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Shape the developer-facing surfaces of our company- the CLI, dashboard, App Builder, and documentation. You will own full features from design to production across TypeScript, React, and Go.

Own the model plane end to end - vLLM inference, fine-tuning (GRPO/RL and LoRA), and the evaluation-and-promote pipeline that takes an owned model from checkpoint to production serving. We train and serve our own models on GPUs in our own cluster.

Own the surfaces developers touch every day - the company CLI, our SDKs, the Electron desktop IDE, and the docs. Make the path from zero to a deployed app feel effortless.

We are a fast-moving startup developing breakthrough digital printing technology for optical lens coatings. We are seeking a highly motivated and experienced Director of Mechanics & Automation to lead our multidisciplinary Automation group. This key leadership role holds disciplinary responsibility over the mechanics, software, control, and electrical engineering teams. The ideal candidate will thrive in a dynamic startup environment, driving the team forward with a strong sense of ownership, excellent interpersonal skills, and a collaborative mindset. You will oversee the development of cutting-edge technologies, manage the delivery of the software and control systems, and ensure successful technological implementations at customer sites.
Key Responsibilities:
* Multidisciplinary Leadership: Provide direct management and disciplinary leadership for the mechanics, software, control, and electrical engineering teams.
* Delivery Management: Lead and manage complex software and control plans from concept through development to successful deployment.
* Technology Development & Innovation: Drive the research, design, and development of new automation technologies to enhance our industrial equipment platforms.
* Customer Implementation & Support: Oversee the deployment and integration of new technologies, ensuring seamless implementation at customer site and providing ongoing top-tier support.
* Teamwork & Collaboration: Foster a culture of exceptional teamwork and cross-functional collaboration. Work closely with other R&D groups: System Engineering, Process, Materials, Integration & Testing as well as with Operations and Customer support.
* Execution & Ownership: Take full ownership of the Automation roadmap. Push projects forward proactively, overcome obstacles in a fast-paced environment, and lead the team to meet ambitious technological and business milestones.

What You'll Actually Be Doing
Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow by pushing the boundaries of AI development tools to deliver secure, optimized, and high-quality code.
Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale.
Contribute to building and maintaining the shared system context, an explicit repository of system designs, constraints, and standards that enables AI to operate accurately and reliably.
Critically evaluate code (human or AI-generated) for correctness, quality, security, and performance.
Build new and exciting components in an ever-growing and evolving market technology to provide scale and efficiency.
Develop high-quality, production-ready code that can be used by millions of users of our cloud platform.
Make design decisions on the basis of performance, scalability, and future expansion.
Work in a Hybrid Engineering model and contribute to all phases of SDLC including design, implementation, code reviews, automation, and testing of the features.
Build efficient components/algorithms on a microservice multi-tenant SaaS cloud environment.
Code review, mentoring junior engineers, and providing technical guidance to the team (depending on the seniority level).

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 AI product 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.

We're seeking a Staff engineer - AI Builder to lead the design and implementation of AI-native technical frameworks that redefine how large-scale systems are built, extended, and evolved.

This role is deeply technical and architecture-driven, focused on designing modular, extensible, and AI-first system foundations that enable scalable development with AI as a core engineering collaborator.

You will work across the full stack, building infrastructure where AI actively generates, extends, and maintains software components as part of the native development flow.

This is not about incremental AI integration - this is about architecting the frameworks that let AI scale software engineering 10x faster and smarter.

Responsibilities
Architect and lead the development of AI-native system frameworks that emphasize modularity, extensibility, and adaptive scalability.
Build platform primitives that enable dynamic AI-driven module and extension generation.
Design and implement developer workflows optimized for AI-assisted software development, embedding AI collaboration as a first-class design principle.
Define and codify AI-native engineering practices, patterns, and guidelines to elevate our software development lifecycle.
Establish and champion best practices for AI-assisted engineering across system design, code quality, testing, observability, and operational scalability.
Drive hands-on prototyping and iterative delivery across frontend, backend, and orchestration layers.
Provide technical leadership and mentorship, fostering an AI-first engineering culture across teams.
Partner closely with Product, AI, and Engineering teams to align system evolution with AI-native goals.

What will you do:
Develop and refine machine learning models and generative AI systems to automatically understand and categorize risks in Application Lifecycle Management systems.
Collaborate with product and engineering teams to integrate AI tools seamlessly into existing workflows, enhancing the proactive identification of security risks.
Analyze data from a variety of sources, ensuring that the AI systems adapt to new patterns and insights dynamically.
Participate in the ongoing development of our AI capabilities, pushing the boundaries of automated security risk assessments.
Communicate complex AI concepts and the benefits of proposed solutions to non-technical stakeholders.

As a Senior Back End Engineer, you will play a crucial role in designing, building, and optimizing the core backend systems that power our advanced AI-driven security tools. Your work will be instrumental in ensuring that our platforms are scalable, secure, and efficient, enabling our AI engines to process vast amounts of data, assess risks, and deliver actionable insights.


What you will do:
Design, develop, and maintain robust and scalable backend systems to support our Security's AI-driven security tools.
Collaborate with the AI, front-end, and product teams to integrate backend services with AI models and data processing pipelines.
Optimize the performance and reliability of backend services, ensuring low latency and high availability.
Implement and manage APIs that facilitate seamless integration with external systems and internal components.
Ensure that backend architecture adheres to security best practices, safeguarding sensitive data and maintaining compliance with industry standards.
Monitor, debug, and resolve any issues in the production environment to maintain system health and uptime.

