דרושים » הנדסה » Founding Forward Deployed Engineer

We are hiring Senior Forward Deployed Engineers - the Technical Builders, responsible for deciding that leads who design, build, and deploy the most complex agentic AI solutions directly inside enterprise customer environments. You'll lead technical delivery on your team, mentor the next wave of engineers, and set the engineering standards that define how agentic AI gets deployed at scale. Partnering closely with a Deployment Strategist (Strategic Builder - a customer-facing advisor who shapes AI strategy and drives adoption), you'll own outcomes end-to-end, from architecture decision to production. Your passion for well-crafted software that solves real problems will be supported by best-in-class AI tools and a team of talented builders around you.

The Senior Builder Experience

Technical Ownership: Partner with a Deployment Strategist to own customer outcomes end-to-end. You're the senior technical authority in the room, ensuring deliverables meet the highest engineering standards, whether it was generated by an AI tool by a colleague or written by you directly.

AI-Supported Engineering: Cursor, Claude, and Salesforce coding products like Vibes are embedded in your daily workflow. The conventions and patterns you develop tend to propagate through the team because other Builders look to your work as reference.

Roadmap Influence: Your field experience routes directly to Product and Engineering. The gaps you hit and the edge cases you encounter in real life customer environments become the features they ship. Senior Forward Deployed Engineers are the most credible voice in agentic AIproduct evolution.

Continuous Innovation: You're expected to stay at the forefront - piloting emerging AI tools, experimenting with new models and frameworks, and sharing what you learn with your team and customers.

LLM optimization is a young field with no settled playbook, so the optimizations you invent here are genuinely new - and they ship into production, not 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 - and 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. Knowing exactly when an optimization actually wins - deterministic transforms, per-session decision pins, semantic dedup - is a lot of the job.
Build and own the gateway that runs them. The real-time proxy that carries production LLM traffic - streaming, retries, 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 - AWS, GCP, Azure, containers, 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.

Join our company, an innovative startup building a fully-managed LLM-inference platform, that enables data heavy enterprises to perform any AI task at any scale without limits.
We're looking for an Experienced Performance Researcher to join our founding team. Youll be responsible for building and optimizing scalable cloud infrastructure solutions tailored for AI workloads. This role offers a unique opportunity to directly shape our infrastructure strategy, improve system reliability and performance, and contribute to establishing our company as a leader in adaptive AI compute management.
Join us to tackle the magic that make AI tick under the hood and build the backbone powering the AI revolution.
What Youll Do
- Design and build high-performance distributed inference pipelines for LLMs, focused on large-batch, non-real-time scenarios.
- Optimize GPU memory usage, kernel execution, and communication across nodes (NCCL, MPI, etc.).
- Own CUDA kernels, compiler-level tricks, and multi-GPU scheduling logic.
- Lead profiling and performance tuning for throughput, and cost- down to the kernel level.
- Collaborate with infra, product, and research teams to define SLAs, resource allocation logic, and runtime behaviors.
- Help build the core infrastructure that will run LLM workloads across hybrid GPU environments (cloud/on-prem/self-hosted).

The MLIL DataPlane team is looking for a Software Development Engineer to own the design and implementation of our inference data plane. We build the software that makes large models run efficiently on custom hardware - spanning model execution, memory management, data movement, and serving integration.
Our work covers the full inference path: integrating serving engines with custom hardware, developing high-performance compute kernels, enabling efficient data movement, and driving models from early validation through production. We operate at frontier scale with large distributed models.
This is a ground-up effort with rapidly evolving hardware and software. We need an individual contributor who can write and optimize low-level code for custom hardware, validate model architectures end-to-end, build test and profiling infrastructure, and drive performance across the stack.

Key job responsibilities
- Develop and optimize compute kernels for a custom ML accelerator architecture, targeting production-level performance for large language model inference.
- Implement and validate LLM architectures end-to-end - from PyTorch model definition through distributed execution on custom hardware.
- Integrate custom accelerator backends into open-source ML serving frameworks (vLLM, PyTorch), including scheduler extensions, memory management, and model parallelism.
- Build and maintain test infrastructure for model correctness validation across CPU, GPU, simulator, and hardware targets.
- Profile and optimize inference workloads - identify bottlenecks, instrument critical paths, and drive latency and throughput improvements from simulation through hardware bringup.
- Own features end-to-end: from design through implementation, testing, and integration into the broader software stack.
- Contribute to CI/CD pipelines that gate model and kernel changes on correctness and performance regressions.

Were looking for our first Forward Deployed Engineer (FDE) to join our company and help build this function from the ground up.
This is a highly technical, hands-on, and customer-facing role. Youll be based in Israel, working closely with our Product and Engineering teams, while traveling regularly across the U.S. to work on-site with our enterprise customers.
Youll work directly with customers to understand their business processes, technical environments, and operational challenges, and translate them into AI-powered solutions. Youll own projects end-to-end - from discovery and requirements gathering through technical design, development, deployment, adoption, and continuous improvement.
As our first FDE, youll have a unique opportunity to define how our company works with customers from a technical perspective, build the processes and best practices for this function, and play a key role in how we scale customer delivery.
Were looking for a strong engineer with a product mindset who enjoys navigating ambiguity, working directly with customers, and turning complex business problems into scalable technical solutions.
our Values
Ownership - We take responsibility and move decisively.
Clarity - We simplify complexity to deliver meaningful impact.
Accuracy - Precision matters in everything we do.
Velocity - We move fast and execute with purpose.
Partnership - We succeed by working closely together.
What Youll Do
Partner directly with enterprise customers, both remotely and on-site across the U.S., to deeply understand their business processes, technical architecture, data flows, and operational challenges.
Lead technical discovery sessions, gather business and product requirements, and translate complex customer needs into scalable AI-powered solutions.
Design, build, configure, and deploy production-ready AI agents, integrations, and workflows that operate reliably within customer environments.
Work hands-on with APIs, enterprise systems, structured and unstructured data, and customer infrastructure to deliver end-to-end solutions.
Own customer engagements from technical discovery through implementation, deployment, adoption, and continuous optimization.
Validate solutions using real customer data, troubleshoot technical challenges, and continuously improve system performance and business outcomes.
Drive measurable business impact by identifying new use cases, increasing platform adoption, and helping drive growth and expansion within customer accounts.
Work closely with Product and Engineering in Israel to influence product direction based on customer feedback, implementation learnings, and real-world use cases.
Act as the technical owner throughout the customer engagement, building trusted relationships with both technical and business stakeholders.
Create reusable implementation playbooks, tools, and best practices that help scale the FDE function as our company grows.

