Principal Machine Learning Engineer, Artificial Intelligence (AI) Required, Work From Home
At a glance
Senior Machine Learning Engineering role at Ginas Tech Jobs. Remote, United States · full-time · $170,000–$200,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Design ML systems across data, model training, evaluation, inference, and deployment
- Build repeatable GPU training pipelines and tune inference for latency, throughput, cost, and reliability
- Maintain real-world and synthetic data systems, plus evaluations for quality, safety, robustness, and bias
- Run production deployments, optimizing GPU use, memory, latency, and scaling
- Work with application engineers to connect ML systems to backend, mobile, and desktop products
What you bring
- Strong deep-learning knowledge, including transformer architectures, and practical AI experience
- You have trained, fine-tuned, or deployed large ML models in production
- You can build production-grade software and have used a modern ML framework such as PyTorch or JAX
- Experience with distributed training and inference, plus GPU tuning for memory use, quantization, and mixed precision
Who this fits
This is a hands-on principal-level role for someone who sets technical direction across ML systems while solving architecture and performance problems. The position is fully remote.
From the employer
Principal Machine Learning Engineer, Artificial Intelligence (AI) Required, Work From Home
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. The Principal Machine Learning Engineer will 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. This position is 100% Remote.
Principal Machine Learning Engineer 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
Principal Machine Learning Engineer Outcomes:
- 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.
Principal Machine Learning Engineer Qualifications:
- Strong background in deep learning and transformer-based architectures.
- Artificial Intelligence (AI) experience required.
- 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).
Benefits include medical insurance, Dental, Vision, Savings Plan Options, PTO, etc.
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