Head of ML
Define and drive a coherent vision for leveraging data to build automation products in surveying and design, translate this vision into a technical roadmap and execute it to advance product capabilities, build and grow the machine learning team including hiring and structuring as the organization scales, mentor ML engineers and researchers by providing technical direction and career growth guidance, stay hands-on by reviewing designs, code, and architecture to maintain credibility and connection with the team, and partner with product and engineering leadership to align research investments with product strategy and customer needs.
Applied ML Researcher (Force Fields and Simulation)
In this role, you will train, fine-tune, and distill machine learning force fields and research and develop novel ML force field architectures suited to production simulation workloads. You will integrate these models into public and in-house high-performance simulators and develop training and inference architectures for large-scale training, data generation, and simulation. You will distribute these workloads via Ray to scale across compute infrastructure and build modular systems so components can be reused across many kinds of chemistry. Additionally, you will build an active learning system that closes the loop between simulation, data generation, and training, develop interfaces that make the system easy for domain scientists to use and extend, and collaborate closely with computational chemists on density functional theory (DFT) data generation and validation.
Senior Design Producer
As a modeling lead for the AI lab, the primary responsibilities include defining the technical roadmap for the team and supporting the modeling needs across the organization. The role involves defining and establishing best practices to manage the model life cycle from data acquisition to deployment, building tools and platforms to support building and deploying machine learning models on devices with specific constraints, and working closely with different teams to translate user needs into specific modeling requirements. The position also requires defining and driving the AI Lab technical strategy in support of HP’s AI roadmap, making decisions regarding models, runtimes, inference engines, and optimization, leading device AI strategy including tasks such as model compression, quantization, distillation, and hardware-aware optimization across CPUs, GPUs, NPUs, and TPUs, architecting and evolving tooling and platforms for the full model lifecycle including evaluation, deployment, and monitoring, establishing standards and evaluation frameworks to ensure high-quality and safe Gen AI models in production, partnering with cross-functional leaders to align technical direction with product and hardware strategy, mentoring a small group of senior engineers, and operating as a hands-on technical leader who sets direction and moves quickly.
Member of Engineering (Reinforcement Learning)
Research and experiment on ways to improve reasoning and code generation for LLMs. Own the full experiment life cycle from idea to experimentation and integration. Keep up with the latest research, and be familiar with the state of the art in LLMs, RL, and code generation. Translate research ideas into clean, reusable codebases that other researchers can build on. Design, analyze, and iterate on data generation and training of LLMs. Implement and iterate on RL training pipelines that scale reliably across domains. Diagnose training instabilities and failures, debug RL runs and propose mitigation methods. Write high-quality, reproducible and maintainable code.
Manager of Technical Staff, Sovereign AI
As the Manager for the Sovereign AI Modelling team, you will manage a team of scientists and engineers, fostering a culture of high-performance, innovation, and continuous learning. You will stay up-to-date with the latest research in large language models (LLMs) and related fields, lead scalable strategies to train frontier models, and collaborate with cross-functional teams across modelling, forward-deployed engineering, and solutions architecture. The role is hands-on and research-driven, involving designing and implementing novel research ideas, shipping state-of-the-art models to production, and maintaining deep connections to academia and government. You will dive into the latest literature on LLMs, experiment with frontier models, and lead a team of talented engineers and researchers to build scalable, production-ready solutions.
Research, Mid-Training
The role involves owning late-stage training decisions that shape model capabilities, including designing and iterating on high-quality data mixtures for late-stage and annealing training runs, developing methods for sourcing, filtering, and weighting data to enhance model performance, driving targeted improvements in coding, mathematics, and reasoning via curated data strategies and training interventions, developing and evaluating synthetic data pipelines for scalable training signal generation, researching and optimizing multi-stage learning rate schedules and compute allocation, implementing methods to extend effective context length without hurting short-context performance, building evaluations to distinguish real capability improvements from benchmark overfitting, and measuring how mid-training interventions scale with compute and data while developing new approaches when existing methods reach limits. The role crosses traditional pre-training and post-training boundaries and encompasses both research and engineering responsibilities.
Senior Machine Learning Scientist
The Senior Machine Learning Scientist will train, evaluate, and iterate on ML models and agentic systems for customer feedback, including owning custom fine-tuning pipelines. They will run experiments end-to-end, track results rigorously, and make recommendations on what to ship, iterate, or retire. The role involves building and maintaining LLM-powered features such as retrieval pipelines, reranking systems, insight agents, data mining agents, and automated taxonomy generation. The scientist will design and run robust evaluation frameworks including building test sets, defining metrics, evaluating non-deterministic systems, handling class imbalance, and automating checkpoint comparisons. They will improve and extend semantic search and retrieval methods, write production-quality code, and collaborate closely with Engineering on productionisation, model serving, data pipelines, and monitoring. The role includes working with Product and Commercial teams to translate business needs into practical ML solutions and supporting client evaluations and accuracy benchmarking. Additionally, the scientist will mentor team members, review code and research, and integrate relevant advances from literature into the product.
Senior Applied AI Manager
The Senior Applied AI Manager is responsible for owning the strategy and execution for AI science at Oumi. This includes setting the applied science agenda, building and leading the team, and being accountable for the science quality of every feature shipped on the platform. The role covers the full model development lifecycle, including data strategy, pre-training and post-training methodology, evaluation science, and production deployment, as well as developing agentic systems that automate and improve each stage. The manager works closely with the CEO and product leadership to translate company strategy into a concrete AI science roadmap and executes it with a team of ML engineers and applied researchers. Responsibilities include defining and driving the research and engineering roadmap, recruiting and managing a high-performing team, leading experimentation across the training stack, owning the data side of model development, designing evaluation frameworks and automated feedback loops, researching and developing agent-based systems for the training lifecycle, partnering with infrastructure and product teams to ensure reliable feature deployment, and contributing to open source and community collaborations.
AI Evaluation Engineer
Design and implement evaluation pipelines to measure the performance and reliability of AI models, develop automated testing frameworks to assess model outputs at scale, analyze model performance using both traditional statistical metrics and AI-specific evaluation methods, evaluate AI systems built on modern architectures such as LLM-based applications and Retrieval-Augmented Generation (RAG), identify potential issues related to accuracy, hallucinations, bias, safety, and model drift, conduct adversarial testing to uncover vulnerabilities and ensure safe model behavior, collaborate with engineering and AI teams to improve prompt design, model outputs, and system performance, monitor model performance in production, and help define best practices for AI evaluation and observability.
Senior/Staff Machine Learning Engineer - Perception Offline Driving Intelligence
As an engineer in the Offline Driving Intelligence (ODIN) team at Zoox, the responsibilities include developing advanced multimodal large language models to enhance environmental understanding for robotaxis, designing model architectures and training techniques using sensor inputs and large scale data, driving end-to-end machine learning solutions from research to production using Zoox's data pipelines and infrastructure, collaborating with perception, planning, safety, and systems teams to integrate models into the vehicle's decision-making pipeline, and validating and optimizing solutions using real-world driving scenarios to contribute directly to the safety and reliability of Zoox's autonomous system.
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