דרושים » תוכנה » 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.

We are 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.

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 a Senior MLOps Engineer to be a core driver in how our product empowers security teams. You will be expected to deeply understand customer needs and translate them directly into product features that deliver real value. You'll own key parts of our frontend stack, drive key architectural decisions, and turn complex security data into clear, actionable business insights.

As we scale our AIDR product and expand deeper into model-driven security intelligence, we are looking for a Senior MLOps Engineer to own the infrastructure, tooling, and operational foundations that power our NLP and LLM training, evaluation, and deployment workflows.

You will architect and operate the systems that enable us to train, fine-tune, deploy, and monitor models at scale making ML reliable, fast, cost-efficient, and production-ready.

This is a high-visibility, high-impact role where you will partner closely with DevOps, Backend, Data, and Product to establish world-class ML infrastructure from the ground up.

What Youll Do

Build & Scale ML Pipelines
Design, build, and maintain pipelines for training, fine-tuning, evaluating, and deploying NLP and LLM models across GPU and CPU environments.
Establish LLM-Focused CI/CD
Implement automated CI/CD workflows for ML models, including benchmarking, testing, performance gating, and production deployment.
Optimize Runtime & Inference
Select and optimize serving frameworks for low-latency, high-throughput inference, ensuring reliability and scalability.
Own ML Infrastructure
Manage training environments, experiment tracking, model registries, artifact versioning, and distributed training systems.
Operational Excellence
Monitor and optimize production models for performance, cost efficiency, availability, and observability.

We seek a versatile Senior Software Engineer who is passionate about performance optimization and generative AI. Our team brings the latest research in LLM inference - from novel decoding strategies to quantization schemes - into production across our hardware lineup, from large data center servers to powerful edge devices. We work on the most advanced architectures in the field, with a focus on NVIDIA's own.

What you'll be doing:

Implement and optimize inference algorithms for LLM and omnimodal architectures, including hybrid Mamba-Transformer and mixture-of-experts models.

Profile inference pipelines using NVIDIA's profiling and simulation tools. Correlate simulation predictions against real hardware across data center and edge devices.

Write and tune GPU kernels (CUDA, Triton) for operators like fused MoE layers, SSM state updates, and quantized GEMMs.

Solve distributed inference problems: expert parallelism, communication-compute overlap, collective tuning, multi-node deployment.

Build production-grade software inside major open-source libraries - vLLM, SGLang, Dynamo, FlashInfer.

Own optimization features end-to-end, from scoping through delivery, collaborating with research, product, and engineering teams worldwide.

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.

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.