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We are currently looking for senior- and staff-level ML engineers to work on research in areas such as:

Guided search and reinforcement learning for agentic systems
Reinforcement learning for reasoning models
Web-scale problem collection for training agents
Efficient model distillation
Some examples of what your responsibilities might include are:

Conducting experiments to figure out efficient ways to train a large language model on traces of interactions with various environments
Exploring methods of guided generation and search in the trajectory space
Coming up with ways to mine relevant data at web scale and figuring out efficient ways to use this data in model post-training
Conducting experiments with different reinforcement learning configurations in verifiable domains
Exploring methods to train AI agents on tasks with non-verifiable reward signals

Some directions we currently working on and which you can be a part of:
Advanced Fine-Tuning: Enhancing fine-tuning methodologies - both LoRA-based and full-parameter - for cutting-edge LLMs (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-4.7), focusing on both model quality and training efficiency.

Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. This involves building model training and evaluation pipelines in JAX for speculative decoding, experimenting with architectures (dense/MoE, auto-regressive/parallel), and deriving scaling laws to guide resource allocation.
Low Precision Training & Inference: Investigating low-precision (FP8, NVFP4/MXFP4) methodologies for supervised fine-tuning and reinforcement learning - spanning both inference and training - optimized for modern hardware

We are building a high-performance inference and fine-tuning platform designed to push foundation models to their hardware limits. Our mission is to maximize throughput, minimise latency, and optimise cost-per-token across tens of thousands of GPUs.



Some directions we are currently working on, and which you can be a part of:

Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. Squeezing the maximum performance for a wide range of LLM architectures at scale (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-5).
Inference engines support: Implement novel speculative decoding architectures, optimise components of various LLM designs (dense/MoE, autoregressive/parallel), and contribute to open-source inference engines.
Low Precision Training & Inference: Design and productionise low-precision (FP8, NVFP4/MXFP4) training and inference pipelines with measurable gains in throughput and cost-efficiency.

We're hiring an exceptional Applied AI Scientist to join our team!
In this role, you'll design and deploy enterprise-grade AI agents that drive real business impact across core functions - Sales, Marketing, Customer Success, Customer Support, Risk, and Finance. You'll be building agentic systems that execute complex, multi-step business workflows end-to-end. The ideal candidate is a hands-on builder with deep AI expertise who thrives on turning complex business problems into reliable, production-grade AI systems.
In This Role, You Will Be Responsible For:
Enterprise AI Agent Development: Designing, building, and deploying AI agents that automate and augment high-value workflows across business functions - including lead qualification and outreach (Sales & Marketing), churn reduction(Customer Success), and Ticket deflection (Support).
Agentic Architecture & Orchestration: Owning the end-to-end agent stack - from tool use, memory management, and multi-step planning to human-in-the-loop escalation patterns, guardrails, and audit trails suited for an enterprise fintech environment.
Knowledge Systems: Building and maintaining RAG pipelines that ground agents in proprietary knowledge - product documentation, customer data, financial records, and internal playbooks - using vector databases, hybrid search, and re-ranking.
Evaluation & Continuous Improvement: Defining agent evaluation frameworks that measure task completion, accuracy, hallucination rates, latency, and business impact - then iterating on agent behavior based on real usage data and stakeholder feedback.
Cross-Functional Collaboration: Partnering with stakeholders across the company to define agentic use cases, translate business requirements into technical features, and deliver products that create measurable ROI - from data preprocessing through product deployment.

We're looking for an Engineering Tech Lead to join the AI Core team and lead the technical direction of one of the core capabilities in AI Platform.
This role combines hands-on development with technical leadership, including architecture design, technical decision-making, mentoring engineers, and close collaboration with Product, Architecture, and cross-functional engineering teams.
This is an opportunity to help shape AI Platform, influence key technical decisions, and build the infrastructure that powers AI capabilities across the company. You'll work on challenging technical problems, collaborate with exceptional engineers, and have a significant impact on the future of AI .
In This Role, You Will Be Responsible For
Provide technical leadership and mentorship to the team.
Design scalable, reliable, and maintainable solutions.
Lead architecture discussions and drive technical decisions.
Conduct design and code reviews to ensure high engineering standards.
Lead end-to-end delivery of complex features and initiatives.
Collaborate closely with Product, Architecture, and other engineering teams.
Drive improvements in system quality, performance, security, and scalability.