our company builds the production-grade AI backbone for top-tier enterprises. We don't just wrap LLMs. Our platform autonomously handles mission-critical workflows, replacing operational friction with seamless, predictive experiences. We are a lean, engineering-driven company working directly with the biggest players in the market.
An FDE at our company is a customer-embedded engineer who owns the technical success of an AI deployment end to end. You don't advise and hand implementation to someone else. You are the implementation owner. You take an ambiguous business problem, break it into solvable technical components, build the solution on our platform, and stay with it until it works reliably in production.
What You'll Do
Own deployments end-to-end. From signed deal to production go-live and stabilization. No hand-offs.
Build production AI agents. Design and ship agent flows on our platform: prompt engineering, orchestration, edge cases, and the testing discipline that makes them production-grade.
Integrate into brownfield reality. Legacy systems, APIs, CRMs, telephony and customer infrastructure, while navigating security, compliance and incomplete requirements.
Debug in environments you don't control. When production misbehaves inside the customer's stack, you find it and fix it.
Push back with judgment. Challenge poor requirements, find creative alternatives, and align technical and business stakeholders behind them.
Be embedded with customers. On-site several days a week during active deployments, often as the most senior technical person in the room. On-site days carry dedicated compensation on top of your salary, and every production go-live carries a milestone bonus.

We are looking for a Senior Data Engineer to build and scale the data foundation behind platform and products. You will own complex data end-to-end - from ingestion and transformation through modeling, quality, observability, and production delivery.
This is a hands-on senior IC role for a strong builder who can solve difficult data problems independently, set a high technical bar, and collaborate closely with engineering, DS, and product. You will turn large, fragmented datasets into reliable, reusable capabilities that power every product.
Responsibilities
Build and own scalable data pipelines- Design, implement, and operate robust pipelines for high-volume structured and unstructured data, with validation, monitoring, lineage, and recovery built in.
Scale the platform for growth- A key near-term initiative is re-architecting the system to support a significantly larger customer base. You will own performance and cost-efficiency across pipelines and services, keeping reliability and operating costs under control as the platform scales.
Build across the stack- This is not a pipelines-only role. You will also write backend services and some frontend, including the internal backoffice the team runs on. We hire builders, not narrow specialists.
Own the core data tables- Own schema design and evolution, data contracts, and the modeling standards the team follows - naming, shared dimensions, normalization, documentation. Be accountable when a table is wrong, late, or drifting.
Level up the teams data work- Pair with and advise software engineers and data scientists on Spark, SQL, and modeling, and help turn notebook-grade code into production-grade pipelines.
Partner cross-functionally- Translate product, client, compliance, and business requirements into clear technical designs and dependable production systems.

We are looking for an AI Research Engineer to build the backbone of our autonomous security platform.
our company builds AI Digital Employees who help cyber teams close the execution gap. Our first AI Digital Employee, Alex, learns, understands and takes away the burden of Identity and Access Management (IAM) tasks - proactively completing the organization's cyber objectives.
About the Role:
You'll own technically difficult problems before the solution is known. Starting with a cybersecurity need, you'll study the domain, frame the technical challenge, investigate possible approaches, and build the solution through production. You'll have access to our company's shared agent infrastructure, but the role goes far beyond configuring existing components. Many use cases will require new reasoning methods, agent architectures, evaluation techniques, data strategies, or model behavior. This is a role for someone who enjoys applied research but is motivated by outcomes. The goal is not simply to prove that an idea can work, it is to make it useful and reliable for customers.
What You'll Do:
Design and implement AI solutions for complex cybersecurity workflows.
Develop new approaches for agent reasoning, planning, tool use, context management, decision-making, and recovery from failures.
Determine the right technical approach for each problem, combining models, algorithms, data, and conventional software where appropriate.
Define quality for each use case and create evaluations that measure correctness, task completion, consistency, safety, latency, and cost.
Analyze agent behavior deeply, identify the underlying causes of failures, and improve the relevant model, data, tool, or architecture.
Collaborate with cybersecurity experts, product teams, software engineers, and AI infrastructure engineers throughout development.

A senior, hands-on engineer joining a small team that is already shipping a live platform. You own whole features end to end, from a rough problem to something running in production that a hospital relies on. You will not wait for a spec, and you help set the bar for how we build as we grow.
What you'll own
Whole feature verticals across frontend, backend, data, and cloud: the auditable AI record, the workflows around it, and the ingestion behind them.
The reliability of what you ship, including incidents on your own code.
Local architecture and tooling calls, made without escalating everything.
The engineering-quality bar a small team compounds on: review, testing, and the habits later hires inherit.