Find your next AI engineering role.

OpenAI

The Human Flourishing team is dedicated to ensuring AI systems understand and advance human flourishing, aligning AI capabilities with human values and long-term goals. This role involves measuring and improving models' understanding of human values, supporting user agency, and helping users achieve their long-term objectives. The team also focuses on scaling tangible impact in domains like education and mental health, working across research, product, and partnerships to benefit a large user base. Recent work includes Beneficial RL and the MentalHealthBench benchmark.

$380k - $500k

Anthropic

Anthropic is seeking individuals for a paid, fixed-term research residency program, lasting between six weeks and six months. This program is designed for experienced professionals, such as professors in quantitative disciplines, technical founders, or staff-level engineers, who are looking to transition into full-time work focused on ensuring AI systems are safe and beneficial for humanity. While a background in machine learning is not strictly required, candidates should be motivated by AI safety and excited about empirical AI research. The residency offers a scoped project with direct mentorship from Anthropic researchers, access to Claude models and GPU compute, and connections to the AI safety and security research community.

$350k - $500k

The Machine Learning Center of Excellence (MLCOE) within JPMorgan Chase's Chief Data & Analytics Office (CDAO) partners across the firm to develop and deploy Machine Learning solutions for complex business challenges. The CDAO leverages data and AI/ML technologies to generate insights, drive decision-making, develop new products, improve productivity, and enhance risk management. As a Time Series and Reinforcement Learning Summer Associate, you will join a team of technologists and researchers exploring cutting-edge ML research and applying advanced techniques to J.P. Morgan's data assets. You will collaborate directly with business stakeholders to drive a data-led transformation. The internship program includes project work, mentorship, and speaker series, with potential for full-time offers upon successful completion.

Together AI

We are seeking a reliability-minded software engineer to manage the production operations of our Kubernetes-native control plane, which provisions and runs our GPU inference fleet. This platform team develops the manifest-driven API and controllers that enable the inference team to define their needs, such as cluster configurations, model deployments, and capacity adjustments, and ensures these are reconciled. Your role will be to maintain the reliability of this system in production, addressing issues like InfiniBand problems during node communication, diagnosing faults in hero-model and LoRA serving that generic health checks miss, and enhancing automated self-healing to permanently resolve recurring failure modes. The primary goal is to ensure the decoupling remains effective even during failures and to maintain fleet efficiency without introducing new instabilities. You will be responsible for inference serving SLOs and error budgets, leading incident response, and implementing systemic fixes based on postmortem analyses. Additionally, you will contribute to the reliability aspects of capacity efficiency, recognizing that misconfigured defragmentation or bin-packing can pose production risks. We value candidates with hands-on production reliability experience in large-scale distributed or GPU-serving systems, proficiency in troubleshooting InfiniBand/RDMA and Kubernetes controller/CRD-based platforms, and a software engineering approach to GPU infrastructure and its supporting tools.

$160k - $280k

Together AI

The Developer Experience (DevX) team at Together AI is responsible for how developers learn and build on the platform, managing the documentation site, API reference, quickstarts, and cookbooks. They treat documentation as a product, with content living in Git and publishing through Mintlify, synchronized with the API via CI/CD automation. The team also focuses on making content accessible to AI coding agents, developing a house style guide for AI-assisted authoring and structured content. As a Technical Writer Intern on DevX, you will write, test, and ship documentation used by thousands of developers. You will be paired with a mentor and own significant projects from scoping to launch, with real users and metrics. This is a hands-on, technical role where you will run the code you document, call APIs, and contribute to the documentation repository from your first week. Potential projects include building migration kits, making documentation agent-ready, generating CLI reference from source, and writing evaluation design tutorials.

NVIDIA's DSX OS transforms GPU infrastructure into a multi-tenant platform for AI teams, offering scheduling, orchestration, serving, and observability for AI workloads at data-center scale. The DSX OS Platform Software group develops this layer through projects like KAI-Scheduler, Snapshot, Grove, and Run:ai, contributing to the open-source cloud-native and AI serving ecosystems. The team prioritizes performance, reliability at scale, and providing users with insights into their workloads. We are seeking skilled software engineers to design and build scalable, high-performance systems, solve complex distributed-systems problems, and collaborate with experts to support the next generation of AI workloads.

We are building AI that changes how people capture, understand, and act on conversations and meetings. As Director of AI Research, you will lead research and development across automatic speech recognition (ASR), speaker identification (SID), large language models (LLMs), and audio language models. Your work will shape our AI capabilities, product direction, and long-term research strategy. This is a hands-on leadership role for someone who combines research depth with experience building and deploying AI systems at scale. You will set technical priorities, guide model and infrastructure decisions, and work directly with the team on challenging research and engineering problems. Partnering closely with Product, Engineering, and Data teams, you will turn research advances into reliable product capabilities. You will also build the team, experimentation practices, and technical foundation needed to move quickly while maintaining high standards for quality and performance.

