Find your next AI engineering role.

Together AI

We are seeking a software engineer to develop a Kubernetes-native control plane for managing our GPU inference fleet. This role involves designing a manifest-driven API that allows the inference team to declare their needs for clusters, model deployments, or capacity changes. Our controllers will handle the underlying reconciliation, provider/runtime selection, and lifecycle management, abstracting away the complexities of the serving stack, scheduler, and hardware pool. You will also build systems to optimize fleet efficiency, including defragmentation, rebalancing logic for consolidating workloads, and scheduling/bin-packing improvements to maximize GPU utilization without impacting latency. The primary goal is to decouple platform users from operational complexities while increasing usable capacity from existing hardware. This includes developing controllers, reconciliation loops, and self-service interfaces (API/CLI) for seamless decoupling, alongside event-driven systems for health monitoring, remediation, and utilization management without manual intervention. A product mindset is crucial, with experience building internal platforms or APIs for other engineering teams and a focus on developer experience and ownership of delivered software, including production support.

Together AI

We are seeking a software engineer to develop a Kubernetes-native control plane for managing our GPU inference fleet. This role involves designing a manifest-driven API that allows the inference team to declare their needs for clusters, model deployments, or capacity changes. Our controllers will handle the underlying reconciliation, provider/runtime selection, and lifecycle management, abstracting away the complexities of the serving stack, scheduler, and hardware. You will also build systems to optimize fleet efficiency, including defragmentation, rebalancing logic for consolidating workloads, and scheduling/bin-packing improvements to maximize GPU utilization without impacting latency. The primary goal is to decouple users of the platform from operational complexities while maximizing hardware capacity. This includes building controllers, reconciliation loops, and self-service APIs/CLIs, along with event-driven systems for health, remediation, and utilization, all designed to operate without human intervention. A product mindset is crucial, focusing on the developer experience of internal platforms and APIs, with a 'build it, own it' philosophy that includes production operation and support.

SambaNova

As a Senior Principal Machine Learning Engineer, you will be responsible for designing, developing, and optimizing machine learning models, with a focus on cutting-edge Large Language Models (LLMs), to run efficiently on SambaNova's specialized hardware architecture, including the RDU. This critical role bridges advanced LLM research and practical deployment, involving the development of model architectures, improving training and inference efficiency, and collaborating on hardware-software co-design with compiler, systems, and hardware teams. The engineer will also act as the ML expert, guiding the integration of LLM solutions into production systems and customer-facing products like SambaStack and SambaCloud, with work spanning the full lifecycle from training and inference to evaluation and data curation.

$220k - $300k

Tenstorrent is seeking a Field Application Engineer to act as a technical liaison between the company and its customers throughout Southeast Asia. Based in Singapore, this role involves understanding customer AI and machine learning workloads, guiding technical evaluations and deployments, and resolving hardware and software issues to ensure customers maximize the performance of Tenstorrent's AI platforms. This is a customer-facing position requiring hands-on technical problem-solving, solution development, and regional relationship management, with regular travel across Southeast Asia. The role is remote and based out of Singapore, and the company is open to candidates at various experience levels.

Prodigal is seeking a Machine Learning Engineer to join their Data Science and Machine Learning (DSML) team. This role involves owning the full lifecycle of ML systems, from data curation and model selection to fine-tuning, deployment, evaluation, and monitoring. You will work closely with product, engineering, and customer-facing teams to translate real-world problems into well-scoped ML challenges, curate necessary datasets, and train a variety of models including neural networks, transformers, and LLMs. The position requires taking models from development to production, optimizing for performance and scalability, and designing evaluation frameworks to ensure meaningful results.

Together AI

As a Forward Deployed Engineer (FDE) focused on Inference & Post-Training, you will be a hands-on technical partner to strategic customers looking to leverage high-quality models and perform inference at scale. You will act as a deep-domain specialist in inference optimization, fine-tuning pipelines, and production deployment, partnering with Solutions Architects. FDEs add significant value by ensuring complex Proofs of Concept (POCs) are met, facilitating platform adoption, and guiding tailored optimization efforts, directly impacting customer success and platform hardening.

Together AI

As a Technical Support Engineer at a pioneering AI company, you will be the first line of defense supporting customers building training, fine-tuning, and inference solutions. You will address complex technical challenges, provide effective solutions, and act as a product expert. Collaborating with product and sales teams, you will contribute to the continuous improvement of offerings. This role is for a technically proficient individual passionate about AI and customer success, seeking to make a significant impact in a fast-paced, innovative environment.

