to the Director of Product, alongside engineering, design, and go-to-market, shaping how AI agents discover, retrieve, and act on web information in real time.

The product serves both a developer-facing PLG motion and a growing enterprise customer base, so you will need to be equally comfortable digging into API design and search quality as you are running customer discovery calls and informing competitive positioning. We are looking for someone who can operate as a senior strategic partner, not just an executor, and who has the potential to grow into a principal-level PM over time.

Youre welcome to work hybrid from Tel Aviv, Israel.

Your responsibilities will include:

Own a core product area end-to-end: strategy, roadmap, execution, and iteration based on real usage signals and customer feedback.
Drive search quality and relevance improvements by working closely with engineering and research to define evaluation frameworks, benchmarks, and release criteria.
Engage directly with enterprise customers and developer users to validate ideas, understand pain points, and shape product-market fit across both PLG and enterprise motions.
Monitor the competitive landscape in agentic search and AI-powered retrieval, identifying shifts, threats, and opportunities that should inform product decisions.
Collaborate with go-to-market teams (sales, solutions, growth) to ensure product positioning, ICP definitions, and enablement materials reflect product reality.
Contribute to product strategy as a senior voice on a small, high-impact team, challenging assumptions and bringing independent judgment to key decisions.
Lead product experiments and rapid iteration cycles, moving from hypothesis to prototype to production with clear success criteria.

In your first year, we expect you to set a clear quality bar for our search products that the team builds against, have a real influence on where our index goes next, and bring us a point of view on where search infrastructure is heading.

This is a hands-on senior IC role with real ownership, reporting to the Director of Product. You will work day to day with our search engineering and research teams, with the evaluation team, and with customer success, who bring us quality feedback from real accounts every week. Our search engineers are deeply experienced in large-scale retrieval, and the product team is small, so you will argue your case directly with the people building it. That only works if you have built at this scale yourself.

Your responsibilities will include:

Set the quality bar and release criteria for search: what good means per use case, and what a change has to clear before it ships. Our evaluation team owns the measurement, and you work closely with them on what to measure and why.
Own the depth, latency, and cost tradeoffs across our search products, and make them explicit and defensible.
Shape the direction of our index: what it needs to cover, how fresh it needs to be, and what quality bar it has to meet.
Build a point of view on where search infrastructure is going as agents, rather than people, become the main consumer, and turn that into a roadmap.
Know how we compare to the alternatives customers evaluate, and drive the work that closes the gaps that matter.
Work with customer success and directly with customers to understand where our results fall short in real workflows, and turn that into quality priorities.
Partner with the data and vertical product owners on what our corpus needs to contain and how results should be shaped per industry.

You will sit with customers in these industries, learn how they actually work, and turn their needs and pain points into clear specs. You will build with engineering, ship it, and make sure go-to-market knows how to sell it. You own the outcome, not just the handoff: from the first market call through to adoption and revenue.

The hard part is choosing. There are more verticals worth building than we can build, and the difference between a good year and a wasted one is knowing which ones to invest in and which to walk away from, even when a large customer is asking for another.

In your first year, we expect you to know our top use cases better than anyone here, ship a product built for one of them that customers will pay more for, and have a defensible view of what comes next.

This is a hands-on senior IC role with real ownership. You will work within a small PM team reporting to the Director of Product, alongside engineering, research, data, and go-to-market.

Your responsibilities will include:

Own the vertical roadmap. You decide where we invest, what we ship for each, and when a vertical is not worth it.
Shape the product surface on top of the data, so a customer gets something built for their job.
Find and evaluate the data behind each vertical, across public sources and paid providers, and decide what is worth investing in.
Sit with customers in these industries until you understand their workflow, then turn that into specs engineering can build.
Set what a good answer looks like in each use case, working with our evaluation team, who own how we measure it.
Work with customer success to learn what customers ask us for and what makes them leave.
Partner with the search owner on how results should be ranked and shaped for each use case.

We're an SRE team within DevTools, looking for someone ready to help maintain and grow our systems
Your responsibilities will include:

Improving services based on user feedback

Building fault-tolerant, self-healing architecture

Finding ways to speed up our systems and reduce user friction

Modifying well-known closed- and open-source solutions Supporting our users