Research Engineer (Machine Learning)

Aldea is a multi-modal foundational AI company reimagining the scaling laws of intelligence. We believe today's architectures create unnecessary bottlenecks for the evolution of software. Our mission is to build the next generation of foundational models that power a more expressive, contextual, and intelligent human–machine interface.

The Role

We are hiring a Research Engineer (Machine Learning) to build the infrastructure that powers Aldea's multi-modal AI research. You will design, optimize, and scale the training and inference systems that enable our research team to explore next-generation architectures across language, speech, and multi-modal domains.

What You'll Do

Build and maintain distributed training infrastructure supporting researchers across language and speech domains at a billion-plus-parameter scale.

Optimize training and inference performance across the stack, delivering significant speedups through framework optimization, custom kernels, and system-level improvements.

Design experiment infrastructure including automated evaluation pipelines, experiment tracking, and monitoring systems that enable rapid iteration.

Scale infrastructure from single-node to multi-node distributed training and deploy production inference systems for real-time applications.

Support researchers with fast turnaround on infrastructure issues and maintain high reliability across all systems.

Collaborate with research scientists, data engineers, and leadership to define technical priorities and infrastructure roadmap.

Minimum Qualifications

Bachelor's degree in Computer Science, Engineering, or related field, or equivalent practical experience.

3+ years of experience with PyTorch and distributed training frameworks (DDP, FSDP, DeepSpeed, or similar).

Experience training large-scale deep learning models at 1B+ parameters.

Deep understanding of training optimization techniques including mixed precision, gradient checkpointing, and memory management.

Proven ability to build production-grade ML infrastructure with high reliability.

Track record of delivering significant performance optimizations in ML training or inference systems.

Preferred Qualifications

Experience with custom kernel development (CUDA, Triton) or GPU optimization.

Hands-on experience with large-scale pretraining (100B+ tokens, ideally trillion+ scale).

Experience optimizing inference for production : quantization, vLLM, TensorRT, or custom serving engines.

Familiarity with speech / audio ML systems and real-time inference constraints.

Experience building automated evaluation frameworks and experiment tracking systems.

Knowledge of profiling tools and multi-node training across 8-32+ GPUs.

Exposure to job orchestration systems (SLURM, Kubernetes, Ray).

Master's or PhD in Computer Science, Machine Learning, or related field.

Performance-based bonus aligned with research and model milestones

Equity participation

Comprehensive health, dental, and vision coverage

Flexible paid time off

Aldea is proud to be an equal‑opportunity employer. We are committed to building a diverse and inclusive culture that celebrates authenticity to win as one. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, citizenship or immigration status, or any other legally protected characteristics.

Aldea uses E‑Verify to confirm employment eligibility in compliance with federal law. For more information please visit : https : / / www.e-verify.gov

Please note : We do not accept unsolicited resumes from recruiters or employment agencies and will not be responsible for any fees related to unsolicited resumes.

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