Together AI

Research Engineer role focused on improving inference efficiency and unifying it with RL/post-training systems for production-grade AI APIs. The role involves end-to-end ownership of critical systems, translating frontier ideas into robust infrastructure, and shipping measurable improvements in latency, throughput, cost, and model quality at scale.

Forward Deployed Engineer focused on optimizing inference engines and fine-tuning pipelines for production AI teams, acting as a technical partner to strategic customers. Responsibilities include inference engine optimization, performance tuning, post-training/fine-tuning (LoRA, SFT, DPO, RLHF, GRPO), customer alignment, onboarding, and providing product feedback.

Senior ML Engineer focused on optimizing the model serving layer for voice AI workloads, including speech-to-text and text-to-speech models. The role involves hands-on work with inference engines, GPU optimization, batching strategies, and ensuring new model architectures can be productionized efficiently. The goal is to achieve best-in-class latency and reliability for real-time voice applications.

Research Engineer focused on translating internal model training research into production-ready deployments by fine-tuning general-purpose models into specialized tools. This involves designing novel speculative algorithms, data curation, hyperparameter tuning, and checkpoint evaluation, with a focus on accuracy-efficiency tradeoffs for generative AI models.

This role focuses on optimizing and developing GPU-accelerated kernels and algorithms for ML/AI applications, requiring expertise in GPU programming (CUDA, Triton) and performance profiling. The engineer will collaborate with modeling, hardware, and software teams to enhance AI system efficiency and co-design GPU architectures.

AI Researcher focused on the intersection of efficient inference algorithms, architectures, engines, and post-training/RL systems for production-scale API services. The role involves advancing inference efficiency, unifying inference with RL/post-training, and owning critical systems.

The Forward Deployed Engineer (FDE) will be a technical partner to customers building large-scale AI models, focusing on GPU cluster infrastructure, networking, storage, and orchestration to ensure stability, optimize performance, and facilitate platform adoption. This role involves hardening clusters, tuning orchestration layers (Kubernetes/SLURM), debugging low-level bottlenecks, building reference designs, and leading benchmarking exercises.

Engineering Manager for Together AI's Model Serving platform, focusing on delivering world-class inference and fine-tuning in public APIs and customer deployments. Responsibilities include owning SLAs, improving testing/deployment/monitoring, building self-serve tooling, defining configuration best practices for inference engines, leading incident response, and mentoring team members. Requires 5+ years operating production ML inference or training systems at scale and 2+ years in senior IC or tech lead roles, with deep expertise in Kubernetes, multi-cluster orchestration, and ML serving frameworks.

Machine Learning Engineer at Together AI focused on developing and scaling production systems for LLM inference and fine-tuning APIs. Requires strong experience in high-performance, distributed systems and the LLM inference ecosystem.

Machine Learning Engineer focused on optimizing and enhancing the performance of AI inference systems, working with state-of-the-art large language models to ensure efficient and effective operation at scale. Responsibilities include designing and building production systems, optimizing runtime inference services, and creating supporting tools and documentation.

Senior Platform Engineer for Together AI's Voice AI platform, focusing on the API and infrastructure layer for real-time speech-to-text and text-to-speech models. The role involves building WebSocket and HTTP APIs, designing autoscaling for latency-sensitive streaming, and ensuring platform reliability for production voice agents.

Senior Backend Engineer focused on building and optimizing the inference platform for advanced generative AI models, including LLMs and multimodal models, at scale. The role involves optimizing latency, throughput, and resource allocation across tens of thousands of GPUs, collaborating with researchers to productionize frontier models, and contributing to open-source inference projects.

Machine Learning Platform Engineer at Together AI, focusing on building a container platform, optimizing autoscaling, minimizing cold starts, and improving end-to-end model performance for custom models and dedicated inference. The role involves optimizing inference across the stack, including CUDA kernels, PyTorch, inference engines, and container orchestration.

AI Infrastructure Engineer responsible for keeping user-facing services and production systems running smoothly, applying engineering principles and automation to operating environments. Focuses on systems, availability, reliability, and scalability, with interests in algorithms and distributed systems. Builds and runs infrastructure using Ansible, Terraform, and Kubernetes, and designs monitoring systems.

Senior Software Engineer focused on building and operating a high-performance, global AI cloud infrastructure platform. This includes designing and maintaining backend services for hardware management, IaaS software layer for GPU data centers, high-performance object storage for pretraining datasets, and advanced observability stacks for distributed pretraining. The role also involves architecture and research for decentralized AI workloads and contributing to the open-source platform.

Solutions Architect at Together AI to work with customers and prospects to create business value through Generative AI applications. This role involves acting as a technical advisor, running demonstrations and POCs, collaborating with sales, building relationships with customer leadership, delivering feedback to product/engineering/research, and building educational content. Requires 5+ years in a customer-facing technical role with 2+ years in pre-sales, strong technical background in AI/ML/GPU, understanding of LLM training/fine-tuning/inference, Python/JavaScript proficiency, and familiarity with infrastructure services.