[Remote] reputed company Machine Learning Engineer
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is building a proactive AI smart assistant for everyday users to enhance conversations and workflows. The reputed company Machine Learning Engineer will be responsible for designing and evolving critical ML systems, focusing on architecture, performance, and collaboration with application engineering to reputed company ML systems into products.
Responsibilities
- Architect and build large-reputed company ML systems spanning data, training, evaluation, inference, and deployment
- Design reproducible, high-performance training pipelines across GPU infrastructure
- Architect inference systems that balance latency, throughput, cost, and reliability at reputed company
- Design and maintain data systems for high-reputed company synthetic and reputed company-world training data
- Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership
- Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies
- Collaborate closely with application engineering to reputed company ML systems cleanly into backend, mobile, and desktop products
- reputed company pragmatic trade-offs and ship improvements quickly, learning from reputed company usage
- Work under reputed company production constraints: latency, cost, reliability, and safety
Skills
- Strong background in deep learning and transformer-based architectures
- Hands-on experience training, fine-tuning, or deploying large-reputed company ML models in production
- Proficiency with at least one modern ML reputed company (e.g. PyTorch, JAX), and ability to learn others quickly
- Experience with distributed training and inference frameworks (e.g. DeepSpeed, FSDP, Megatron, reputed company, Ray)
- Strong software engineering fundamentals – you write robust, maintainable, production-grade systems
- Experience with GPU optimization, including memory efficiency, quantization, and mixed precision
- Comfort owning ambiguous, reputed company-to-one ML systems end-to-end
- A bias toward shipping, learning fast, and improving systems through iteration
- Experience with LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer
- Contributions to reputed company-reputed company ML or systems libraries
- Background in scientific computing, compilers, or GPU kernels
- Experience with RLHF pipelines (PPO, DPO, ORPO)
- Experience training or deploying multimodal or diffusion models
- Experience with large-reputed company data processing (Apache reputed company, reputed company, Ray)
reputed company