LoRA Jobs

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

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

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

We are seeking a Senior Forward Deployed Engineer to lead technical engagements with partners and customers, focusing on NVIDIA's open model portfolio. The primary mission is to transform open models into production-ready AI solutions tailored to specific domains, covering the entire lifecycle from post-training to harness optimization. This role involves collaborating closely with NVIDIA's partner ecosystem, co-leading customer engagements, and developing resources that enable partners to scale their AI initiatives. You will work hands-on to build custom AI solutions, from initial customer conversations through to production deployment, ensuring models are optimized for performance and cost-effectiveness.

$184k - $357k

Snorkel AI is seeking an early member for its ML & Research Engineering team to help build the data and environments behind high-performing frontier and agentic AI. You will be responsible for making the process of generating and evaluating frontier AI data faster, cheaper, and more rigorous using ML and AI. This role involves studying data generation and evaluation, forming and validating hypotheses with production data, and shipping scalable solutions. You will play a key part in shaping the direction, standards, and growth of the discipline.

$208k - $315k

At Thinking Machines, we build tools that empower individuals to customize AI models for their specific needs, including the ability to train model weights. In this role, you will focus on frontier customization techniques and contribute to developing a leading post-training engine called Tinker. Your work will involve a comprehensive understanding of Reinforcement Learning (RL) science, and your findings will directly influence Tinker's training defaults, API design, and the open-source Tinker Cookbook. You will collaborate with internal research teams and contribute to open science initiatives for external partners.

$350k - $475k

Thinking Machines is building AI that extends human will and judgment, with a focus on training frontier models and developing tools that allow users to customize AI for their unique needs. This role leads the Fine-tuning Science team, focusing on frontier customization techniques and the Tinker post-training engine. It's a player-coach position where you will remain hands-on with scientific research while also guiding the team, shaping its direction, and ensuring research findings are integrated into Tinker. You will collaborate with internal research teams, contribute to open science, and engage with external users.

$350k - $475k