GT Principal Machine Learning Engineer, Artificial Intelligence (AI) Required, Work From Home Ginas Tech Jobs · Remote · US · Machine Learning Engineering $170,000–$200,000 12d ago

Aimlroles; San Francisco (CA); Remote; USD 210,000 - 320,000; Full time

Benefits offered by this job

Medical coverage

Dental & Vision

Savings plan

Paid time off

Job summary

Aimlroles is seeking a Principal Machine Learning Engineer to own and evolve critical AI systems across training, inference, and deployment. You will set technical standards, drive latency, cost, and reliability targets, and collaborate with cross-functional teams to ship production-grade ML solutions.

This remote role demands hands-on leadership, deep learning expertise, and the ability to balance architectural depth with pragmatic delivery and scalable ML infrastructure.

Qualifications

• Proficient in deep learning and transformer architectures.
• Experience deploying ML models in production at scale.
• Strong software engineering fundamentals for production-grade systems.
• Hands-on experience with GPU optimization and inference.
• Experience with RLHF pipelines or multimodal model deployment.

Responsibilities

• Architect and build large-scale ML systems (training, inference, evaluation, deployment).
• Design reproducible, high-performance training pipelines across GPU infrastructure.
• Architect inference systems balancing 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; partner with research leadership.
• Own production deployment with GPU optimization, memory efficiency, and latency reduction.
• Collaborate with application engineering to integrate ML systems into backend/mobile/desktop products.
• Ship improvements quickly while learning from real usage.
• Operate under production constraints: latency, cost, reliability, and safety.

Skills

Deep learning

Transformer architectures

AI systems

Distributed training

GPU optimization

LLM inference

PyTorch

JAX

Open-source

Tools

DeepSpeed

Megatron

Ray

TensorRT-LLM

vLLM

FasterTransformer

Apache Arrow

Spark

Kubernetes

PyTorch

Job description

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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