AI Engineer

• Rethink the PM job when most users are agents.
• Talk to customers across the full spectrum.
• Own the problem statement and requirements behind every agent tooling bet.
• Decide what gets built, what gets deferred, and what gets cut.
• Define how each launch is measured before it ships.
• Keep engineering, design, and leadership aligned.

Product Manager – AI Platform

LLM Engineer

• Conducting experiments with LLMs: Explore and analyze the effectiveness of different architectures and techniques (SFT, RLHF, Adapters, etc.) to enhance the capabilities of AI models.
• Developing and implementing evaluation methodologies: Implement and maintain robust frameworks to assess the performance, accuracy, and user satisfaction of AI bots, including offline and online metrics.
• Optimizing models for inference: Improve the efficiency and speed of AI models during inference to ensure they meet the performance and scalability requirements for production environments.
• Collaborating with cross-functional teams: Work closely with data scientists, software engineers, and product managers to integrate AI solutions into the overall product pipeline.

Senior NLP/LLM Engineer

• Build production software with code and Supernal's proprietary platform, including backend services, data models, and CRUD applications
• Build and maintain integrations with external systems (APIs, webhooks, third-party tools, and data sources) that AI Employees can safely act on
• Design, implement, and deploy conversational agents, including multi-turn flows, state management, and tool usage
• Own end-to-end technical delivery for high-priority customer implementations, from architecture through production launch
• Translate customer requirements and SOWs into clear technical designs, execution plans, and deliverables
• Make and own architectural decisions across application design, API integrations, LLM orchestration, RAG design, and workflow decomposition
• Handle real-world voice system challenges including latency, interruptions, fallbacks, error handling, and failure recovery
• Write automated tests — unit tests for isolated logic and end-to-end tests for full system and user journey validation
• Apply solid error handling: distinguish retryable vs. fatal failures, surface meaningful error messages, and avoid silent failures
• Actively debug complex production issues across agent logic, prompts, integrations, and external dependencies
• Partner with delivery and product leadership to manage timelines, scope, and technical tradeoffs during implementation
• Review technical work for quality, scalability, and maintainability, setting a high bar for engineering excellence
• Define, document, and evolve best practices for building and delivering reliable AI Employees

Senior AI Engineer – Clients

• Build and productionize AI-powered product features across eCommerce, marketing, analytics, and customer workflows
• Design and implement agentic AI systems and LLM-powered workflows
• Develop, train, evaluate, and deploy machine learning models into production environments
• Work across the full ML lifecycle: experimentation, feature engineering, model evaluation, deployment, monitoring, and iteration
• Collaborate closely with product managers, backend engineers, analysts, and DevOps teams to bring AI systems into production
• Design scalable APIs and inference services for AI-driven functionality
• Improve and evolve our ML infrastructure, tooling, and AI-native development workflows
• Rapidly prototype ideas, validate hypotheses, and iterate based on real-world feedback and business impact
• Contribute to experimentation frameworks, A/B testing approaches, and production monitoring practices
• Stay current with the latest developments in LLMs, agentic systems, generative AI, and applied machine learning

Senior ML/AI Engineer

AI Research Scientist

• Conduct end-to-end research and engineering initiatives to advance post-training of agentic and tool-use models to achieve SOTA results.
• Drive broad, cross-cutting model improvements, including factuality, instruction adherence, tool/function use, multi-agent coordination, and reasoning calibration.
• Design and enhance large-scale post-training systems, including data pipelines, training workflows, evaluation frameworks, and benchmark infrastructure.
• Develop rigorous evaluation suites and diagnostic tools to assess model readiness for deployment.
• Strengthen feedback loops from real-world product usage, incorporating both explicit and implicit user signals into post-training.
• Collaborate with tooling, product, and training teams to improve the usefulness, reliability, and agentic capabilities of frontier models.
• Closely liaise with research, engineering and cross-functional teams to determine which integrations are production-ready for inclusion in major model releases.

