Remote Machine Learning Researcher - Gensyn

Blockchain Works; London; Remote; GBP 60,000 - 100,000; Full time

Job summary

A leading company in machine learning is looking for a highly motivated researcher to join their team. This role involves training distributed models, researching innovative architectures, and publishing findings in top AI conferences. The ideal candidate will have a strong research background and experience in machine learning and distributed systems.

Benefits

Competitive salary + equity

Fully remote work

Company retreats

Flexible equipment

Paid sick leave

Health, vision, and dental insurance

Qualifications

• Extremely strong research background with publications.
• Hands-on experience with distributed model training.
• Self-motivated with excellent verbal and written communication skills.

Responsibilities

• Train highly distributed models over decentralized infrastructure.
• Research novel model architectures.
• Publish and collaborate with top-tier AI conferences.

Skills

Research background

Machine learning

Distributed systems

Communication

Job description

The world will be unrecognisable in 5 years.

Machine learning models aredriving our cars, testing oureyesight, detecting ourcancer, giving sight to theblind, giving speech to themute, and dictating whatwe consume, enjoy, and think. These AI systems are already an integral part of our lives and will shape our future as a species.

Soon, we'll conjure unlimited content: fromnever-ending TV series(where we’re the main character) topersonalised tutorsthat areinfinitely patientand leave no student behind. We’ll augment our memories withfoundation models—individually tailored to us throughRLHFand connected directly to ourthoughtsvia Brain-Machine Interfaces—blurring the lines between organic and machine intelligence and ushering in the next generation of human development.

This future demands immense, globally accessible, uncensorable, computational power. Gensyn is the machine learning compute protocol that translates machine learning compute into an always-on commodity resource—outside of centralised control and as ubiquitous as electricity—accelerating AI progress and ensuring that this revolutionary technology is accessible toall of humanitythrough a free market.

Our Principles:

AUTONOMY

• Don’t ask for permission - we have aconstraint culture, not a permission culture.
• Claim ownership of any work stream and set its goals/deadlines, rather than waiting to be assigned work or relying on job specs.
• Push & pull context on your work rather than waiting for information from others and assuming people know what you’re doing.
• No middle managers - we don’t (and will likely never) have middle managers.

FOCUS

• Small team - misalignment and politics scale super-linearly with team size. Small protocol teamsrivalmuch larger traditional teams.
• Thin protocol - build and designthinly.
• Reject waste - guard the company’s time, rather than wasting it in meetings without clear purpose/focus, orbikeshedding.

REJECT MEDIOCRITY

• Give direct feedback to everyone immediately rather than avoidingunpopularity, expecting things to improve naturally, ortradingshort-term pain for extreme long-term pain.
• Embrace an extreme learning rate rather than assuming limits to your ability/knowledge.

Responsibilities

Train highly distributed models- over uniquely decentralised and heterogeneous infrastructure, rather than GPU clusters

Research novel model architectures- design, build, test, and iterate over completely new ways of building neural networks; with an eye towards achieving byzantine tolerance in a trustless compute setting

Publish & collaborate- write research papers targeting top-tier AI conferences such as AAAI, ICML, IJCAI and NIPS, and collaborate with experts from universities and research institutes

Engineering support- work with the engineering team on wider issues concerning ML (e.g. reproducible training)

Follow best practices-build in the open with a keen focus on designing, testing, and documenting your code

Write & engage -contribute to technical reports/papers describing the system and discuss with the community

Minimum requirements

Extremely strong research background- with publications at major machine learning conferences (or commensurate industrial experience)

Strong background in machine learning and distributed systems

Hands-on experience with distributed model training

Highly self-motivated with excellent verbal and written communication skills

Comfortable working in an applied research environment -with extremely high autonomy and unpredictable timelines

Nice to haves

Communication backend experience -e.g. NCCL, GLOO and MPI

Experience training Large Language Models (LLMs)

Compensation / Benefits:

Competitive salary + share of equity and token pool

Fully remote work -we hire between the West Coast (PT) and Central Europe (CET) time zones

4x all expenses paid company retreats around the world, per year

Whatever equipment you need

️ Paid sick leave

Private health, vision, and dental insurance -including spouse/dependents [ only]