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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.

Own the technical integrations that connect us with retailers systems, making sure data flows are smooth, secure, and ready for scale.

Responsibilities
Manage data integrations with POS, ERP, and other systems.
Define and document technical requirements for new onboardings.
Work with APIs, SFTP, and database connections.
Troubleshoot and resolve integration issues with engineering.
Collaborate with retailers IT teams during setup.

Every camera and sensing system we ship is used, every day, by hundreds of millions of people who simply expect it to work. Ensuring our systems are worthy of that expectation is this team's goal. We challenge products through the physical world to prove it, and to feed real-world evidence back into system design - measuring and analyzing the performance of multi-disciplinary optical sensing systems. This role is the analytical core of that work. You will define what we measure and why, mine large volumes of measurement data for the effect that actually matters, and take a question that has never been answered to a defensible answer. It is highly collaborative: you will work directly with module, optics, algorithm and system teams, and your conclusions will be the basis of their decisions. You will work alongside our lead system engineer as a peer, with problems allocated between you according to where the issue actually sits. We are looking for someone who genuinely enjoys digging into data until it gives up what it knows, and who wants to take that on with us.
Responsibilities
Own the characterization of system performance end to end - from risk and failure mode mapping through methodology definition, execution, analysis and reporting
Define metrics, testing & measurement methodology
Mine large data sets to separate isolate performance root causes
Correlate physical measurement to human perception - where the HW meets the user
Build the analysis capability the work requires - pipelines, tooling methods
Present and defend findings to module, optics, algorithm and system teams

Join our team in a pivotal role where you'll own the entire IP-level netlist generation and timing convergence journey from synthesis to sign-off.
You'll drive synthesis, UPF power intent, scan insertion, and external IP integration while architecting timing constraints for both standard and complex custom designs that ensure sign-off quality from day one.
Working at the intersection of multiple disciplines, you'll partner closely with RTL designers to deeply understand design intent and clock architecture, collaborate with CAD teams to shape and optimize cutting-edge flows, and team with Physical Design engineers to achieve flawless timing sign-off.
We're seeking an innovative thinker who brings fresh perspectives to timing analysis methodologies and proactively identifies and resolves timing challenges to eliminate pessimism and accelerate convergence, ultimately making their mark on next-generation chip design.

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 seeking talented, creative and disciplined engineers to join the best-in-class team that plays a significant part in the development of new silicon for our eco-systems by engaging in a dynamic, highly collaborative environment with the focus on the following goals.

Deep understanding of components/modules within wireless communication systems and the corresponding calibration and impairment reduction techniques - Develop methods/algorithms to alleviate hardware induced impairments by calibration, cancellation and other applicable techniques - PHY firmware architecture and design, including calibrations, real-time processing flows, collaboration with RF hardware, microcode, and MAC layer.

Develop highly efficient designs delivering outstanding performance, optimizing power and memory footprints while meeting hard-realtime latency requirements.

Debug real-time systems with limited visibility, working hands-on with development boards and lab equipment.

Collaborate with Radio, MAC, and Systems engineering teams to architect, define and implement innovative algorithms, calibrations, and protocols.

Support pre-silicon validation efforts, including simulation, emulation, and FPGA prototyping.

Develop SoC test and calibration capabilities for chip, module, and product factories.

Support post-silicon bringup, validation, performance characterization, system integration and debugging. - Repeatedly measure and optimize results using various test harnesses - Automate calibration and verification processes

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.

As a member of our Physical Design team in this highly visible role, you will directly own implementation and verification of design partition(s) / IPs (netlist to delivery of our final GDS) for a highly complex SoC utilizing state of the art process technology.
* Implementation - Block level PnR, floor-planning, clock, power planning and distribution.
* Verification and Analysis - Static Timing closure using commercial tools, Physical Verification as well as Electrical/Power Analysis (EM / IR-Drop / Xtalk / noise )

We are looking for a mid-level Data Engineer, experienced with building data pipelines using Python and Airflow for structured data and relational databases. The ideal candidate is a self-directed learner, takes ownership of the pipelines they build, and cares about data quality as much as feature delivery.





Key Responsibilities:

Own our data pipelines, including maintenance and adding new features.
Take charge of data integrity and completeness, including writing and maintaining tests.
Improve process efficiency and optimize runtime performance.
Own schema design and evolution across our data pipelines.

We're looking for a Senior Algorithm Developer with a strong algorithmic background in both classical and deep learning methods.

What Youll Do
Develop, release and improve algorithms and models for recommending the best machining operations that balance customer valued KPIs such as machining time, machining tolerances and tool wear.
Dive deep into the CNC machining domain to cultivate intuition to inform your algorithm development decisions.
Collaborate with the Product team to discover and drive improvements in our human-AI interface.
Own your models in production. Validate your solutions against real-world machining scenarios and customer parts.
Research novel approaches to continuously improve our core technology.

We are looking for a Data Products Engineer to take full ownership of our internal product suite - the full-stack applications, AI agent tools, and automation pipelines that power teams across the entire company every day. You will step into an established ecosystem and take the reins. Your mission is to maintain, evolve, and build the next generation of these internal tools so every department can move faster and make smarter, data-driven decisions.

Reporting directly to the VP of CS & Data, this is a high-autonomy, tech-lead role. You wont just write code; you will make architectural decisions, solve complex domain challenges with smart software, and shape how the entire company interacts with data.

How will your day-to-day look?
Own the Internal Product Suite: Lead the development of our core full-stack applications. You will build and scale the central platforms and dashboards that give Product, CS, Sales, and executive leadership visibility into product usage, account health, business performance and more.
Advance the Internal AI Layer: Design, deploy, and refine custom agentic AI workflows - enabling teams across the organization to query complex B2B and product data naturally and conversationally.
Engineer Smart Automations: Architect workflow automations and custom APIs that pipe downstream data directly into the operational tools our teams use every day.
Bridge Data and Software: Partner seamlessly with Data Ops and Data Analysts to transform raw data models into highly usable, intuitive user interfaces and automated actions.
Run the Infrastructure: Own the end-to-end DevOps lifecycle for our internal applications, managing deployment, containerization, observability, and CI/CD pipelines.
Drive Technical Strategy: Act as the technical anchor for internal tools. You will translate high-level product and business challenges from company leaders into shipped, reliable software.