Member of Engineering (Pre-training / Data Research)
Follow the latest research related to Large Language Models (LLMs) and data quality, being familiar with relevant open-source datasets and models. Design and implement complex pipelines to generate large amounts of diverse data while optimizing available resources. Collaborate closely with teams such as Pretraining, Posttraining, Evals, and Product to ensure short feedback loops on the quality of models delivered. Suggest, conduct, and analyze data ablations or training experiments to improve the quality of generated datasets using quantitative insights.
Director of Technology & Systems
As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, while supporting end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and the resilient cloud infrastructure required for international government partners. You will own the production outcome by taking full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies. You will ensure full-stack integrity by overseeing the end-to-end health of the platform, ensuring seamless integration between the AI core and all full-stack components, from APIs to UI, to maintain a responsive and production-ready environment. You will build automated systems to monitor model performance and data drift across geographically dispersed environments, ensuring the right levels of reliability. You will manage the technical lifecycle within diverse regulatory frameworks, lead the response for production issues in mission-critical environments ensuring rapid resolution and building guardrails to prevent recurrence. You will translate deep technical performance metrics into clear insights for senior international government officials and partner with Engineering and ML teams to ensure lessons learned in the field directly influence the technical architecture and decisions of future use cases.
Research Engineer – Evals
Build the evaluation systems from scratch that measure whether Firecrawl's outputs are effective across scraping, crawling, extracting, and mapping. This includes designing metrics, building pipelines, curating datasets, and integrating evaluations into continuous integration and deployment to catch regressions before release. Design benchmarks that represent real customer data distribution including edge cases, and create the collection and labeling systems. Own LLM-as-judge pipelines by designing and validating automated judges for scoring extraction quality, understanding LLM evaluation failure modes, and building human review tooling. Collaborate with research engineers working on models and reinforcement learning to use evaluation metrics as training signals and feedback loops to improve models. Design, run, and communicate fast experiments that test meaningful hypotheses and enable clear decision-making across the team.
Machine Learning Engineer (Singapore)
Build and scale systems for ingesting, processing, and delivering large-scale video and multimodal data for model training. Own the full pipeline from raw content to curated, filtered, and training-ready datasets focusing on speed, reliability, reproducibility, and cost-efficiency. Design and scale distributed data pipelines for preprocessing, dataset generation, and repeated dataset refreshes. Own workflow orchestration, job scheduling, monitoring, and failure recovery for large-scale data processing jobs. Implement and maintain containerized pipeline infrastructure using Kubernetes or equivalent orchestration systems. Optimize cloud-based data storage and movement across providers (AWS, GCS, or Azure) for cost, throughput, and operational efficiency. Define and implement best practices for dataset storage layout, versioning, caching, retention, and access patterns. Design and implement curation pipelines for selection, filtering, and retention of video and image content for model training including image-text pair datasets. Build and improve VLM-based captioning and metadata generation workflows at scale across video and image data. Develop and apply quality and aesthetic scoring models, CLIP-based semantic filtering, and other signal-extraction approaches for data selection. Build tooling to support deduplication workflows at scale, including near-dedup and exact deduplication pipelines over large video corpora. Analyze dataset composition, identify quality issues, iterate on curation logic to improve training outcomes. Define and evolve standards for high-quality, training-ready video data across different training regimes.
Research Engineer, Training & Inference
Maintain and optimize the proprietary reinforcement learning (RL) training and serving infrastructure with total stack ownership, including the Python API to CUDA kernels, to achieve peak performance for foundation model workloads. Maximize throughput of the RL system from data generation to model training utilizing sharded multi-node training and inference algorithms. Optimize the inference stack for high-throughput RL and low-latency large language model (LLM) production traffic by tuning the inference engine, router, scheduler, and custom kernels if necessary. Identify and resolve performance bottlenecks in distributed clusters to ensure optimal throughput and memory efficiency for multi-billion parameter models, balancing memory constraints with compute-heavy training cycles.
Director, Forward Deployed Engineering
As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, while supporting end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and the resilient cloud infrastructure required for international government partners. You will take full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies. You will oversee the end-to-end health of the platform, ensuring seamless integration between the AI core and all full-stack components from APIs to UI to maintain a responsive and production-ready environment. You will build automated systems to monitor model performance and data drift across geographically dispersed environments, ensuring the right levels of reliability. You will manage the technical lifecycle within diverse regulatory frameworks. You will lead the response for production issues in mission-critical environments, ensuring rapid resolution and building guardrails to prevent recurrence. You will translate deep technical performance metrics into clear insights for senior international government officials. You will partner with Engineering and ML teams to ensure lessons learned in the field directly influence the technical architecture and decisions of future use cases.
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/Staff Machine Learning Engineer - Perception HD Mapping
Design and develop novel algorithms and machine learning models for 2D/3D machine perception and mapping in real-world environments. Contribute to large-scale, automated mapping pipelines. Serve as a technical leader on the team by maintaining coding and machine learning development best practices and making architectural decisions. Help set the vision for the team and build out technical roadmaps. Coordinate cross-functional initiatives and collaborate with engineers from Mapping, Perception, Planner, Simulation, Data Science, and more. Drive the use of metrics and tools to guide development, validate algorithms, and measure progress.
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.
Forward Deployed Engineer Intern
As an Applied Research Engineer at Labelbox, you will develop systems and methods to create, analyze, and leverage high-quality human-in-the-loop data for frontier AI model developers. This includes designing and implementing advanced systems that align human feedback into AI training processes such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO). You will work on techniques to measure and improve human data quality, develop AI-assisted tools to enhance the data labeling process, and investigate how different types of human feedback impact model performance and alignment. Your work will involve optimizing human feedback collection through novel algorithms, integrating breakthroughs into Labelbox's product suite to make human-AI alignment scalable, engaging with customers and the AI community to understand data needs and share best practices, publishing research, exploring new frontiers in human-AI collaboration, creating technical documentation, blog posts, and educational content, and driving industry innovation through these activities.
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