ML Scientist
Contribute to the development of ML models across multiple modalities. Work across the ML stack including model architectures, data curation, model evaluation, training and inference infrastructure, research, and experimentation. Select promising approaches from the literature to pursue and create new approaches where necessary to achieve unique goals.
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.
Senior Research Engineer
As a Senior Research Engineer at Decagon, you will be responsible for building industry-leading conversational AI models, taking them from idea to production. Your role includes leading research and engineering efforts to improve core conversational capabilities in production such as instruction following, retrieval, memory, and long-horizon task completion. You will build and iterate on end-to-end models and pipelines focusing on quality, efficiency, and user experience. Collaboration with platform and product engineers to integrate new models into production systems is essential. Additionally, you are expected to break down ambiguous research ideas into clear, iterative milestones and roadmaps.
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.
Staff Research Engineer
On the Research team, you will be responsible for building AI systems that can perform previously impossible tasks or achieve unprecedented levels of performance. You will design and implement state of the art methods for instruction tuning and information retrieval. You will develop models for customer support tasks that exceed the performance of closed source models, experiment with small open-source models to drive order of magnitude reductions in latency across channels, and break down ambiguous research ideas into clear, iterative milestones and roadmaps. Engineers own their work end-to-end, making real impact by diving deep into complex system challenges, building elegant solutions that scale to millions of users, and creating automation that prevents problems before they happen.
Member of Technical Staff - Alignment Lead
Drive the entire alignment stack, including instruction tuning, RLHF, and RLAIF, to push the model toward high factual accuracy and robust instruction following. Lead research efforts to design next-generation reward models and optimization objectives that improve human preference performance. Curate high-quality training data and design synthetic data pipelines addressing complex reasoning and behavioral gaps. Optimize large-scale reinforcement learning pipelines for stability and efficiency, enabling rapid model iteration cycles. Collaborate closely with pre-training and evaluation teams to create feedback loops that translate alignment research into generalizable model improvements.
HR Operations Partner
Develop novel architectures, system optimizations, optimization algorithms, and data-centric optimizations that significantly improve over state-of-the-art. Take advantage of the computational infrastructure of Together to create the best open models in their class. Understand and improve the full lifecycle of building open models; release and publish insights such as blogs and academic papers. Collaborate with cross-functional teams to deploy models and make them available to a wider community and customer base. Stay up-to-date with the latest advancements in machine learning.
MEP Manager, Data Centers
Develop novel architectures, system optimizations, optimization algorithms, and data-centric optimizations that significantly improve over state-of-the-art. Take advantage of the computational infrastructure of Together to create the best open models in their class. Understand and improve the full lifecycle of building open models; release and publish insights through blogs, academic papers, etc. Collaborate with cross-functional teams to deploy models and make them available to a wider community and customer base. Stay up-to-date with the latest advancements in machine learning.
Revenue Operations Intern (Summer 2026)
Develop novel architectures, system optimizations, optimization algorithms, and data-centric optimizations that significantly improve over state-of-the-art. Take advantage of the computational infrastructure of Together to create the best open models in their class. Understand and improve the full lifecycle of building open models; release and publish insights such as blogs and academic papers. Collaborate with cross-functional teams to deploy models and make them available to a wider community and customer base. Stay up-to-date with the latest advancements in machine learning.
Senior Financial Analyst, GTM
The Applied Research Scientist is responsible for developing state-of-the-art tools for correcting, improving, and enhancing written English using various NLP, ML, and DL technologies. They will productize and ship these features into Superhuman's product offerings used by millions of users daily. The role requires staying up-to-date with the latest research trends that could improve the product, contributing to the research strategy and technical culture of the company, and attracting professionals in the industry to build a best-in-class research team that creates a state-of-the-art writing and communication assistant.
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