Machine Learning Engineer — AI Architecture Research
Remote (world)
- Pay
- Pay not listed
- Work setting
Remote stated — work setting source
Listed location: Remote (world)
Read the full posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
Research — team source
Department: Research
From the employer’s posting
What you’ll work on
Full postingWe’re looking for a Machine Learning Engineer focused on AI architecture research to help design, prototype, and validate next-generation model architectures.
This role is ideal for someone who enjoys questioning architectural assumptions, experimenting with novel model designs, and pushing beyond standard Transformer-style approaches.
Research and develop new neural network architectures, such as Transformer alternatives and hybrid models
Design and run architecture-level experiments on scaling, memory, and compute trade-offs
Prototype models end-to-end, from research code to training-ready implementations
Work with inference and systems engineers to make architectures deployable and efficient
Analyze model behavior, failure modes, and inductive biases
From the employer’s posting
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.
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
What you’ll bring
All qualificationsCore experience
- Strong background in machine learning fundamentals and deep learning
- Hands-on experience implementing model architectures from scratch
- Solid understanding of attention, RNNs, state-space models, or hybrid architectures
- Comfortable working in PyTorch or JAX
- Clear communicator who can explain architectural trade-offs
Qualification wording
Strong background in machine learning fundamentals and deep learning
Hands-on experience implementing model architectures from scratch
Attention mechanisms, RNNs, state-space models, or hybrid architectures
Comfortable working in PyTorch or JAX
Clear communicator who can explain architectural trade-offs
Tools in this posting
- PyTorch
Source — Tool mentions in context
- Memory, latency, and compute constraints at the model level - Comfortable working in PyTorch or JAX - Ability to move fluidly between theory, experimentation, and engineering
Posting record
Report an errorOpen 4 monthsWe first saw this listing 144 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jun 2, 2026 · 144 days ago
- Recorded sightings
- 83
- Last seen by us
- Oct 24, 2026
- Employer says posted
- Jan 22, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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Job description
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
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)
What We’re Looking For
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
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
Opportunity to ship research into production
Competitive compensation + meaningful equity
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Remote (world)
What You’ll Work On - 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)
More source context
- Solid understanding of: - Attention mechanisms, RNNs, state-space models, or hybrid architectures - Training dynamics, scaling behavior, and optimization
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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