We're looking for a hands-on Engineering Manager to lead a team of senior engineers owning this space end-to-end, from integration design through production ownership. You'll have real autonomy, a direct line from your team's work to customer outcomes, and room to shape how a technically deep domain is built and run.
What you'll own:
Lead and grow a team of engineers, creating clarity on priorities, raising the technical bar, and building real domain ownership
Own execution end-to-end: integration architecture, technical design, delivery predictability, and the operational health of systems that sync critical financial data at scale
Drive reliability and data correctness, strong debugging and sound judgment around sync semantics, idempotency, error handling, and recovery, plus calm decision-making when something breaks in production
Make sure your engineers understand the business and the ERPs behind the systems, the financial flows and edge cases, not just the code
Translate business goals and the realities of messy, heterogeneous ERP systems into pragmatic technical plans
Partner closely with Product, Operations, Support, and other engineering teams to deliver integrations that balance customer impact, time-to-value, and quality
Coach engineers through feedback, mentoring, and high standards

we are looking for a Senior Site Reliability Engineer (DevTools)
Your responsibilities will include:

Improving services based on user feedback

Building fault-tolerant, self-healing architecture

Finding ways to speed up our systems and reduce user friction

Modifying well-known closed- and open-source solutions Supporting our users

Ensure fault-tolerance, scale, and uninterrupted operations for the service.
Use cutting-edge cloud technology to solve a variety of infrastructure problems.
Implement and improve CI/CD processes.

We're looking for a Software Engineer to join our core engineering team and help build the infrastructure behind one of the fastest-growing AI APIs. You'll work on systems that handle massive scale across a distributed microservices architecture running on AWS. You'll ship fast, take ownership of critical systems, and solve hard infrastructure problems as we grow.

This is a great role for an engineer who loves building ambitious systems from scratch, and wants to tackle the kind of scale and complexity challenges typically reserved for much larger companies.

What Youll Do

Design and build high-performance distributed systems

Design and implement backend infrastructure and API endpoints

Build and optimize real-time data pipelines that process billions of events per day across distributed queues and stream processors

Improve performance, monitoring, and reliability across the stack

Own core systems and contribute to key architectural decisions

Help shape a strong engineering culture focused on velocity and quality

As a Software Engineer - Scraping Specialist, youll build the systems that power how AI interacts with the live web. Youll design and implement large-scale, intelligent web acquisition pipelines, making sense of an ever-changing online ecosystem and transforming it into structured, high-quality data.

This role blends technical understanding of browsers, networks, and automation with hands-on engineering. Youll work on adaptive systems that navigate modern websites, understand evolving web interaction ,detection systems, and gather meaningful information safely, efficiently, and at scale.

Your work will directly shape ability to provide AI agents with accurate, real-time knowledge of the world , bridging the gap between the open web and intelligent reasoning.

What Youll Do
Build distributed data acquisition systems that capture and structure the live web
Investigate and analyze browser internals, fingerprinting, and anti-automation systems to develop stealthy, adaptive orchestration layers
Prototype and deploy intelligent automation frameworks using Playwright, Puppeteer, and low-level browser control protocols (CDP)
Conduct hands-on research into network flows, JavaScript-based protections, and emerging web standards affecting automation
Collaborate with AI and infrastructure teams to integrate real-time web data into retrieval pipelines and LLM-powered agents
Translate deep technical insights into production-grade components, balancing research freedom with engineering rigor
Continuously evolve capabilities in resilience, speed, and authenticity of web interaction

We are looking for a Senior AI Software Engineer to build the agents that sit on top of our search platform: systems that take a user's intent, break it into steps, gather and verify information from the web, and return answers an agent can act on. You will work across applied research and engineering, designing how agents plan, call tools, retrieve, and reason so they accomplish open-ended tasks reliably and at scale.

This is a high-ownership role at the intersection of agent systems, retrieval, and product. You will turn frontier-model capabilities into dependable, production-grade agent behavior, and you will own that behavior end to end, from the prompts and tools to the evaluation that proves it works.

In this position, your responsibility will be to

Design and build AI agents that plan, retrieve, and reason over real-world information to complete open-ended tasks

Build the tool interfaces and context engineering that let frontier models use our search and other tools effectively

Mine and analyze usage data to build agents that learn and improve continually from how they are used

Turn new model capabilities into reliable product features, and own them from prototype to production

Define the evaluations, metrics, and guardrails that prove an agent is accurate, grounded, and safe

Improve agent quality across reasoning, planning, tool use, and grounding against real user tasks

Build the backend and infrastructure that run agents reliably under high volume

Collaborate with the search, ML, and product teams to make agent and platform capabilities reinforce each other