Machine Learning Systems Research Engineer - Post-training Algorithms

OverviewPluralis Research is at the forefront of Protocol Learning, innovating a decentralized approach to train and deploy AI models that democratizes access beyond just well-funded corporations. By aggregating computational resources from diverse participants, we incentivize collaboration while safeguarding against centralized control of model weights, paving the way for a truly open and cooperative environment for advanced AI.We are seeking a talented Machine Learning Training Platform Engineer to design, develop, and scale the core infrastructure that powers our decentralized ML training platform. In this role, you will have ownership over essential systems including infrastructure orchestration, distributed computing, and service integration, facilitating ongoing experimentation and large-scale model training.ResponsibilitiesMulti-Cloud Infrastructure: Create resource management systems that provision and orchestrate computing resources across AWS, GCP, and Azure using infrastructure-as-code tools like Pulumi or Terraform. Manage dynamic scaling, state synchronization, and concurrent operations across hundreds of diverse nodes.Distributed Training Systems: Design fault-tolerant infrastructure for distributed machine learning, including GPU clusters, NVIDIA runtime, S3 checkpointing, large dataset management and streaming, health monitoring, and resilient retry strategies.Real-World Networking: Develop systems that simulate and manage real-world network conditions—such as bandwidth shaping, latency injection, and packet loss—while accommodating dynamic node churn and ensuring efficient data flow across workers with varying connectivity, as our training occurs on consumer nodes and non-co-located infrastructure.

Why Join Achira?Become part of an elite team comprising scientists, machine learning researchers, and engineers dedicated to transforming the predictability of the physical microcosm and revolutionizing drug discovery.Explore uncharted territories: we are on a mission to innovate next-generation model architectures that merge AI with chemistry.Engage in large-scale operations: harness massive computational resources, extensive datasets, and ambitious objectives.Take ownership of significant projects from inception to deployment on large-scale infrastructures.Thrive in a culture that values precision, speed, execution, and a proactive mindset.About the PositionAt Achira, we are committed to developing state-of-the-art foundation models that tackle the most complex challenges in simulation for drug discovery and beyond. Our atomistic foundation simulation models (FSMs) serve as world models of the physical microcosm, incorporating machine learning interaction potentials (MLIPs), neural network potentials (NNPs), and various generative models.We are seeking a Machine Learning Research Engineer (MLRE) who excels at the intersection of advanced machine learning and rigorous research methodologies. Collaborate closely with our research scientists to design and enhance intelligent training systems that propel us beyond contemporary architectures into a new era of ML-driven molecular modeling.Your mission is clear yet ambitious: to establish the foundational frameworks for training atomistic simulation models at scale. This entails a deep dive into architecture, data, optimizers, losses, training metrics, and representation learning, all while constructing high-performance systems that maximize the potential of our models. In this role, you will be instrumental in creating a blueprint for pretraining FSMs similar to today’s large-scale generative AI systems, making a significant impact on drug discovery.At Achira, you will have the chance to pioneer models that comprehend and simulate the physical world at an atomic level, achieving unprecedented speed and accuracy.

At Scale AI, we collaborate with leading AI laboratories to supply high-quality data and foster advancements in Generative AI research. We seek innovative Research Scientists and Research Engineers with a strong focus on post-training techniques for Large Language Models (LLMs), including Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and reward modeling. This position emphasizes optimizing data curation and evaluation processes to boost LLM performance across text and multimodal formats. In this pivotal role, you will pioneer new methods to enhance the alignment and generalization of extensive generative models. You will work closely with fellow researchers and engineers to establish best practices in data-driven AI development. Additionally, you will collaborate with top foundation model labs, providing critical technical and strategic insights for the evolution of next-generation generative AI models.

OverviewPluralis Research is at the forefront of innovation in Protocol Learning, specializing in the collaborative training of foundational models. Our approach ensures that no single participant ever has or can obtain a complete version of the model. This initiative aims to create community-driven, collectively owned frontier models that operate on self-sustaining economic principles.We are seeking experienced Senior or Staff Machine Learning Engineers with over 5 years of expertise in distributed systems and large-scale machine learning training. In this role, you will design and implement a groundbreaking substrate for training distributed ML models that function effectively over consumer-grade internet connections.

