Machine Learning Engineer – AI Architecture Research

Job not on LinkedIn

🕒 January 23

Apply Now

Find Similar Remote Jobs

Receive Emails For Remote Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Featherless AI

WebsiteLinkedInAll Job Openings

1 - 10 employees

Founded 2023

🤖 Artificial Intelligence

☁️ SaaS

🔌 API

Artificial Intelligence • SaaS • API

Featherless AI is a serverless AI inference and model hosting provider that offers API access to a large and growing catalog of open-weight models (12,200+), enabling developers and businesses to deploy, fine-tune, and run models at scale without managing servers. The company provides flat subscription pricing with unlimited tokens, GPU orchestration, private/anonymous usage (no logs), and options for enterprise self-hosting or scale units for high concurrency. Featherless AI also operates as an AI research lab focused on open-source and post-transformer model research, claiming significant cost and performance improvements for large models and AI agents.

📋 Description

• Research and develop new neural network architectures (e.g. alternatives or extensions to Transformers, recurrent / hybrid models, long-context systems) • Design and run architecture-level experiments (scaling laws, memory mechanisms, compute trade-offs) • Prototype models end-to-end — from research code to training-ready implementations • Collaborate with inference and systems engineers to ensure architectures are deployable and efficient • Analyze model behavior, failure modes, and inductive biases • Read, reproduce, and extend cutting-edge research papers • Contribute to internal research notes, benchmarks, and open-source efforts (where applicable)

🎯 Requirements

• Strong background in machine learning fundamentals and deep learning • Hands-on experience implementing model architectures from scratch • Solid understanding of: • Attention mechanisms, RNNs, state-space models, or hybrid architectures • Training dynamics, scaling behavior, and optimization • Memory, latency, and compute constraints at the model level • Comfortable working in PyTorch or JAX • Ability to move fluidly between theory, experimentation, and engineering • Clear communicator who can explain architectural trade-offs • Nice to Have • Experience with non-Transformer architectures (RNN variants, SSMs, long-context models) • Background in research-driven startups or open-source ML projects • Experience with large-scale training or custom training loops • Publications, preprints, or notable research contributions • Familiarity with inference optimization and deployment constraints

🏖️ Benefits

• Competitive compensation + meaningful equity