Note: The job is a remote job and is open to candidates in USA. Protingent is an Award-Winning provider of top-tier Engineering and IT talent, trusted by companies at the forefront of innovation. They are seeking a Principal Machine Learning Engineer who will be responsible for designing and evolving critical ML systems, collaborating with teams to integrate these systems into products, and ensuring high performance and reliability across all ML operations.
Responsibilities
- Architect and build large-scale 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 scale
- Design and maintain data systems for high-quality synthetic and real-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 integrate ML systems cleanly into backend, mobile, and desktop products
- Make pragmatic trade-offs and ship improvements quickly, learning from real usage
- Work under real production constraints: latency, cost, reliability, and safety
- ML systems (training, inference, evaluation) are reliable, scalable, and meet defined performance targets
- Models deployed to production achieve measurable quality improvements and meet user-impact goals
- Production issues are proactively monitored, debugged, and resolved with clear root-cause analysis
- Team and cross-functional collaborators benefit from clear guidance, best practices, and scalable ML solutions
- Research-to-production cycles are efficient, safe, and continuously improve the product experience
Skills
- Strong background in deep learning and transformer-based architectures
- Hands-on experience training, fine-tuning, or deploying large-scale ML models in production
- Proficiency with at least one modern ML framework (e.g. PyTorch, JAX), and ability to learn others quickly
- Experience with distributed training and inference frameworks (e.g. DeepSpeed, FSDP, Megatron, ZeRO, 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, zero-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 open-source 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-scale data processing (Apache Arrow, Spark, Ray)
Company Overview
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