AI Applied Research Scientist Jobs

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Design and implement worst-case demonstrations that concretely reveal AGI alignment risks for stakeholders, focusing on high-stakes use cases; develop adversarial and system-level evaluations based on these demonstrations and promote their adoption across OpenAI; create automated tools and infrastructure to scale automated red-teaming and stress testing; conduct research on failure modes of alignment techniques and propose improvements; publish influential internal or external papers that impact safety strategy or industry practice; collaborate with engineering, research, policy, and legal teams to integrate findings into product safeguards and governance; and mentor engineers and researchers to foster a culture of rigorous, impact-oriented safety work.

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As a Research Engineer / Research Scientist on the Alignment team, you will design and implement alignment experiments focused on intent following, honesty, calibration, and robustness. You will train and evaluate models using reinforcement learning and other empirical machine learning methods. Your role includes developing evaluations for failure modes such as hallucination, instruction-following failures, reward hacking, covert actions, and scheming. You will study methods that encourage models to verify their behavior and report shortcomings honestly, including confession-style training objectives. You will build monitoring and inference-time interventions that ensure compliant behavior or surface model issues to users or downstream systems. Additionally, you will investigate how alignment methods scale with model capability, compute, data, context length, action length, and adversarial pressure. You will integrate successful techniques into model training and deployment workflows, produce externally publishable research when results advance the broader science of alignment, and collaborate with researchers and engineers across post-training, reinforcement learning, evaluations, safety, and product-facing teams.

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Formulate new machine learning problems in RF sensing and spectrum understanding. Design experiments and evaluation approaches reflecting real operating conditions such as domain shift, changing interference, and varying sensors and platforms. Build models for structured, noisy, and partially observed signal environments. Improve robustness across propagation, interference, and low-visibility waveform conditions. Optimize models for throughput, latency, and deployment constraints. Move promising research into a release path for real systems through proofs-of-concept, realistic validation, and conversion into maintainable, deployable code. Use field performance to inform the development of the next generation of models and tooling. Work across the lifecycle of research and deployment including data and evaluation design, experimentation, model development, release readiness, and iteration based on real-world outcomes. Collaborate closely with embedded, hardware, and mission teammates, influencing how machine learning capability is built as the company scales.

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You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house. You will develop and extend the state-of-the-art in uncertainty quantification and uncertainty calibration. This involves understanding the AI systems built at Helsing, interfacing with them, and evaluating their robustness in real-world and adversarial scenarios. You will contribute to impactful projects and collaborate with people across several teams and backgrounds.

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As a Staff Research Engineer at Decagon, you will lead research and engineering efforts to improve core conversational capabilities in production, including instruction following, retrieval, memory, and long-horizon task completion. You will build and iterate on end-to-end models and pipelines that optimize for quality, efficiency, and user experience. You will partner with platform and product engineers to integrate new models into production systems. Responsibilities also include breaking down ambiguous research ideas into clear, iterative milestones and roadmaps, mentoring other researchers and engineers, setting technical direction, and establishing best practices for applied research and engineering.

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The candidate will be responsible for defining operational domains and evaluating the reliability of AI capabilities developed in-house. They will develop and extend state-of-the-art methods in uncertainty quantification and uncertainty calibration. This role involves understanding the AI systems built by the company, interfacing with them, and assessing their robustness in both real-world and adversarial scenarios. The candidate will contribute to impactful projects and collaborate with team members across different teams and backgrounds.

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Develop and refine AI-driven clinical tools across notes, risk adjustment (HCC capture), clinical decision support, and prior authorization using clinical expertise and prompt engineering; define what "clinically meaningful" output looks like for each product area, including acceptable error rates, failure modes, and quality thresholds; collaborate with cross-functional teams including engineers, data scientists, and clinicians to integrate medical insights into AI models; write, review, and optimize prompts to improve AI model performance in clinical contexts; build and refine evaluation tools to streamline medical documentation quality assessment including hallucination detection, omission detection, and medication safety checks; translate clinical needs into technical specifications and data models; define clinical guardrail metrics and baselines and own pre/post evaluation requirements for any model update that affects clinical output; contribute to product development, revenue cycle management, and broader business initiatives; monitor clinical guideline update cycles and flag when product behavior needs to evolve based on new standards or payer policies.

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Participate in research and development within the Models team, Data Research team, or Agent team in H Company's research lab as a graduation internship; work on building foundational models powering agentic technology, advancing multimodal intelligence through large-scale models, and defining new learning algorithms and agent paradigms for autonomous AI systems.

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Work on the Multimodal Language Action model by exploring novel discrete action tokenization and flow matching approaches, building on MotionLM, FAST, and other models. Train models at the billion+ scale using millions of miles of proprietary Zoox driving data. Gain experience and insight into training Multimodal Language Action models at scale. Contribute to publishable research that could be integrated into Zoox vehicles.

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During this internship, the intern will lead the development of a multi-modality (vision, LiDAR, Radar, and language), temporal foundation encoder to support 3D object detection & tracking, 3D segmentation (occupancy), and live maps. The research will aim to significantly improve system performance on long-tail events and rare classes by utilizing a large-capacity foundation model to learn rich representations across different sensor modalities. The project also aims to improve perception in adverse environmental conditions such as medium to heavy rain and fog, reduce false positives on water splashes or dust particles, achieve long-range sensing for highway driving, and build robustness to occlusion. The role includes exploring novel directions such as tri-modal foundation models with self-supervised pre-training, radar-language grounding for zero-shot detection, efficient sensor fusion via sparse cross-attention, or integrating 3D Gaussian Splats for dynamic agent geometry and streaming sparse Gaussian occupancy prediction.