ML Researcher Jobs

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As a Cohere Research Intern, you will conduct cutting-edge machine learning research, training and evaluating production large language models. You will focus on research projects aimed at making models better understood, safer, more reliable, more inclusive, and more beneficial for the world. You will disseminate your research results through the production of publications, datasets, and code. Additionally, you will contribute to research initiatives that have practical applications in Cohere's product development. The internship involves collaborating with the Modelling Safety team on implementing novel research ideas related to fairness, safety (including for multiple languages, dialects, and cultural contexts), robustness, generalisation, interpretability, safety for agents with complex read/write actions, and safety for codegen. The project details and topic will be designed collaboratively between the intern and the team, with a goal to publish a paper in a top venue and contribute to open science. The internship may be remote or onsite, with no relocation or housing provided.

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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.

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As a senior AI Researcher for reinforcement learning, you will shape and improve the underlying reinforcement learning methodology, maintain a high-quality training codebase, and conduct large-scale experiments to improve performance benchmarks. Your responsibilities include conducting large-scale LLM training runs, analyzing evaluation scores, proposing and implementing improvements, staying at the forefront of reinforcement learning research by identifying and iterating on novel approaches, optimizing RL training loops to scale training infrastructure, and collaborating cross-functionally with other post-training teams to convert feedback into actionable training signals for measurable improvements in performance.

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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.

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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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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.

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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.

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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.

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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.

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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.