AI Researcher
You will work across the model development loop, from research questions to training runs to evaluation. This includes designing and testing architecture changes and training regimes for large language models, running controlled experiments at scale and isolating causal effects, studying failure modes in reasoning, generalisation, robustness, and representation, shaping objectives, data mixtures, and optimisation choices that influence model behaviour, building and refining evaluations that measure capability and reliability, analysing training dynamics using logs, metrics, and model outputs, collaborating with ML systems engineers on distributed training and training operations, and writing clear internal notes that turn experimental results into design decisions. You will spend substantial time in code, training runs, logs, and evaluation outputs with the goal of clarity about what improves the model and why. You will work hands-on with code as a primary tool for thinking, moving between theory and implementation quickly and precisely, preferring controlled experiments over broad sweeps, using logs, metrics, and model behaviour to guide decisions, and working closely with engineering counterparts to scale and validate ideas.
Scientist/Sr Scientist, Display Technology (Contract)
The role involves working as a research engineer in an AI-related company, being enthusiastic and motivated, collaborating within a team to solve challenging problems, learning and teaching within the team, and operating in a hybrid working culture based on trust.
Member of Technical Staff: Agent DX Research
The role involves collaborating with Modal’s SDK team and other product engineers to build a framework and process for evaluating agent productivity. Responsibilities include defining quantitative objectives, designing systems to measure performance, translating results into product improvements, staying current with new developments in tools and workflows, and working with customers to understand their use of coding agents with Modal and identify areas for providing more value.
Research Scientist (Measurement and Evaluation)
Design and conduct evaluations of Abridge models and products; engage with external researchers and other stakeholders on designing and conducting research on ambient AI and research that leverages Abridge data; develop a user-centric and patient-centric mindset grounding research in empathy for providers and patients; collaborate with cross-functional product teams to ensure research is informed by current practices and product roadmap; write technical reports and give presentations to internal and external stakeholders; actively contribute to the wider research community by publishing original research in leading peer-reviewed venues; mentor research interns.
Research Scientist, PhD
Conduct original research to advance the state of the art in machine learning and artificial intelligence. Design, implement, and evaluate novel algorithms, models, or training approaches at large scale. Collaborate with researchers and engineers to translate research insights into production systems and real-world applications.
ML Research Scientist (Health & Sensing)
The ML Research Scientist will use AI and Machine Learning to transform sensor data into personalized intelligent health and fitness experiences by working closely with a cross-functional R&D and production team to prototype and ship solutions. Projects include advancing the Pod’s adaptive thermoregulation system using reinforcement learning and closed-loop control, developing multimodal health foundation models integrating physiology and environmental context from Pod signals, wearable sensors, and contextual data, and building high-fidelity physiological simulators to model how daily behaviors affect sleep and readiness. The scientist will tackle problems with a systems approach and make data-driven decisions to deliver the best products to users.
Researcher, Automated Red Teaming
This role leads the Automated Red Teaming (ART) effort by building scalable, research-driven systems that continuously discover failure modes in models and mitigations, translating findings into actionable, production-facing improvements to reduce expected harm by identifying high-leverage weaknesses early and reliably. The responsibilities include owning the research and technical direction for automated red teaming across catastrophic risk areas initially focused on automated classifier jailbreak discovery (cyber and bio), automated bio threat-development elicitation, and Chain-of-Thought monitoring evasion probing. The role requires tight partnership with vertical risk teams to define threat models, prioritize targets, and implement mitigations; collaboration with the Classifiers team to convert discovered attacks into training data, evaluations, and robustness improvements; and working with product, engineering, and safety stakeholders to ensure outputs are operationally useful.
Researcher, Frontier Cybersecurity Risks
As a Researcher for cybersecurity risks, you will design and implement mitigation components for model-enabled cybersecurity misuse, including prevention, monitoring, detection, and enforcement, under guidance from senior technical and risk leadership. You will integrate safeguards across product surfaces in partnership with product and engineering teams to ensure protections are consistent, low-latency, and scalable with usage and new model capabilities. You will evaluate technical trade-offs within the cybersecurity risk domain such as coverage, latency, model utility, and user privacy, proposing pragmatic and testable solutions. You will collaborate closely with risk and threat modeling partners to align mitigation design with anticipated attacker behaviors and high-impact misuse scenarios. You will execute rigorous testing and red-teaming workflows, stress-testing the mitigation stack against evolving threats including novel exploits, tool-use chains, and automated attack workflows, iterating based on findings.
Computational Protein Design
Leverage proprietary generative AI models to design proteins for experimental validation by analyzing protein design problems based on functional requirements, biochemistry, structural biology, and sequence homology; generate designs and optimize them for experimental validation; coordinate with lab-based protein engineers to plan and optimize the design process and validation strategy. Leverage proprietary data to improve models by analyzing experimental results to improve subsequent design rounds and collaborating with machine learning scientists to fine-tune and prompt models. Serve as an effective interface between machine learning model development and experimental validation, capturing bioengineering learnings and feedback to the machine learning unit and fostering collaboration and innovation to create clarity and alignment between different units. Contribute to computational tools by helping improve the use, service, and integration of AI models, feeding back to software engineers and foundational machine learning units, and improving data management systems and workflows. Work to the highest scientific standards, stay informed about developments in synthetic biology, continue developing knowledge of generative AI and protein and cell biology, participate in knowledge sharing by organizing and presenting at internal reading groups, and attend and present at conferences when relevant.
Machine Learning Researcher, Audio
As a Machine Learning Researcher at Bland, the responsibilities include foundational research and development across core components of the voice stack such as speech-to-text, large language models, neural audio codecs, and text-to-speech. The role involves building and scaling next-generation text-to-speech (TTS) systems by designing and training large-scale TTS models for expressive, controllable, human-sounding output, developing neural audio codec-based TTS architectures for efficient and high-fidelity generation, improving prosody modeling, question inflection, emotional expression, and multi-speaker robustness, and optimizing real-time, low-latency inference in production. It also includes advancing speech-to-text modeling by building and fine-tuning large scale automatic speech recognition (ASR) systems robust to accents, noise, telephony artifacts, and code switching, leveraging self-supervised pretraining and large-scale weak supervision, and improving transcription accuracy for enterprise scenarios. Responsibilities extend to pioneering neural audio codecs by researching and implementing codecs achieving extreme compression with minimal perceptual loss, exploring discrete and continuous latent representations, and designing codec architectures for downstream generative modeling and controllable synthesis. Additionally, the role demands developing scalable training pipelines by curating and processing massive audio datasets, designing staged training curricula and data filtering strategies, and scaling training across distributed GPU clusters with focus on cost, throughput, and reliability. Finally, it involves running rigorous experiments, designing ablation studies to isolate architectural impacts, measuring improvements via objective metrics and perceptual evaluations, and validating ideas quickly with focused experiments.
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