Artificial Intelligence is increasingly becoming a pivotal element across all sectors of society. At Scale AI, we are committed to accelerating the evolution of AI applications. For nearly a decade, we have been the premier AI data foundry, propelling groundbreaking advancements in areas such as generative AI, defense applications, and autonomous vehicles. Following our recent investment from Meta, we are intensifying our efforts to develop advanced post-training algorithms that are essential for sophisticated agents in enterprises worldwide.The Enterprise ML Research Lab is at the forefront of this AI revolution, leveraging a suite of proprietary research, tools, and resources to support our enterprise clients. As a Staff Machine Learning Research Engineer focusing on Agent Post-training, you will be instrumental in creating our next-generation Agent Reinforcement Learning training platform. Your work will enable the training of top-tier Agents that deliver state-of-the-art results in real-world enterprise applications.You will incorporate cutting-edge research into our training framework, empowering ML Research Engineers on the Enterprise AI team to deploy use cases ranging from next-generation AI cybersecurity firewalls to training foundational healthtech search models. If you are passionate about shaping the future of the GenAI movement, we welcome your application!

Company Overview:At Specter, we are pioneering a software-defined control plane for the physical realm, beginning with safeguarding American enterprises through comprehensive monitoring of their physical assets.Our innovative approach leverages a connected hardware-software ecosystem built on advanced multi-modal wireless mesh sensing technology. This breakthrough enables us to reduce the deployment costs and time for sensors by a factor of 10. Our ultimate goal is to establish a perception engine that provides real-time visibility of a company’s physical environment and facilitates autonomous operations management.Co-founders Xerxes and Philip are dedicated to empowering our partners in the rapidly evolving landscape of physical AI and robotics. Join our dynamic and rapidly expanding team comprised of talents from Anduril, Tesla, Uber, and the U.S. Special Forces.Position Overview:We are seeking a Perception AI Engineer who will be instrumental in transforming sensor data pipelines into actionable insights for our clients.Key Responsibilities:Implement and deploy a range of deep-learning models, including vision, vision-language, and large language models, within our sophisticated distributed perception system.Design and scale a production-ready data collection, labeling, and model retraining platform.Lead the design of a multimodal software user interface.

At Exa, we are revolutionizing the way AI applications access information by building a cutting-edge search engine from the ground up. Our team is dedicated to developing a robust infrastructure capable of crawling the web, training advanced embedding models, and creating high-performance vector databases using Rust to facilitate seamless searches.As part of our ML team, you'll be instrumental in training foundational models that refine search capabilities. Our mission? To deliver precise answers to even the most complex queries, effectively transforming the web into an incredibly powerful knowledge database.We are seeking a talented Machine Learning Research Engineer who is passionate about crafting embedding models that enhance web search efficiency. Your responsibilities will include innovating novel transformer-based architectures, curating extensive datasets, conducting evaluations, and continuously improving our state-of-the-art models.

OpenAI is hiring a Software Engineer for Post-Training Research in San Francisco. This position centers on improving the performance and capabilities of advanced machine learning models after their initial training phase. Role overview Work closely with a skilled team to explore new ways of strengthening AI systems. The focus is on researching and developing methods that push the boundaries of what these models can achieve once training is complete. Collaboration Expect to contribute to ongoing research efforts and share insights with colleagues who are passionate about advancing AI. Teamwork and knowledge exchange are key parts of this role. Location This position is based in San Francisco.

