Job Position | Company | Posted | Location | Salary | Tags |
---|---|---|---|---|---|
Affine.io | Remote | $150k - $500k | |||
Affine.io | Remote | $140k - $250k | |||
Kronosresearch | Remote | $121k - $125k | |||
Genies | Remote | $36k - $56k | |||
Learn job-ready web3 skills on your schedule with 1-on-1 support & get a job, or your money back. | | by Metana Bootcamp Info | |||
Genies | Remote | $36k - $56k | |||
Brave | Remote | $126k - $131k | |||
travoom | Austin, TX, United States | $92k - $125k | |||
MoonPay | United Kingdom | $96k - $108k | |||
Bitso | Latin America | $126k - $131k | |||
Genies | Remote | $90k - $118k | |||
Zscaler | Remote | $122k - $150k | |||
Tether | Medellin, Colombia | $115k - $120k | |||
Tether | Dublin, Ireland | $115k - $120k | |||
Tether | TI Lugano CH | $115k - $120k | |||
Tether | Stockholm, Sweden | $115k - $120k |
About Affine
Affine is building an incentivized RL environment that pays miners for incremental improvements on tasks like program synthesis and coding. Operating on Bittensor's Subnet 120, we’ve created a sybil-proof, decoy-proof, copy-proof, and overfitting-proof mechanism that rewards genuine model improvements. Our vision is to commoditize reasoning—the highest form of intelligence—by directing and aggregating the work of a large, non-permissioned group on RL tasks to break the intelligence sound barrier.
Overview
We’re looking for research-minded engineers who can push the frontier of reinforcement learning, program synthesis, and reasoning agents inside Affine’s competitive RL environments. This role is about experimentation and discovery: designing new post-training methods, exploring agent architectures, and proving them in live competitive benchmarks. You’ll take cutting-edge theory (GRPO, PPO, multi-objective RL, program abduction) and turn it into working systems that miners can improve, validate, and monetize through Affine, Bittensor’s Subnet-120.
This is a rare opportunity to help reshape how AI is trained, evaluated, and aligned in a decentralized ecosystem. The position is ideal for someone who thrives at the intersection of research and engineering—able to prototype novel algorithms quickly, evaluate them rigorously, and scale them into production pipelines that feed back into Affine’s incentive system.
Responsibilities
- Design decentralized RL systems that incentivize miners to train, refine, and host high-quality agentic LLMs on the Bittensor subnet.
- Develop evaluation frameworks to assess model performance, safety, and alignment—including task design, metrics, adversarial testing, and red-teaming.
- Advance RL for agentic models by researching and applying cutting-edge RL and alignment techniques to improve the training–evaluation loop.
- Prototype and scale algorithms: explore new agent architectures and post-training methods, then build reproducible pipelines for finetuning, evaluation, and data flow.
- Contribute to live competitive benchmarks, deploying new approaches in production and ensuring the system rewards genuine intelligence gains rather than gaming.
Requirements
- Reinforcement Learning expertise with deep knowledge and hands-on experience in RL algorithms, design, and tuning. Background in multi-agent systems, mechanism design, or RLHF is a strong plus.
- Strong engineering skills in Python and experience building production-level ML systems with PyTorch, JAX, or TensorFlow.
- Distributed systems experience, with comfort designing and scaling high-performance, reliable infrastructure.
- Knowledge of LLMs and tool use, including how models interact with APIs, external tools, and function calling.
- Advanced academic or practical background: Master’s or PhD in a relevant field, or equivalent applied research and engineering experience.
Nice-to-Haves
- Publications in leading AI/ML conferences (NeurIPS, ICML, ICLR, AAAI), especially in RL, game theory, AI safety, or decentralized AI.
- Experience with virtualization and sandboxed code execution environments for safe tool use.
- Knowledge of game theory and advanced mechanism design.
- Contributions to significant open-source RL or LLM projects.
Is machine learning a good career?
Yes, machine learning is a rapidly growing field and can be a very promising career option for those interested in it
As businesses and industries increasingly rely on data to drive decision-making, there is a growing need for skilled professionals who can analyze and make sense of this data
Machine learning, which involves developing algorithms that can learn from and make predictions on large datasets, is a crucial part of this process
Machine learning careers can range from data analysts, machine learning engineers, data scientists, and more
These professionals work in a variety of industries, including finance, healthcare, e-commerce, and technology
The demand for machine learning experts is high, and the salaries in this field are also generally quite competitive
However, it's important to note that machine learning can be a complex field that requires a strong background in mathematics, statistics, and computer science
It also requires ongoing learning and staying up-to-date with the latest developments and tools in the field
If you enjoy working with data, have a strong interest in programming, and are willing to put in the effort to stay current with developments, a career in machine learning can be very rewarding.