Researcher, Frontier Cybersecurity Risks
As a Researcher for cybersecurity risks, you will design and implement mitigation components for model-enabled cybersecurity misuse that span prevention, monitoring, detection, and enforcement, under the guidance of 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. Additionally, you will evaluate technical trade-offs within the cybersecurity risk domain, propose pragmatic and testable solutions, and collaborate with risk and threat modeling partners to align mitigation design with anticipated attacker behaviors and misuse scenarios. You are expected to execute rigorous testing and red-teaming workflows to stress-test the mitigation stack against evolving threats across different product surfaces and iterate based on the 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 and optimize designs for experimental validation; coordinate with lab-based protein engineers to plan and optimize the design process and validation strategy. Analyze and leverage experimental results to improve designs and increase success rates over validation rounds; collaborate with machine learning scientists to fine-tune and prompt models. Act as an effective interface between machine learning model development and experimental validation; capture bioengineering learnings and feedback to the machine learning unit and vice versa; foster a collaborative and innovative environment by proactively finding opportunities to innovate and create clarity and alignment between different units. Contribute to computational tools by helping improve the use, service, and integration of AI models through feedback to software engineers and the foundational machine learning unit; assist in improving data management systems and workflows. Maintain the highest scientific standards with publication-grade work; stay current on developments in synthetic biology; continue building understanding of generative AI and expanded areas of protein and cell biology; participate in knowledge sharing through organizing and presenting at internal reading groups; attend and present at conferences when relevant.
Machine Learning Researcher, Audio
As a Machine Learning Researcher at Bland, your responsibilities include building and scaling next-generation text-to-speech (TTS) systems by designing and training large scale models capable of expressive, controllable, and 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 for real-time, low-latency inference in production. You will advance speech-to-text modeling by building and fine-tuning large scale 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 real-world enterprise scenarios including structured extraction and conversational nuance. You will pioneer neural audio codecs by researching and implementing neural audio codecs that achieve extreme compression with minimal perceptual loss, exploring discrete and continuous latent representations for scalable speech modeling, and designing codec architectures that enable downstream generative modeling and controllable synthesis. Additionally, you will develop scalable training pipelines by curating and processing massive audio datasets across languages, speakers, and environments, designing staged training curricula and data filtering strategies, and scaling training across distributed GPU clusters focusing on cost, throughput, and reliability. You will run rigorous experiments by designing ablation studies to isolate the impact of architectural changes, measuring improvements using both objective metrics and perceptual evaluations, and validating ideas quickly through focused experiments that confirm or eliminate hypotheses.
Scientist I, Platform Development and Antibody Screening
The job requires industry experience as a research engineer in an AI-related company and involves working, learning, and teaching within a collaborative team focused on solving challenging problems.
Model Policy Manager, Chemical & Biological Risk
Design model policies that govern safe model behavior in an objective and defensible way, including determining how models should respond in risky or unsafe scenarios and defining what constitutes unsafe behavior while balancing safety and beneficial model capabilities. Develop taxonomies to inform data collection campaigns, model behavior, and monitoring strategies, balancing utility maximization with catastrophic risk prevention. Lead prioritization of safety efforts across the company for new model launches, addressing technical and business trade-offs. Develop a broad range of subject matter expertise while maintaining agility across various topics. Collaborate with many internal teams requiring high organizational acumen and confident decision making.
Applied Research - Team Lead
Lead the research, development, and deployment of AI agents for production systems. Collaborate closely with engineering and product teams to integrate AI capabilities into the product experience. Drive the full life-cycle of AI systems from conception through deployment, including building robust evaluation frameworks to measure and improve agent performance. Stay at the forefront of AI by exploring the latest advancements in agents, evals, and applied AI research. Provide strategic direction and mentorship while contributing directly to prototyping, building, and iterating on AI systems.
AI Research Engineer
Design and implement multi-agent and reinforcement learning (RL) approaches for agentic code generation and tool-use. Build research prototypes that integrate with nectar and collaborate to productionize successful results. Create evaluation suites including task specifications, pass/fail checkers, coverage, and cost/latency dashboards. Acquire and curate datasets from PDFs, logs, tables, and generate synthetic data when appropriate, while maintaining data cards and licensing. Analyze experiments using disciplined ablations, document results and decisions. Stay current on developments in LLM agents, RL (offline/online, RLHF/RLAIF), constrained decoding, and program synthesis.
Applied Legal Researcher
Develop and deliver subject-matter expertise to support AI research; work closely with engineering, product, and design teams to define and develop AI systems; build and improve AI systems through prompt engineering, fine tuning, and other techniques; build proprietary benchmarks and datasets to evaluate models and model systems; partner directly with clients to understand their workflows, identify pain points, and translate complex business and legal requirements into technical solutions.
Applied Legal Researcher
Develop and deliver subject-matter expertise to support AI research; work closely with engineering, product, and design teams to define and develop AI systems; build and improve AI systems through prompt engineering, fine tuning, and other techniques; build proprietary benchmarks and datasets to evaluate models and model systems; partner directly with clients to understand their workflows, identify pain points, and translate complex business and legal requirements into technical solutions.
Research Scientist, Human AI Interaction
As a Research Scientist, Human–AI Interaction, you will lead research at the intersection of Human–Computer Interaction, Large Language Models, and task-level benchmarking to define how AI systems support real human work. Responsibilities include leading research on jobs-to-be-done benchmarks for AI systems by defining task taxonomies grounded in real professional and economic activities, identifying meaningful task completion criteria, and translating qualitative work understanding into measurable benchmarks. Develop methods to measure human activity in AI-mediated workflows and design benchmarks to assess AI-as-a-collaborator/copilot. You will design and run empirical studies involving controlled experiments and field studies to measure task performance and capture fine-grained interaction traces, drive strategy for professional-domain AI benchmarks based on understanding domain-specific workflows, and build and prototype AI systems and evaluation infrastructure including LLM-powered copilots, benchmark harnesses, data pipelines, and human-in-the-loop evaluation interfaces. Collaborate closely with User Experience Research to leverage qualitative insights and translate ethnographic findings into formal research constructs. Publish and present your research regularly to advance the field of human-centered AI benchmarking at top-tier conferences.
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