We're looking for a smart and capable DevOps Engineer. You will be part of a small and agile team, working on well-built infrastructure, handling production workloads, building and maintaining infrastructure that supports continuous delivery. This job includes work across the devops stack, including agent development, CI/CD, networking, resource allocation, cloud architecture, security, developer experience, and more.
We work with enterprise customers, with the SLA and expectations that this dictates.
This position includes on-call shifts.
Our stack:
Kubernetes on AWS
Terraform and IAC
Github & Github Actions
Responsibilities
What will you DO:
Design and evolve our cloud multi-region architecture on AWS - networking, interconnects, IAM and high-availability topology - and express it entirely as code with Terraform, following GitOps as a first principle.
Build and own the CI/CD pipelines that plan, review, test and safely apply IaC changes - Policy-as-Code guardrails, drift detection and progressive rollout so infrastructure changes ship as confidently as application code.
Advance Platform-as-a-Product: build self-serve capabilities and paved paths so engineers can provision what they need
Strengthen our observability stack - metrics, logs, traces and alerting.
Troubleshoot and mitigate production incidents, participating in on-call rotations to ensure system health and stability.
Design and develop the next generation of AI agents to support higher pace and evolve the RnD productivity while maintaining security and engage the entire company to use AI for better solutions.

We are looking for a highly driven and experienced AI Engineer Team Leader to manage and mentor a core team of engineers while remaining 50% hands-on. In this critical role, you will lead the development of our enterprise Agentic AI platform, blending deep technical expertise in AI agent development with strong engineering leadership.
As a Team Leader, you will drive the technical agenda, plan sprints, and collaborate closely with Product Managers and cross-functional partners to turn powerful capabilities into reliable, safe, and fast product experiences. You must thrive in a high-pressure, dynamic startup environment characterized by a fast pace and frequent pivots, ensuring your team remains focused, agile, and aligned with business goals while executing at the highest level.
You won't just manage - you'll own agent workflows end to end, from design through evaluation, deployment into the tools employees use daily, and iteration in front of enterprise customers.
Responsibilities
Team Leadership & Management (50%): Manage, mentor, and grow a team of AI and software engineers. Oversee task allocation, sprint planning, and daily agile ceremonies.
Hands-On Engineering (50%): Actively code, design, and build multi-agent systems and orchestration - intent routing, planning, tool use, and coordination across specialized agents.
Technical Strategy & Architecture: Drive the technical agenda, architecture decisions, and best practices for developing scalable, LLM-driven applications grounded in our knowledge graph.
System Integration: Integrate LLMs with tool/function calling and protocols such as MCP to give agents controlled access to HCM systems, business logic, and workflows.
Production Excellence: Build evaluation harnesses, guardrails, and safety/bias checks. Optimize agents for latency, cost, and reliability at enterprise scale. Take agents from prototype to production - with monitoring, observability, and a fast iteration loop.
Cross-Functional Collaboration: Work intimately with Product Management, Data, and Design teams to translate customer needs into agent capabilities, define roadmaps, and prioritize tasks.
Agility & Execution: Lead the team through frequent pivots and rapid development cycles, ensuring high-quality delivery under pressure in a fast-paced startup environment.

We are looking for a DevOps Engineer to own and scale the infrastructure behind our AI systems.

What you'll do
Own cluster reliability, availability, monitoring, alerting, capacity planning, and incident response.

Configure, maintain, and troubleshoot GPU servers, networking equipment, switches, InfiniBand fabrics, storage, and related infrastructure.

Deploy, maintain, and improve infrastructure around Slurm, Kubernetes, and GPU workload orchestration.

Manage and improve our cloud environment, including compute, networking, storage, IAM, security groups, cost visibility, and production infrastructure.

Build, maintain, and improve CI/CD pipelines.

Work closely with engineers to understand infrastructure bottlenecks and improve development, training, inference, and deployment velocity.

Contribute to security best practices across cloud, network, CI/CD, access control, secrets management, and production systems.

We are looking for a Deep Learning Engineer to help design, implement, optimize, and scale the training and inference systems behind our models.

What you'll do
Collaborate with cross-functional teams to bring research breakthroughs into production environments.

Build tools, abstractions, and infrastructure that make model training, evaluation, and deployment faster and more reliable.

Analyze and overcome performance and scalability challenges in large-scale distributed training.

Debug complex issues across the full ML stack: data loading, model code, distributed execution, GPU utilization, memory usage, networking, checkpointing, and serving.

Work with modern training and inference frameworks such as Megatron, TorchTitan, PyTorch, and vLLM.

We are looking for a Product Engineer to own and ship the products that put this capability in the hands of engineers, data scientists, and researchers.

What you'll do
Own product features end-to-end: scope, build, ship, measure, and iterate - across CLI, IDE integrations, web surfaces, and the AI pipeline behind them.

Talk directly to users - engineers, data scientists, ML practitioners - and let their feedback shape what you build next.

Define what AI-powered code optimization and verification feels like as a developer experience: the interaction model, the feedback loops, the trust signals. There is no established playbook; you will create the conventions.

Craft interfaces that feel fast, focused, and effortless for technical users who have zero patience for friction.

Collaborate with researchers to translate model breakthroughs into polished, reliable tooling that developers can depend on in production.

Build evaluation infrastructure and product metrics that measure whether the tools are actually getting better.

Work directly with the founding team on product strategy and roadmap.

We are looking for someone with strong fundamentals, algorithmic problem solving capabilities, hands-on engineering skills, a collaborative mindset, and a desire for continuous learning. We value people who are comfortable moving between code, systems, algorithms, and problem domains.

What you'll do
Write production-grade code powering frontier AI systems.

Work on performance, scalability, and reliability of distributed systems.

Collaborate with researchers and engineers to turn ideas into systems that run.

Improve tooling, infrastructure, and developer workflows.