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About Telegraph and why robotics
Telegraph is the network that turns intelligence into a graded commodity. Ranked by performance. Driven by demand. Providers plug in machine-callable capabilities â models, APIs, datasets, tools â and are ranked per Intent on measured performance. Consumers plug in once and access ranked intelligence for the job, paid per request in USDC.
The protocol is technology-agnostic by design. The same architecture can support intelligence ranging from weather forecasting to object detection and path planning, with task-specific interfaces and evaluation. Robots are among the most demanding consumers of intelligence: they need many kinds at once â perception, mapping, localisation, task planning, language, simulation, environment data â and each must meet the performance and reliability requirements of the task. Telegraph is building an open, continuously evaluated market for that intelligence. This role exists to bring the robotics industry into that market.
The role
Robotics Lead â a technical-commercial hire who opens the robotics vertical on Telegraph, end to end. Remote, full-time, reporting to the CEO. This is not a pure research or sales role. Telegraph's core protocol is designed to support intelligence across domains. The work is to bring robotics companies onto the network on both sides â as Providers selling the intelligence they have built, and as Consumers buying the intelligence their robots need â and turn their technical requirements into Intents Telegraph can rank.Â
You should also be capable of building the initial integrations yourself: connecting robotics services through APIs, developing adapters and prototypes, and demonstrating working end-to-end use cases. Telegraph's engineering team will support protocol-specific development, infrastructure, and changes to the core network. The scope is intelligence a robot can consume through suitable network interfaces: perception and detection models, maps and localisation services, task and motion planning, simulation and synthetic data, fleet analytics, environment and site data. It is not the on-board, millisecond control loop.
You should also be capable of building the initial integrations yourself: connecting robotics services through APIs, developing adapters and prototypes, and demonstrating working end-to-end use cases. Telegraph's engineering team will support protocol-specific development, infrastructure, and changes to the core network. The scope is intelligence a robot can consume through suitable network interfaces: perception and detection models, maps and localisation services, task and motion planning, simulation and synthetic data, fleet analytics, environment and site data. It is not the on-board, millisecond control loop.
What you'll do
- Build the pipeline. Map the robotics landscape â humanoid and mobile robot makers, warehouse and logistics automation, drones, autonomous vehicles, robotics model labs and simulation companies â and open conversations through your network and warm second-degree introductions.
- Onboard Providers. Bring robotics model and data teams onto Telegraph as Miners. Get their capabilities behind APIs, registered against the right Intents and earning from paid requests.
- Onboard Consumers. Work with robotics teams to find which off-board intelligence they buy or build today, and route that demand through Telegraph.
- Build integrations and demos. Develop robotics API adapters, working prototypes, and end-to-end demonstrations. Help Providers and Consumers integrate with Telegraph, working alongside the protocol engineering team where necessary.
- Capture technical needs. Sit with engineering teams, document what their robots need â inputs, outputs, latency budgets, accuracy requirements, data formats including vision, point clouds and sensor streams â and turn those needs into clear Intent specifications.
- Seed new Intents. Where demand exists but no Provider does, scope an Intent Bounty to attract supply, and help Evaluator developers build the benchmarks that rank Providers for it.
- Feed the roadmap. Report what blocks robotics adoption â non-text modalities, streaming, latency, jurisdiction constraints â so engineering knows what to build first.
- Shape the ads. Work with the ads team on social ads that target robotics companies and convert them, as both Providers and Consumers.
- Close. Move deals from interest to pilot, to signed mainnet commitment, to paid usage.
What success looks like
Outcomes count; introductions don't. Each stage counts more than the one before.
- Intro or expressed interestÂ
- Pilot agreed (Provider or Consumer)
- Signed mainnet commitment
- Paid usage or revenue on the network
- Pilot agreed (Provider or Consumer)
- Signed mainnet commitment
- Paid usage or revenue on the network
By the end of the first two months: a named target list of robotics companies, a set of robotics Intents specified with real customer requirements, the first robotics Providers live as Miners, working integration demos, and the first pilots signed ahead of or at mainnet.
The ultimate measure of success is real robotics intelligence being supplied, consumed, evaluated, and paid for through Telegraph.
Who you are
Must have
- A working network in robotics â founders, CTOs, heads of autonomy or perception at robotics companies who will take your call.
- Enough technical depth to talk to robotics engineers as a peer: perception, SLAM and localisation, planning, sim-to-real, sensor data. You can read a specification and spot what's missing.
- Hands-on engineering ability. Experience building robotics integrations, working with APIs, and developing prototypes using technologies such as ROS 2, Python, C++, and robotics simulation tools. You can write code and get a working integration running yourself.
- Strong commercial instincts. You can identify a real customer need, explain a technical solution, manage relationships, and turn an opportunity into a pilot or paid engagement.
- Comfort working independently, remotely, and fast in a lean team of nine.
- Fluent English, with exceptional written and verbal communication skills.
Nice to have
- A PhD or research background in robotics, computer vision or ML â useful for credibility with labs, not required.
- Experience with model marketplaces, APIs or developer platforms.
- Experience developing robotics benchmarks, evaluation systems or simulation-based testing.
- Contacts in Asian robotics and manufacturing.
No prior blockchain, cryptocurrency, USDC, or x402 experience is required. Telegraph's engineering team handles the underlying protocol and payment infrastructure. What matters is your ability to understand robotics, build integrations, and bring real customers onto the network.
Engagement terms
- Full-time, fully remote.
- Salary and equity, negotiated based on experience.
- Performance-based incentives may be discussed.
How to apply
- Ten robotics companies you could get a meeting with in the next 30 days, and who you'd speak to.
- Five intelligence capabilities robots need today that have no clear best provider.
- A deal you would try to close with a technical buyer, and what it would look like.
Note: Because of the high volume of applications, we only accept applications through the email address above. Messages sent via LinkedIn or other channels wonât be seen.
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.