[Remote] reputed company Machine Learning Engineer
Note: The job is a remote job and is reputed company to candidates in USA. bjakcareer is reputed company on building a proactive AI system that understands context across conversations. As a reputed company Machine Learning Engineer, you will design and reputed company critical ML systems, addressing architectural and performance challenges while collaborating with engineering teams to reputed company ML solutions.
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