$160k - $230k

Together AI

The Model Shaping team at Together AI focuses on tailoring open foundation models for downstream applications, building services that help machine learning developers select and improve models with domain-specific data. We also develop novel methods for efficient model training and evaluation, drawing from machine learning, natural language processing, and ML systems. As a Research Engineer on the Scaling Team, you will translate cutting-edge research in efficient foundation model training into robust, high-performance systems. You will optimize Together's training infrastructure, identify performance bottlenecks, and implement state-of-the-art techniques in production environments. Your work will directly influence the fine-tuning experience for Together's customers. You will integrate newly released open-source models onto the platform, ensuring efficient and reliable training across diverse workloads. Collaborating with Research Scientists, you will build experimental infrastructure to accelerate research and deploy validated ideas at scale.

$200k - $290k

Scale AI

Scale's Global Public Sector team is focused on using AI to address critical challenges facing the public sector around the world. As a Staff Applied AI Engineer, you will raise the bar for how AI is built, governed, and evaluated across the team. You will define standards that other engineers adopt, such as evaluation methodologies for agents and MLOps practices, while acting as the senior technical voice for the most sensitive AI decisions our clients face. You will be accountable for production AI behavior, its backend integration, and the evaluation required for a specific customer.

Together AI

Together AI is seeking a full-stack engineer to join their Product Engineering team. This role focuses on enhancing the developer experience across their AI platform, including surfaces like Playground, Model Garden, and Fine-Tuning. The ideal candidate is motivated by shipping impactful features that users value, working across the entire product lifecycle from data models to user interfaces. This is a high-ownership position on a fast-moving team where individual contributions are highly visible and directly influence product direction.

$160k - $230k

Appier is seeking a Research Scientist to work at the forefront of generative and agentic AI. This role involves advancing Large Language Models (LLMs), Vision-Language Models (VLMs), and AI agents capable of reasoning, planning, and tool utilization for real-world problem-solving. Your research will encompass post-training techniques, reasoning, multimodal intelligence, and autonomous agentic systems. You will play a key role in shaping Appier's core AI capabilities, contributing to publications at top AI/ML conferences, and collaborating with scientists and engineers to translate cutting-edge research into impactful products.

Together AI

We are seeking a software engineer to develop a Kubernetes-native control plane for provisioning and managing our GPU inference fleet. This role involves designing a manifest-driven API that allows the inference team to declare their needs, such as clusters, model deployments, or capacity adjustments. Our controllers will handle the underlying reconciliation, provider/runtime selection, and lifecycle management, abstracting away the complexities of the serving stack, scheduler, or hardware pool. You will also build systems to ensure fleet efficiency, including defragmentation, rebalancing logic for consolidating workloads, and scheduling/bin-packing improvements to maximize GPU utilization without compromising latency. The core objective is to decouple platform users from operational complexities while optimizing hardware capacity. This includes developing controllers, reconciliation loops, and self-service interfaces (API/CLI) for seamless operation, alongside event-driven systems for health monitoring, remediation, and utilization management, all designed to operate without human intervention. A product-oriented mindset is crucial, with a focus on building internal platforms and APIs that enhance the developer experience for consuming engineering teams. This role operates under a "you build it, you own it" philosophy, encompassing both software delivery and production support.

$160k - $280k

Scale AI

Scale is seeking a Staff Machine Learning Research Engineer to join the Applied Intelligence Systems (AIS) team, which is part of the Scale Generative AI Platform. This role focuses on advancing agentic AI applications for enterprise and government use cases. You will work across the full spectrum of technical challenges within AIS, contributing to areas such as model training and fine-tuning, designing evaluation systems, building improvement loops from production data, prototyping novel agent architectures, and developing internal tooling. This is a hands-on research and engineering position where you will write code, ship production systems, and set the AI/ML technical direction for the team, influencing methods, architectures, and standards for others.

$250k - $350k

Together AI

At Together AI, you’ll build and operate one of the world’s largest GPU fleets used for frontier model training and inference. This role is for engineers who love building systems, automating everything, and solving problems at massive scale. If you enjoy writing software more than clicking dashboards, obsess over eliminating manual work, and want to build infrastructure that manages tens of thousands of GPUs autonomously, we’d love to talk.

Stripe

The Conversation Platform team is building a comprehensive platform for Stripe merchants, focusing on automating simple tasks and assisting with complex ones. This includes features like customizing landing pages, enabling natural language interaction with the Stripe API, and automating issue resolution. The team is developing Retrieval-Augmented Generation (RAG) systems using the latest Large Language Models (LLMs) and fine-tuning their own models, managing the entire lifecycle from ideation to production deployment.

