Develop, fine-tune, and optimize self-hosted large language models for personalized AI conversations.
Work across the full model development lifecycle, from dataset preparation to GPU-based experiments and production deployment.
Collaborate with cross-functional teams to enhance conversational quality, memory, and multilingual interactions.
This company builds innovative consumer-facing AI products powered by self-hosted large language models. They operate a remote-first culture across Europe, emphasizing collaboration, continuous learning, and technical excellence.
Own end-to-end performance optimization for production ML workloads, from bottleneck identification to rollout.
Diagnose ML performance bottlenecks across model execution, frameworks, runtimes, memory, and CPU/GPU utilization.
Design and implement improvements in inference execution, runtime behavior, batching, memory management, and precision.
Reddit is a community of communities, hosting open and authentic conversations across the internet. With over 100,000 active communities and approximately 130 million daily active users, it is one of the web's largest sources of information.
Fine-tune open-weight LLMs using SFT, LoRA/QLoRA, and post-training methods such as DPO and GRPO to shape model behavior for 50M+ users.
Diagnose model failures and turn them into experiments, building training data and evals that directly influence production models.
Ship to production, speed up the training loop, and collaborate with Product, Web, and DevOps teams.
EverAI is building the world's largest AI companionship platform, redefining relationships for millions. With ~100 people, it's a fully remote, hardworking team with an ownership-driven culture and a track record of scaling products from 0 to IPO.
Own the client's GKE-based H200/GB200 training platform from the workload side, tuning GPU scheduling and networking.
Run, scale, and debug multi-node distributed training with PyTorch (FSDP, DeepSpeed, Megatron/NeMo-style) and Ray Train.
Design benchmarks, evals, and telemetry so platform and training changes are measured, not assumed.
Egen is a data-first engineering company that helps clients drive action and impact through data and insights using advanced platforms like Google Cloud and Salesforce. It is a fast-growing, entrepreneurial workplace focused on learning, solving tough problems, and continual innovation.
Design, deploy, and optimize large-scale ML systems for retrieval, ranking, and LLM-powered features.
Lead ML projects from concept to production, improving answer relevance and user experience.
Collaborate with Product, Data Science, and Engineering teams to shape technical direction.
The hiring partner is a technology company focused on building advanced AI systems that help users discover relevant answers from large collections of online conversations. It values innovation, collaboration, and responsible AI development, offering a remote work environment within the United States.
Build the geometry and data core of a new BIM authoring tool.
Develop AI agents that can plan and edit real building models.
Create generative design capabilities that account for building codes and client constraints.
They build AI-first software for architecture and construction, creating a new BIM authoring tool for AI agents. As an early-stage team, they value generalist engineers comfortable working across the stack and willing to grow into leadership roles.
Own optimization of the inference stack: throughput, latency, cold starts, and cost per token.
Tune SGLang/vLLM engines, cut TTFT, boost KV cache reuse, and kill cold starts on 1.5 TB checkpoints.
Set technical direction with data, benchmark every change, and scale inference across nodes.
Kimchi is an LLM inference platform serving open models like DeepSeek and GLM from its own GPU fleet via one API line. It's made by CAST AI, a globally distributed team across 34 countries that hit unicorn status in 2026.
Build and extend pretraining code to scale experiments into full training runs.
Evaluate model architecture changes and optimize training throughput and resilience.
Run controlled ablations and collaborate with data and evaluation researchers.
Prime Intellect is building an open superintelligence stack, providing the infrastructure and platform for frontier AI training and deployment. The company has raised $150M from top investors and boasts a mission-driven team working to decentralize AI.