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The role is simple to state and hard to do well: make that optimization save more tokens without ever costing output quality or breaking a provider's cache. It's a research-heavy engineering role - you form theories from real traffic and ship the ones that prove out on live customer requests.

Why this role
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.

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.

Build your own pipeline. That means targeted outbound to engineering and platform leaders, starting in the Israeli ecosystem and moving into the US.
Run deals start to finish: demo the product, handle the technical conversation yourself, and carry evaluations through to a signature.
Own the GTM engine: the outbound, the deal process, the playbooks, the collateral.
Get Anyray in front of the right people, on LinkedIn, at meetups and events, and through customers who'll vouch for us.
Grow into leading the whole GTM function. The motion you build is the one you'll run.

Were seeking a Conversational Quality Specialist to help shape and continuously improve voice-based experiences across our platform. This role sits at the intersection of conversation design, AI interaction quality, and cross-functional collaboration. You'll work with product, sales, marketing, and NLP teams to ensure our AI conversations are effective, natural, and engaging.

What Youll Do

Design Engaging Interactions

Create, edit, and optimize prompts, scripts, and dialogue flows that feel intuitive, realistic, and aligned with human communication patterns.

Enhance Demo Experiences

Collaborate with sales and marketing to craft compelling demo conversations that showcase the power of our AI.

Evaluate Conversation Quality

Review real user-AI conversations to assess performance, highlight successes, and uncover improvement opportunities for our core conversation engine.

Define Quality Metrics

Develop and implement conversation quality frameworks and metrics to evaluate effectiveness, user engagement, and learning outcomes.

Support Multi-Language Rollout

Oversee and advise on multilingual implementation to ensure high-quality experiences across regions.

Stay Ahead of the Curve

Keep up with best practices in conversation design, LLM-native UX, voice AI trends, animated avatars, user interaction models and any new technology that is expected in conversations.

We're looking for a Senior Applied Researcher and Team Lead to help shape how our conversational agents are designed, evaluated, and shipped reliably at production scale.



Key Responsibilities

Partner directly with the Applied Research Team Lead to define and evolve team process, technical standards, and the quality bar for agent behavior.
Take real user experience and data through the full arc, from concept to a reliable agent design to a shipped, production-grade product.
Own or share ownership of the standing function of assessing AI tools, models, and providers for reliability and fit.
Raise the technical bar across a team spanning research-leaning grad students to engineers newer to AI, and turn personal expertise into shared tooling and standards the whole team can use.
Operate pragmatically inside current infrastructure constraints while flagging where structural fixes are needed.

We're looking for a Full Stack Team Lead, backend-oriented, someone who can go deep on Python and distributed systems, stay comfortable enough in React to guide a team that owns both ends of the stack, and help shape how a small, fast-moving engineering team works and grows.



Key Responsibilities

Own backend architecture, APIs, and infrastructure end to end, with hands-on involvement in frontend work where needed.
Lead and grow a small team of engineers across different experience levels, from technical mentoring to career development.
Set the tone for incident response, on-call availability, and production reliability.
Adapt the team's direction as the company grows, and take the lead in shaping how the team works rather than just reacting to change.
Build a culture of open feedback, where the team's input genuinely shapes the roadmap.
Represent the team's needs to company leadership, including on how and where people work.
Participate in code reviews and design discussions, and mentor team members directly.

Were looking for a top-tier Senior Full Stack Developer whos passionate about building world-class user experiences and loves working at the intersection of beautiful frontends and powerful backends. If you're a React expert who enjoys diving into backend challenges (especially in Python), and you thrive in a fast-paced, high-impact environment - this role is for you.

Key Responsibilities

Architect, develop, and maintain sophisticated web applications using React with performance, responsiveness, and UX excellence in mind
Own and contribute to backend services, APIs, and infrastructure - Python and Django are a strong bonus
Solve tricky bugs, tackle performance bottlenecks, and drive continuous improvements across the stack
Work closely with product managers and designers to turn complex ideas into elegant, user-friendly solutions
Ensure top-notch speed, scalability, and accessibility across the app
Participate in code reviews, design discussions, and mentor team members with your experience and insight