Together AI

Together AI's Model Shaping team focuses on tailoring open foundation models for downstream applications, building services that help ML developers select and improve models with domain-specific data. The team also develops efficient model training and evaluation methods, drawing from machine learning, NLP, and ML systems. As a Research Engineer, you will develop a platform for users to customize open-source models with their own data, working across training and inference stacks. You will build and enhance services for Fine-Tuning, Reinforcement Learning, and Evaluation, ensuring a smooth transition from post-training to production serving and optimizing inference engines for RL training. Collaboration with product, research, and engineering teams is key to maintaining API reliability and performance, ultimately contributing to the foundational layer of the open-source AI ecosystem.

$200k - $290k

Datadog

The Behavior AI team is responsible for developing the AI-based anomaly detection systems that power Datadog's security products. These models are designed to learn normal behavior patterns from vast amounts of data and flag deviations in real-time, with a focus on efficiency and cost-effectiveness at scale. The work involves creating small, custom models optimized for high-throughput stream processing, rather than relying on large, general-purpose models. This role offers the opportunity to contribute to the entire model lifecycle, from architectural design and large-scale training to optimization for production environments, addressing complex challenges in model efficiency, accuracy trade-offs, and interpretability.

Anduril Industries

Anduril Industries is seeking a backend software engineer to own and evolve their internal LLM agent framework. This role is crucial for integrating generative AI systems into the Lattice software platform, enhancing autonomy and reducing cognitive load for users. The engineer will build the platform that enables teams across Anduril to develop, evaluate, and deploy reliable LLM agents in mission-critical environments, working at the intersection of backend infrastructure, applied AI, agent architecture, model post-training, and evaluation tooling.

$220k - $292k

This role focuses on building AI agents that learn and improve over time by incorporating feedback, correcting mistakes, and optimizing towards user-defined outcomes. As a Staff AI/ML Engineer on the Applied Research team, you will lead the technical direction for feedback-driven learning within DigitalOcean's agentic systems. This includes owning the roadmap for reward modeling, preference optimization, reinforcement learning, and the necessary evaluation infrastructure to ensure these systems are effective. You will be responsible for shipping research into production, setting technical direction, running large-scale experiments, and closing the loop between user signals and model behavior.

$217k - $271k

DigitalOcean is seeking a Senior Engineering Manager to lead their Inference Orchestration team. This role will drive the strategy, execution, and scaling of the company's Kubernetes-based AI infrastructure. The manager will be responsible for balancing business needs with technical excellence, ensuring high throughput, optimal GPU utilization, and robust fault tolerance for next-generation disaggregated inference, fine-tuning, and training workloads. The position involves team leadership, project execution, cross-functional collaboration, and ensuring the operational health of services.

$201k - $251k

Stripe

Stripe is seeking an Engineering Manager for its newly formed AI Conversation Platform team. This team is focused on building a comprehensive conversation platform for Stripe merchants, aiming to automate simple tasks and assist users with complex ones. This includes features like customizing landing pages, enabling natural language commands for the Stripe API, and automating issue resolution. The team works end-to-end, from ideation to production deployment, utilizing RAG-based systems with the latest LLMs and fine-tuning their own models.

Databricks

Databricks is seeking a Staff Software Engineer for its AI Runtime (AIR) team. AIR is a managed platform designed for large-scale GPU training and fine-tuning of state-of-the-art AI models, providing customers with on-demand access to accelerators and a serverless experience. As part of this role, you will be instrumental in building and scaling the systems that ensure fast, reliable, and effortless large-scale training. You will influence the architecture and development of the managed GPU training stack, focusing on areas like scheduling, distributed training performance, fault tolerance, and the overall developer experience for operating jobs at scale. This position also involves defining the long-term technical vision for AIR, mentoring senior engineers, and collaborating with product, research, and platform teams to expand the impact of custom training at Databricks.

$190k - $265k

Databricks

Databricks is seeking a Senior Software Engineer for its AI Runtime (AIR) team. AIR is a managed platform designed for large-scale GPU training and fine-tuning of AI models, providing customers with on-demand access to accelerators and a serverless experience that simplifies the complexities of provisioning, scheduling, and orchestrating multi-node jobs. As a Senior Software Engineer on AIR, you will be instrumental in building and scaling the systems that ensure large-scale training is fast, reliable, and effortless. You will lead the architecture and evolution of the managed GPU training stack, focusing on areas such as scheduling, distributed training performance, fault tolerance, and the developer experience for large-scale job operations. Beyond hands-on system development, you will contribute to the technical vision for AIR, mentor engineers, and collaborate with product, research, and platform teams to expand the impact of custom AI training at Databricks.

