Position Description: Protingent Staffing has an exciting direct hire Principal Machine Learning Engineerwith our client that is fully remote.
Job Description:
As a Principal Machine Learning Engineer, you are a deep technical authority responsible for designing and evolving the most critical ML systems in the company.
You operate across training, inference, evaluation, and infrastructure, solving the hardest architectural and performance problems.
While Technical Leads may own execution at the team level, you set the technical standard and shape how ML systems are built across the organization.
This is a hands-on, high-impact role focused on depth.
Job 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.
Job Qualifications:
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).
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.
Must Have:
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).
Job Details:
Job Type: Direct Hire
Pay Range: Market Rate
Location: Fully Remote.
About Protingent: Protingent is an Award-Winning provider of top-tier Engineering and IT talent, trusted by companies at the forefront of innovation — from Software and Aerospace to AI, Clean Tech, Medical Devices, and Connected Technologies. We’re passionate about making a positive impact by connecting exceptional talent with meaningful opportunities and helping our clients build the future.
Numbers & Facts
Location
-, WA (Remote)
Skills
Aerospace and Defenseunmatched
Apache Sparkunmatched
Architectural Servicesunmatched
Artificial Intelligence (AI)unmatched
Best Practicesunmatched
Clean Technologiesunmatched
Continuous Improvementunmatched
Cross-Functionalunmatched
Customer Support/Serviceunmatched
Data Processingunmatched
Debugging Skillsunmatched
Deep Learningunmatched
Desktop PCunmatched
GPU (Graphics Processing Unit)unmatched
Information Technology & Information Systemsunmatched
JAX (Java API for XML)unmatched
Kernel Programmingunmatched
Large-Scale Systemsunmatched
Leadershipunmatched
Machine Learningunmatched
Medical Equipmentunmatched
Memory Hardwareunmatched
Mobile Devicesunmatched
Open Sourceunmatched
Preferred Provider Organization (PPO)unmatched
Production Systemsunmatched
Quality Metricsunmatched
Root Cause Analysisunmatched
Software Engineeringunmatched
System Architectureunmatched
System Integration (SI)unmatched
Systems Engineeringunmatched
Systems Maintenanceunmatched
Technical Leadershipunmatched
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