Join Scale's innovative Large Language Model (LLM) post-training platform team, where you will contribute to the development of our internal distributed framework designed specifically for LLM training. This sophisticated platform empowers Machine Learning Engineers (MLEs), researchers, data scientists, and operators to perform rapid and automated training and evaluation of LLMs. Additionally, it underpins the training framework for our data quality evaluation pipeline.Scale is at the forefront of the Artificial Intelligence sector, acting as a vital provider of training and evaluation data, as well as comprehensive solutions for the entire machine learning lifecycle. In this role, you will collaborate closely with Scale’s ML teams and researchers to construct the foundational platform that supports all our ML research and development initiatives. Your work will involve building and optimizing this platform to facilitate the training, inference, and data curation of next-generation LLMs.If you are passionate about driving the future of AI through groundbreaking innovations, we invite you to connect with us!

Join Scale AI's ML platform team (RLXF) as a Machine Learning Research Engineer, where you will play a pivotal role in developing our advanced distributed framework for training and inference of large language models. This platform is vital for enabling machine learning engineers, researchers, data scientists, and operators to conduct rapid and automated training, as well as evaluation of LLMs and data quality.At Scale, we occupy a unique position in the AI landscape, serving as an essential provider of training and evaluation data along with comprehensive solutions for the entire ML lifecycle. You will collaborate closely with Scale's ML teams and researchers to enhance the foundational platform that underpins our ML research and development initiatives. Your contributions will be crucial in optimizing the platform to support the next generation of LLM training, inference, and data curation.If you are passionate about driving the future of AI through groundbreaking innovations, we want to hear from you!

Achira is seeking a Machine Learning Research Engineer to help improve workflows and systems for artificial intelligence projects. This position is based in the San Francisco office. Role overview This role centers on developing and refining machine learning pipelines. The focus is on efficient deployment and scaling of AI models in production environments. Collaboration with colleagues from different disciplines is a key part of the work, aiming to bring forward new ideas and solid practices in machine learning systems. What you will do Design and optimize machine learning workflows for better performance and scalability Work closely with cross-functional teams to implement improvements in AI systems Support the deployment process, helping ensure models run efficiently in real-world settings Location This position is based at Achira's San Francisco office.

Anthropic is looking for a Research Engineer with a focus on Machine Learning, particularly Reinforcement Learning (RL) Velocity. This position involves collaborating with a team to design, build, and refine machine learning systems. Much of the work centers on experimenting with new ideas and advancing AI research. What you will do Work alongside researchers and engineers to develop and optimize machine learning models Explore new methods in reinforcement learning to accelerate progress Contribute to projects that push the boundaries of AI capabilities Location and travel This role offers flexibility to work remotely, with some required travel. Anthropic maintains offices in San Francisco, CA and New York City, NY.

About Our TeamAt OpenAI, we are pioneers in the field of artificial intelligence, committed to driving innovation and shaping a future where AI benefits everyone. We seek passionate and visionary Research Engineers to become part of our Applied Voice Team. In this role, you'll engage in transformative research on speech models, translating these insights into real-world applications that can revolutionize industries, enhance human creativity, and tackle complex challenges.About the RoleAs a Research Engineer on OpenAI's Applied Voice Team, you will collaborate with some of the most talented professionals in AI. You will be responsible for designing and developing cutting-edge speech models, including speech-to-speech, transcription, and text-to-speech functionalities. Your work will help translate groundbreaking research into practical solutions for B2B applications, APIs, and ChatGPT AVM. If you are eager to make AI more accessible and impactful, this is your opportunity to leave a lasting legacy.Key Responsibilities:Innovate and Build: Conceptualize and create advanced machine learning models that address real-world challenges, transforming OpenAI's research into AI applications with significant impact.Collaborate with Experts: Partner with software engineers, product managers, and deployed engineers to understand intricate business challenges, respond to customer needs, and deliver AI-driven solutions. Join a vibrant team environment where creativity and ideas flourish.Optimize and Scale: Develop scalable data pipelines, enhance models for improved performance and accuracy, and ensure readiness for production. Contribute to high-tech projects that demand innovative methodologies.Learn and Lead: Stay at the forefront of developments in machine learning and AI by participating in code reviews, sharing insights, and exemplifying high-quality engineering practices.Make an Impact: Oversee and maintain deployed models to ensure they consistently provide value. Your contributions will significantly influence the role of AI in benefiting individuals, businesses, and society as a whole.Ideal Candidate Profile:Master's or PhD in Computer Science, Machine Learning, or a related discipline.A minimum of 2 years of professional experience in engineering roles within technology and product-focused organizations (internships excluded).