AI Research Engineer – Agentic Post-training

• Owned the design and implementation of agentic AI systems
• Worked hands-on with the development team to bring intelligent, production-grade solutions to life
• Designed and iterated on multi-agent AI architectures using LangGraph and LangChain
• Defined integration standards for MCP servers and agent communication protocols
• Collaborated daily with engineers to implement and review AI components
• Advised on prompt engineering strategy, tool use design, and agent reliability
• Contributed to technical documentation and architectural decision records

GenAI Architect

Machine Learning Engineer

• Design, develop, and architect high-performance AI/ML services and pipelines in a cloud-based environment.
• Contribute to cutting-edge R&D initiatives, including service integration with emerging frameworks and platforms.
• Assist with the technical resolution of issues on mission-critical production systems, embracing the philosophy: "you build it, you own it, you run it."
• Think outside the box to devise solutions that scale, are fault-tolerant, easy to grasp, and elegant.

• Design and build backend and platform features (APIs, services, integrations, and data flows)
• Own scoped projects end-to-end: requirements clarification, implementation, testing, rollout, and follow-through
• Write automated tests — unit tests for isolated logic and integration tests for system behavior
• Apply solid engineering fundamentals: error handling, retries/backoff where appropriate, and clear interfaces between components
• Debug production issues using logs/metrics/traces; drive issues to resolution and add safeguards to prevent recurrence
• Improve reliability and maintainability through refactors, performance improvements, and better observability
• Communicate progress, tradeoffs, and risks clearly to engineering and cross-functional partners
• Contribute to team standards: code review, documentation, and best practices that keep builds predictable and supportable

Junior AI Engineer, Clients

• Lead the research, design, architecture, training and validation of advanced neural networks for vectorized mapping (e.g., MapTR), multi-camera BEV transformers, and multimodal fusion models to extract and model lane and route networks for both high-fidelity offline pipelines and real-time online mapping.
• Architect, design, and implement a production-grade lane and route network mapping stack, ensuring high-performance integration with upstream and downstream modules like Perception, Behavior, Policy, and Prediction.
• Drive major feature development from inception to deployment. This includes high-level architecture design, rigorous code reviews, automated testing, mentorship of junior engineers, and technical resolution.
• Own the end-to-end data strategy for the mapping domain, specifically focusing on lane and route networks. You will define data curation, auto-labeling, synthetic data, and active learning pipelines to capture and resolve long-tail scenarios.
• Develop robust metrics and evaluation frameworks for lane and route network accuracy, temporal consistency, and scaling across diverse Operational Design Domains (ODDs).
• Work independently with cross-functional teams to translate complex autonomy goals into clear software and system requirements.
• Collaborate with ML and Autonomy engineers to ensure the seamless deployment and validation of mapping features to the vehicle fleet.
• Stay at the research frontier by evaluating, adapting, and innovating cutting-edge techniques, including online vectorized HD map construction, end-to-end mapping models, and vision/fusion Foundation Models to deliver production-ready solutions.

Lead ML Engineer – Lane & Route Networking Mapping

• Lead the design and development of machine learning systems and LLM-based applications
• Fine-tune and adapt large language models for domain-specific use cases
• Build NLP pipelines to extract structured data from complex, unstructured documents
• Translate ambiguous product goals into clear technical and ML execution plans
• Work directly with product signals, user feedback, and backlog input to prioritize development work
• Build evaluation systems to measure model performance and detect regressions
• Design automated benchmarking pipelines for continuous model testing
• Define meaningful performance metrics tied to real-world outcomes
• Build feedback loops that use user corrections to continuously improve model performance
• Apply computer vision techniques to extract data from PDFs and scanned documents
• Develop AI agents that interact with web systems using automation tools such as Playwright or Selenium
• Work across system design, infrastructure, training, and inference pipelines
• Build scalable ML systems and supporting infrastructure from the ground up

Senior AI/ML Engineer

• Own features end-to-end — from task breakdown to production deploy
• Design and build server-side logic (REST and RPC APIs, services, integrations)
• Implement user interfaces — clean and fast, without over-engineering
• Set up CI/CD, containerisation, and feature-level infrastructure (DevOps basics)
• Participate in code reviews, technical discussions, and task decomposition

• Design and build performant, well-tested Python/FastAPI services that power AI-driven features and automations.
• Architect multi-step AI agent pipelines using frameworks such as LangGraph, CrewAI, or custom orchestration layers.
• Integrate LLM providers (OpenAI, Anthropic, open-source models) via APIs and SDKs.
• Connect and orchestrate external AI creative tools such as Arcads, Seedance, Runway and more into automated content pipelines.
• Identify, propose, and implement AI-native automations across the organization from content creation workflows to data enrichment, QA, customer support, and internal tooling.
• Collaborate closely with our Platform team to deploy and operate services on AWS.

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