Machine Learning Engineer — AI Architecture Research
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Before you apply
LocationRemote (world)
Work moderemote
Typefull-time
DepartmentResearch
EquityIncluded
Company size51–200 people
PostedJan 22, 2026 · 9mo ago
Verified live9h ago
May no longer be actively hiring
Posted by the company on Jan 22, 202671% of this company's open roles have been listed for 90+ days
At a glanceSummarised by Seekless from the posting.
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
Solid understanding of training dynamics, scaling behavior, and optimization
Solid understanding of 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
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
Skills
Machine Learning
Deep Learning
PyTorch
JAX
About the Role
We’re looking for a Machine Learning Engineer focused on AI architecture research to help design, prototype, and validate next-generation model architectures. You’ll work at the intersection of research and production — turning new ideas into scalable, real-world systems.
This role is ideal for someone who enjoys questioning architectural assumptions, experimenting with novel model designs, and pushing beyond standard Transformer-style approaches.
What You’ll Work On
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Research and develop new neural network architectures (e.g. alternatives or extensions to Transformers, recurrent / hybrid models, long-context systems)
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Design and run architecture-level experiments (scaling laws, memory mechanisms, compute trade-offs)
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Prototype models end-to-end — from research code to training-ready implementations
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Collaborate with inference and systems engineers to ensure architectures are deployable and efficient
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Analyze model behavior, failure modes, and inductive biases
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Read, reproduce, and extend cutting-edge research papers
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Contribute to internal research notes, benchmarks, and open-source efforts (where applicable)
What We’re Looking For
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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
Why Join
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Work on core model architecture, not just fine-tuning
•
Direct influence on the technical direction of a Series-A company
•
Small, high-caliber team with fast feedback loops
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Opportunity to ship research into production
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Competitive compensation + meaningful equity