$160k - $225k

Together AI

In this role, you will operate, scale, and optimize multi-petabyte storage systems purpose-built for the world’s largest AI training and inference workloads. You’ll manage and scale high-performance parallel filesystems and object stores, evaluate and integrate cutting-edge technologies, and solve complex engineering challenges of operating at extreme throughput, low-latency data paths, and massive cluster-scale storage operations. You will also build Kubernetes-native storage operators and self-service platforms that provide automated provisioning, strict multi-tenancy, performance isolation, and quota enforcement at cluster scale.

$250k - $330k

10a Labs is seeking a Machine Learning Engineer to design, build, and evaluate advanced machine learning systems for AI safety and model evaluation applications. This role blends strong ML engineering with an experimental approach, tackling problems in reinforcement learning, model evaluations, language models, and multimodal systems. You will transform ambiguous technical questions into rigorous experiments and scalable systems, collaborating with a diverse team of engineers, analysts, and subject-matter experts to support leading AI organizations.

$130k - $200k

Together AI

Together AI is building the best inference infrastructure for voice applications, powering production-grade, real-time voice agents and applications with best-in-class latency and reliability for speech-to-text and text-to-speech models. We are seeking a Staff ML Engineer to lead the model serving layer for voice workloads. This role involves hands-on optimization of inference engines and models, profiling GPU utilization, designing batching strategies for streaming audio, and ensuring rapid deployment of new model architectures from research to production. This is a foundational hire on a small, high-impact team, addressing the unique challenges of voice inference such as streaming audio, tokenization, and real-time latency budgets, and shaping the future of voice model serving.

$220k - $280k

Scale AI

As a Senior Staff Frontier Agents Engineer, you will be the technical bridge between Scale AI's advanced AI capabilities and our most strategic enterprise customers. This role involves understanding customer challenges, architecting custom AI solutions, and ensuring successful deployment and adoption of AI systems in production. You will work hands-on with customer engineering teams to integrate AI into their critical workflows, combining deep engineering expertise with customer-facing problem-solving.

$288k - $360k

Together AI

At Together AI, you will build and operate one of the world's largest GPU fleets for frontier model training and inference. This role is for engineers who excel at building systems, automating processes, and solving problems at a massive scale. If you prefer writing software over manual operations and aim to build infrastructure that autonomously manages tens of thousands of GPUs, we encourage you to apply.

$190k - $270k

Databricks

The AI Forward Deployed Engineering (AI FDE) team at Databricks is a specialized customer-facing AI team focused on building and productionizing first-of-its-kind AI applications. This role is for experienced engineers who excel at working with customers and teammates, and who are passionate about the latest trends in Generative AI, LLMOps, and Machine Learning. You will collaborate cross-functionally with engineering and product teams to shape strategic priorities and initiatives, acting as a trusted technical advisor to customers across various domains.

$182k - $250k

The Forward Deployed Engineering (FDE) team operates at the intersection of AI research, production deployment, and customer impact. As an Agentic AI Applied Scientist, you will embed directly with high-growth startups, tech innovators, and AI native enterprise partners. You will design, build, and deploy custom autonomous agent architectures running on DigitalOcean’s high-performance AI infrastructure. This role involves architecting production-ready agentic frameworks, directing customer integration, synthesizing research and deployment, and driving reliability and evaluation of AI systems.

$220k - $239k

Databricks

Databricks is seeking a Senior Staff Applied AI Engineer to lead the development of context retrieval systems for Databricks agents across various SaaS providers. This is a foundational role focused on building both the underlying retrieval stack and the intelligent search subagents that leverage it. The goal is to significantly enhance the ability of Databricks agents to access and understand relevant information from diverse data sources, thereby improving their performance in tasks ranging from answering complex queries to debugging and data analysis. This role requires deep expertise in information retrieval and a proven track record of shipping production-ready retrieval systems for RAG and agentic workloads.

$229k - $343k

Together AI

As a Forward Deployed Engineer (FDE) focused on Inference & Post-Training, you will be a hands-on technical partner to strategic customers looking to leverage high-quality models and perform inference at scale. You will act as a deep-domain specialist in inference optimization, fine-tuning pipelines, and production deployment, partnering with Solutions Architects. FDEs add significant value by ensuring complex Proofs of Concept (POCs) are met, facilitating platform adoption, and guiding tailored optimization efforts, directly impacting customer success and company growth.

$270k - $300k