At Thinking Machines Lab, our mission is to empower humanity by advancing collaborative general intelligence. We strive to build a future where everyone has access to the knowledge and tools essential for making AI work effectively for their unique objectives.Our team comprises scientists, engineers, and innovators who have contributed to some of the most widely adopted AI products, including ChatGPT and Character.ai, as well as notable open-weight models like Mistral and popular open-source projects such as PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleThe Post-Training Researcher position is pivotal to our roadmap. It serves as a crucial connection between raw model intelligence and a system that is genuinely beneficial, safe, and collaborative for human users.This role uniquely combines fundamental research with practical engineering, as we do not differentiate between these functions internally. Candidates will be expected to produce high-performance code and analyze technical reports. This position is ideal for individuals who relish both deep theoretical inquiry and hands-on experimentation, aiming to influence the foundational aspects of AI learning.Note: This position is classified as an 'evergreen role', meaning we continuously accept applications in this research domain. Given the high volume of applications, an immediate match for your skills and experience may not always be available. However, we encourage you to apply; we regularly review submissions and reach out as new opportunities arise. You are welcome to apply again after gaining more experience, but we ask that you refrain from applying more than once every six months. Additionally, specific postings for singular roles may be available for distinct projects or team needs, in which case you are welcome to apply directly in conjunction with this evergreen role.What You’ll DoDevelop and Optimize Recipes: Refine post-training recipes, encompassing various datasets, training stages, and hyperparameters, while assessing their impact on multiple performance metrics.Iterate on Evaluations: Engage in a continuous process of defining evaluation metrics, optimizing them, and recognizing their limitations. You will be accountable for enhancing performance metrics and ensuring they are meaningful.Debug and Analyze: During the fine-tuning of training configurations, you may encounter results that appear inconsistent. You will be responsible for troubleshooting and cultivating a deeper understanding to apply to subsequent challenges.Scale and Investigate: Assess and expand the capabilities of our models while exploring potential improvements.

About Scale AI At Scale AI, we are dedicated to propelling the advancement of AI applications. Over the past eight years, we have established ourselves as the premier AI data foundry, supporting groundbreaking innovations in fields such as generative AI, defense technologies, and autonomous vehicles. Following our recent Series F funding round, we are intensifying our efforts to harness frontier data, paving the way toward achieving Artificial General Intelligence (AGI). Our work with enterprise clients and governments has enhanced our model evaluation capabilities, allowing us to expand our offerings for both public and private evaluations. About the ACE Team The Agent Capabilities & Environments (ACE) team, a vital part of Scale’s Research organization, unites customer-focused Researchers and Applied AI Engineers. Our primary mission is to conduct research on agent environments and reinforcement learning reward signals, benchmark autonomous agent performance in real-world contexts, and develop robust data programs aimed at enhancing the capabilities of Large Language Models (LLMs). We are committed to creating foundational tools and frameworks for evaluating models as agents, focusing on autonomous agents that interact dynamically with a wide range of external environments, including code repositories and GUI interfaces. About This Role This position sits at the cutting edge of AI research and its practical applications, concentrating on the data types necessary for the development of state-of-the-art agents, including browser and software engineering agents. The ideal candidate will investigate the data landscape required to propel intelligent and adaptable AI agents, steering the data strategy at Scale to foster innovation. This role demands not only expertise in LLM agents and planning algorithms but also creative problem-solving skills to tackle novel challenges pertaining to data, interaction, and evaluation. You will contribute to influential research publications on agents, collaborate with customer researchers, and partner with the engineering team to transform these advancements into scalable real-world solutions.