$244k - $313k

Turing

Enterprise AI is building the platform that enables reliable deployment of AI systems across enterprise environments. As a Platform Engineer, you will build and operate the core of Turing’s Enterprise AI platform, including platform services, the model gateway, multi-tenant infrastructure, CI/CD, infrastructure as code, and observability. The central challenge is creating repeatable deployments across environments Turing may not control, including client cloud accounts, dedicated instances, and sovereign installations. This role requires hands-on software engineering and operational ownership, with a focus on building production software and contributing deeply to platform services.

Turing

Turing is seeking a Senior Full-Stack Engineer to join their team in Mumbai, India. This hybrid role involves building and supporting enterprise AI systems and applications for clients, particularly in asset management. You will take ownership of complex features from design through deployment and production support, working across the Python backend, React frontend, APIs, integrations, and database layers. The role emphasizes hands-on engineering, independent judgment, and the use of AI-enhanced development tools like Claude and Codex, with a strong focus on delivering maintainable and production-ready software.

Anthropic

Anthropic's Life Sciences team is building a research group focused on biological discoveries by combining AI with hands-on biological research. We're seeking a research scientist who combines deep chemistry knowledge with machine learning expertise to join the team. Our goal is to speed up progress in the life sciences by an order of magnitude, and this role puts that mission into practice. You'll be a computational chemist who knows reactions, synthesis, and medicinal chemistry in depth and has hands-on experience training and evaluating machine learning models. Above all, you'll build the reinforcement learning environments, datasets, and evaluations that teach Claude chemistry. Working alongside our bench chemists and AI researchers, you'll turn real chemistry problems and experimental data into training tasks and measure whether the models get better at them.

$300k - $320k

Momentic is seeking a hands-on AI Engineer to join their engineering team and collaborate closely with the CTO. This role focuses on enhancing the AI models and agents that power Momentic's AI-native testing platform. You will be instrumental in fine-tuning models, developing robust evaluation strategies, and transforming production data into more reliable and efficient AI agents. Your work will have a direct and significant impact, as Momentic processes over a million tests daily and billions of tokens per hour.

$175k - $250k

HUD is building reliable, fair, and open infrastructure for AI data, aiming to make data valuable for creators and trustworthy for AI training labs. As a Senior Research Infrastructure Engineer, you will accelerate research on training and evaluating frontier AI agents by building the software and infrastructure that enables researchers to iterate on environments, collect trajectories, and evaluate model behavior. You will collaborate closely with research engineers to understand their needs, identify bottlenecks, and transform research code into robust, reusable systems, owning the tools, pipelines, and execution infrastructure that support their exploration and scaling of promising approaches.

$175k - $260k

Together AI

As a Forward Deployed Engineer (FDE) focused on Post-Training, you will be a hands-on technical partner to strategic customers who are using fine-tuning and reinforcement learning to build differentiated models on open-source foundations. You will work alongside Solutions Architects as the deep-domain specialist in post-training, guiding customers through data strategy, algorithm selection, experiment design, and evaluation, and sizing the compute required. Your role is crucial in ensuring the successful delivery of post-training Proofs of Concept (POCs), driving adoption of the fine-tuning and RL platform, and translating customer goals into measurable improvements in model quality. This position directly impacts customer success, company growth, and the enhancement of our core platform.

$270k - $300k

Anduril Industries

Anduril Industries is a defense technology company transforming U.S. and allied military capabilities with advanced technology. We are seeking a Systems Engineer - Machine Learning to join our rapidly growing team. In this role, you will bridge systems engineering, applied physics, and mathematical and machine learning to solve complex missile defense problems. You will design track-fusion pipelines, build threat-assignment algorithms, and conduct deep sensitivity analyses across complex architectures. This requires proficiency in applied math, physics, and algorithm development using Python or C++, alongside an understanding of sensor data and battle management workflows.

As an Onboard Autonomy Intern, you will contribute to a focused research or engineering project within the onboard driving stack. You will work with experienced researchers and engineers to prototype, evaluate, and improve algorithms or software using vehicle data, simulation, or production-oriented environments. Potential focus areas include sensor fusion, structured perception, ML planning and trajectory generation, local planning and controls, and guardrails and integration.

As an Onboard Autonomy Intern, you will contribute to a focused research or engineering project within the onboard driving stack. You will work with experienced researchers and engineers to prototype, evaluate, and improve algorithms or software using vehicle data, simulation, or production-oriented environments. Potential focus areas include sensor encoders and fusion, structured perception, ML planning and trajectory generation, local planning and controls, and guardrails and integration.

