Point your AI agent at freehire and let it find you a job. A CLI and an MCP server over the whole job API — no browser.
Build and deploy scalable AI/ML systems, including LLM pipelines, RAG, and MLOps tooling, to production for a global lifestyle brand.
Build and fine-tune large language models and deep-learning systems on Cerebras’ wafer-scale AI hardware to solve real-world customer problems, from training bespoke models to deploying agentic AI.
Lead end-to-end training of large language models using domain-adaptive pretraining, fine-tuning, and reinforcement learning, while building robust data and evaluation pipelines for intelligent agent systems.
Design and train cutting-edge AI agents and models for sovereign public-sector use cases, publish research at top venues, and lead AI-safety initiatives for national-scale deployments.
Builds low-level system software to optimize distributed AI training and inference across thousands of GPUs using C++, Python, and CUDA.
Build and deploy cutting-edge text-to-speech models, voice cloning, and audio generation systems using large-scale ML and transformer architectures.
ML engineer builds and runs online reinforcement-learning pipelines to improve GigaChat’s post-training, designing experiments, reward signals, and distributed training workflows.
Lead the online reinforcement learning team for STEM-focused LLM post-training at GigaChat, designing RLHF pipelines, reward models, and data curation to improve reasoning in math, physics, and other sciences.
Lead the online reinforcement-learning team for GigaChat, designing post-training methods, reward models, and data pipelines to make the LLM more helpful and user-friendly.
Design and deploy production-grade GenAI and ML solutions on AWS, optimizing cost, security, and performance while embedding reusable patterns into DoiT’s Cloud Intelligence platform.
Design and deploy production-grade GenAI and ML solutions on AWS for enterprise customers, focusing on cost efficiency, reliability, and security while creating reusable patterns and driving product adoption.
Design and deploy scalable, real-time AI systems including LLM inference pipelines, RAG, and vector databases using Python, TensorFlow/PyTorch, and Kubernetes.
Build and operate an AI agent platform for conservation and climate teams, designing APIs, SDKs, and observability tools while shipping agents to production.
Lead product strategy for OlmoEarth, an AI platform for Earth observation that helps partners detect and classify geospatial features using satellite imagery and foundation models.
Build and own cloud infrastructure for an AI-driven materials discovery platform, focusing on GPU compute, CI/CD, and reproducibility to accelerate scientific breakthroughs.
Build and optimize AI-driven simulation models for engineering and manufacturing, scaling deep learning and distributed training across cloud and on-premise systems.
Build and own the shared AI platform that trains and serves Adobe’s generative AI models at global scale, focusing on GPU fleet utilization, low-latency inference, and end-to-end model deployment pipelines.
Build and scale AI agents and foundation models for drug discovery, integrating biological data and MLOps pipelines to accelerate therapeutic research.
Build and optimize distributed training infrastructure for GenAI models on AWS Trainium using PyTorch/JAX, tuning compiler, runtime, and parallelism to maximize throughput and hardware utilization.
Lead a team building and optimizing scalable ML training platforms on AWS/Kubernetes, focusing on GPU workloads, performance tuning, and Gen AI/LLM pipelines while enforcing enterprise governance and security standards.
We couldn't check your fit for this role — add a CV to your profile to see it next time.