Company OverviewEcho Neurotechnologies is a pioneering startup in the Brain-Computer Interface (BCI) sector, dedicated to revolutionizing the lives of individuals with disabilities through advanced hardware engineering and artificial intelligence solutions. Our vision is to develop innovative technologies that empower users, restoring autonomy and enhancing their quality of life.Team CultureWe pride ourselves on cultivating an inclusive and dynamic team of skilled professionals who are passionate about their work. Our startup environment encourages ownership of impactful decisions and fosters continuous learning and collaboration, where every contribution is essential to our collective success.Job SummaryWe are on the lookout for a talented Machine Learning Research Engineer specialized in speech modeling to join our innovative team. The successful candidate will leverage ML/AI methodologies to create and refine adaptable speech models aimed at brain-computer interface applications, ultimately making a difference in the lives of patients facing severe disabilities. Candidates should possess significant expertise in speech modeling, feature engineering, time-series analysis, and the development of custom ML models.Key ResponsibilitiesDesign and evaluate diverse model architectures and strategies to enhance the accuracy and resilience of models for interpreting speech from brain activity.Investigate and implement cutting-edge speech features and representations within neural-decoding frameworks, informed by speech science and functional neurophysiology.Create pipelines for generating personalized and naturalistic speech from both text and brain activity inputs.Develop algorithms to analyze both intact and compromised speech signals, identifying biomarkers linked to various diseases and disabilities.Collaborate within a tight-knit team to build models, define R&D workflows, and translate scientific discoveries into practical applications.Contribute to best practices ensuring reliability, observability, reproducibility, and scientific rigor across the R&D landscape.Maintain well-documented, versioned code, analysis pipelines, and results for maximum interpretability and reproducibility.

About the TeamJoin the innovative Post-Training team at OpenAI, where we focus on refining and elevating pre-trained models for deployment in ChatGPT, our API, and future products. Collaborating closely with various research and product teams, we conduct crucial research that prepares our models for real-world deployment to millions of users, ensuring they are safe, efficient, and reliable.About the RoleAs a Research Engineer / Scientist, you will spearhead the research and development of enhancements to our models. Our work intersects reinforcement learning and product development, aiming to create cutting-edge solutions.We seek passionate individuals with robust machine learning engineering skills and research experience, particularly with innovative and powerful models. The ideal candidate will be driven by a commitment to product-oriented research.This position is located in San Francisco, CA, and follows a hybrid work model requiring three days in the office each week. Relocation assistance is available for new employees.In this role, you will:Lead and execute a research agenda aimed at enhancing model capabilities and performance.Work collaboratively with research and product teams to empower customers to optimize their models.Develop robust evaluation frameworks to monitor and assess modeling advancements.Design, implement, test, and debug code across our research stack.You may excel in this role if you:Possess a deep understanding of machine learning and its applications.Have experience with relevant models and methodologies for evaluating model improvements.Are adept at navigating large ML codebases for debugging purposes.Thrive in a fast-paced and technically intricate environment.About OpenAIOpenAI is a pioneering AI research and deployment organization dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We are committed to pushing the boundaries of AI capabilities while prioritizing safety and human-centric values in our products. Our mission is to embrace diverse perspectives, voices, and experiences that represent the full spectrum of humanity, as we strive for a future where AI is a powerful ally for everyone.

Achira is seeking a Machine Learning Research Engineer to join its San Francisco office. This position centers on developing new machine learning solutions alongside a team of experienced researchers and engineers. The work directly supports ongoing research and development, shaping the company’s approach to AI technology. Role overview This role focuses on creating and improving machine learning models. Collaboration is central: expect to work closely with colleagues who bring a range of expertise. The team values initiative and the ability to handle complex technical challenges. What you will do Develop and refine machine learning algorithms for research projects Work with a group of experts to test and validate new approaches Contribute to the direction of AI research within the company Who we’re looking for Experience or strong interest in machine learning research Comfort working on complex problems in a collaborative setting Motivation to contribute to advances in AI technology

Join Baseten as a Post-Training Research Engineer and contribute to groundbreaking advancements in machine learning and AI. In this role, you will leverage your engineering skills to analyze and enhance models post-training, ensuring optimal performance and efficiency.