$142k - $142k

We are seeking a hardworking intern with expertise in Reinforcement Learning and Multi-Modal World Simulation Models to advance ML-centric autonomous driving and Physical AI solutions. The role focuses on model-centric RL, understanding world simulation models, and applying state-of-the-art algorithms to real-world applications, enabling vehicles to interpret, anticipate, and respond effectively in dynamic environments. This is an opportunity to contribute to the future of intelligent driving, where innovation drives real-world impact. If you are passionate about building simulation technologies and systems that learn, adapt, and think, we encourage you to apply. Join a team where your contributions are vital to accelerating autonomous vehicle growth with advanced solutions.

As an Executive Director Machine Learning Engineer on the Recommendation Engine team, you will implement fine-tuning and reinforcement learning algorithms on large compute clusters. You will build and run real-time and batch model serving systems, manage hyper-parameter tuning at scale, and oversee model monitoring and production validation. This role is vital for model development, testing, and deployment in a well-managed, controlled environment, supporting high throughput, low latency applications that leverage state-of-the-art machine learning architectures deployed in AWS. These applications power personalized experiences across Chase Consumer & Community Banking channels.

Abundant

Abundant is building the NVIDIA of training data, recognizing the critical need for scaling data as AI models advance beyond supervised learning to reinforcement learning and learning from experience. Our founding team brings deep expertise from leading AI organizations, with a proven track record in deploying large-scale deep learning models, training state-of-the-art self-driving models, and managing massive data pipelines. We are at the forefront of innovation, addressing the scarcity of training data by advancing simulation, synthetic data generation, and learning from experience, which are essential for achieving AGI, ASI, and physical intelligence. We partner with top AI labs and enterprises to provide the data necessary for models to move from general knowledge to domain expertise and from chatbots to agents.

$168k - $168k

At Klaviyo, we believe the future of software lies not only in tools that help people work more efficiently, but in intelligent systems that can take action, learn from outcomes, and improve customer experiences over time. Klaviyo serves more than 167,000 customers and processes billions of consumer profiles, messages, interactions, and conversion signals. This creates a unique opportunity to build state-of-the-art AI systems that help businesses create and execute better customer experiences at scale. We’re looking for a Software Engineer, Conversational Products & Agent Workflows to join the Customer Agent team, Klaviyo’s AI-native conversational platform. You’ll help build scalable backend systems and AI-powered product experiences that enable agents to retrieve context, use tools, and take reliable action on behalf of customers. This is a backend-heavy role with opportunities to contribute to user-facing experiences. You’ll independently own meaningful pieces of larger systems, contribute to technical decisions, and continue developing your depth across backend engineering, applied AI, and production reliability.

$116k - $174k

General Motors is seeking a researcher to develop and apply artificial intelligence and machine learning solutions for advanced manufacturing systems. This role will focus on applying AI/ML to real-world manufacturing challenges involving process improvement, quality, automation, production efficiency, and decision support. The researcher will work with multidisciplinary teams to develop new concepts, build prototypes, evaluate results, and help transition successful solutions into manufacturing applications.

Wayve is building the AI platform for autonomous driving, enabling vehicles to learn directly from real-world experience and adapt at scale. This role is on the Vehicle Generalization Team, focused on making Wayve's core autonomy generalize across a wide range of vehicle platforms. You will work on pre- and post-training methods to improve AI model generalization, specifically addressing how models perform with changes in perception and embodiment across different vehicles. Your work will directly impact production programs with major OEMs.

$370k - $420k

Anthropic

Anthropic is seeking a Research Engineer for its Code RL team, focused on advancing AI models for silicon design. This role involves leveraging chip design expertise to create tasks and signals for AI models, aiming to improve their capabilities in areas like RTL generation, design verification, and physical design optimization. The position sits at the intersection of cutting-edge research and engineering, with a commitment to building high-quality, scalable AI systems.

$500k - $850k

Ema builds AI Employees for HR, IT, and Finance, automating busy work across the employee experience. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team comprises engineers from premier tech companies and graduates of top universities. We are backed by leading investors and are at the forefront of Agentic AI, shipping real systems that run business processes at scale. We are seeking innovative and passionate Machine Learning Engineers who excel at solving complex problems with large datasets, transforming theoretical concepts into practical, scalable solutions, and pushing the boundaries of Natural Language Processing and Information Retrieval.

$160k - $220k

Etched is building AI systems that accelerate its own development. As a Research Engineer on the RL Frontiers team, you will train proprietary frontier models to automate kernel development, ASIC engineering, production pipelines, and 3-D hardware operating systems. You will use proprietary simulators and environments to create custom data mixes and post-train models to SOTA performance on the company's hardest engineering tasks. The company is focused on inference and bets on transformer architectures, aiming to capture the entire inference market. Etched is a fully in-person team in San Jose, valuing engineering skills and expecting technical staff to contribute to both engineering and research across disciplines.

From $400k

Anthropic

Reinforcement learning is crucial for training AI models like Claude to reason, write code, and act autonomously. At scale, RL training involves complex distributed systems running across numerous accelerators and hosts. As an engineer in RL Engineering, you will be responsible for securing this expanding system. This is a hands-on role where you will work directly within the RL codebase and its underlying infrastructure, collaborating with the engineers and researchers who develop and utilize it. The system's rapid evolution, driven by research advancements, requires security solutions that maintain pace without compromising performance or development velocity. We are seeking candidates with expertise in either security engineering for large-scale ML systems or ML systems/research engineering with a strong interest in security.

$320k - $405k

Together AI

We are seeking a software engineer with a strong focus on reliability to manage the production operations of our Kubernetes-native control plane, which provisions and runs our GPU inference fleet. This platform team develops the API and controllers that enable the inference team to define their needs, such as clusters, model deployments, or capacity changes, and ensures these are reconciled. Your role will be to maintain this functionality in the real world by troubleshooting issues like InfiniBand problems, diagnosing serving faults, and enhancing our automated self-healing systems to permanently resolve recurring failures. The primary goal is to ensure the decoupling remains effective even during disruptions and to maintain fleet efficiency without introducing new instabilities. You will be responsible for inference serving SLOs and error budgets, leading incident response, and implementing lessons learned from postmortems into system changes. Additionally, you will address the reliability aspects of capacity efficiency, recognizing that misconfigured defragmentation or bin-packing can pose production risks. We value candidates with hands-on production reliability experience in large-scale distributed or GPU-serving systems, proficiency in troubleshooting InfiniBand/RDMA and Kubernetes controller-based platforms, and a software engineering approach to GPU infrastructure.

$250k - $320k

NODA AI is seeking an AI/ML Engineer to design and implement intelligent agents for adaptive mission planning and orchestration across multi-domain unmanned systems. This role involves working with LLM orchestration frameworks, agent reasoning systems, and deploying AI models to edge computing environments on autonomous vehicles. You will build systems that translate high-level mission intent into actionable autonomous behaviors, dynamically adapt plans to changing environments, and provide explainable reasoning to human operators. The work integrates directly with a ROS autonomy stack and ensures reliable AI performance on resource-constrained edge hardware.

Salesforce AI Research is seeking entrepreneurial Research Interns for Summer 2027 to help build the next generation of Enterprise AI. This is a unique opportunity to work like a founder within Salesforce, exploring high-impact research directions, prototyping cutting-edge AI capabilities, and seeing how ideas are validated with customers and turned into production-ready solutions. Interns will have the freedom to propose and explore new ideas alongside mentors and research scientists, with access to Salesforce's global customer and product ecosystem. The role is ideal for individuals comfortable with ambiguity, bringing energy and focus, and who care as much about customer adoption as technical innovation, offering the pace and ownership of a startup with the reach and resources of Salesforce.

Anthropic

Anthropic is seeking a Staff+ Software Engineer to enhance the vector search infrastructure powering Claude. This role is crucial for Claude's ability to access information, impacting web search, conversation memory, and model training. You will join a small, foundational team responsible for the architecture, performance, scalability, and cost of the vector database layer. As a hands-on engineer, you will contribute to coding and make key architectural decisions, shaping the future evolution of this critical system within the Databases organization.

$320k - $485k

OpenAI

The Agent Post-Training team at OpenAI is responsible for developing the frontier agents that are shipped to users. This involves training models for products like Codex and ChatGPT, focusing on creating persistent, proactive intelligence that can operate computers, collaborate with humans and other agents, and expand user capabilities. The team defines the next generation of agent abilities, builds the training signals, and runs experiments to realize these advancements. The role specifically focuses on teaching models to operate computers, including navigating browsers and desktops, using tools, reasoning through complex workflows, and completing long-horizon tasks reliably. This position is at the intersection of frontier model training, product behavior, evaluation, and systems engineering, directly influencing the computer-use capabilities of OpenAI's future agents.

$380k - $500k

Genmo is a research lab developing sophisticated video world models to understand, simulate, and interact with the physical world, aiming to unlock the right brain of AGI. We are seeking an exceptional Research Scientist to focus on alignment and post-training techniques for large-scale video generation models. This role is at the forefront of ensuring our diffusion-based video models reliably produce high-quality, physically accurate, and safe outputs that align with human preferences and values.