Software Developer, Robotic Data & Deliveries
for each section heading. Use - for lists. Use
for prose.
We need to preserve all substantive information, not invent or summarize away detail. Remove boilerplate like EEO, AI recruitment note, etc. Also remove "A Note on Applying", "Inclusive Hiring at Mecka", "Use of Artificial Intelligence in Recruitment". Also remove "About Mecka AI" maybe we keep as part of About the role? The instruction: "Do NOT repeat the job title as a heading (the page already shows it)". So we can have About the role heading and include description of company and role.
We need to ensure we don't include filler like "commensurate with experience". Not present.
Let's extract content:
About Mecka AI: paragraph about building data infrastructure layer for robotics and embodied AI, design and operate global systems for data capture, labeling, hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems. Work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware.
The Role: hiring a Software Developer to build — end to end — the internal products and tooling that turn Mecka’s ego-data pipeline into fast, reliable, at-spec dataset deliveries to frontier AI labs. This is a 0→1 role: you’ll take a rough delivery problem, design the interface and the system behind it, ship a first version fast, and iterate with the people using it. You’ll own whole surfaces rather than a single layer — the review and QA UIs our labeling and delivery teams live in, the dashboards sales uses to track a delivery, the export and validation logic underneath, and the data plumbing that ties it together. Front-end one week, data pipeline the next, a scrappy internal tool the week after. You’ll work hand-in-hand with the Delivery Operations team — including our Robotic Data & Deliveries TPM — turning recurring delivery pain into products people actually want to use. When a workflow is slow, manual, or fragile, you’re the one who builds the thing that fixes it.
What you will be doing: bullet list:
- Internal Products & Delivery Tooling: Design and build the internal products our teams use every day — dataset review and QA interfaces, delivery-tracking dashboards, data viewers, and the workflows that string them together — owning both the UI and the logic behind it.
- Turn one-off scripts and manual steps into real tools with a usable interface, so delivery, sales, labeling, and QA move faster without engineering babysitting.
- 0→1 Product Building: Take fuzzy problems from a conversation to a shipped v1 quickly, then iterate with real users — internal teams today, and the experience AI-lab customers touch when they receive a delivery. Make pragmatic build-vs-buy and scope calls; ship the 80% that unblocks people now and harden it as it proves out.
- Data & Platform Plumbing: Build the pipelines and APIs that package, validate, index, and serve ego datasets — the systems that make petabyte-scale video and sensor data usable for delivery, labeling, QA, and model training. Keep the tools you ship reliable and honest about data quality, so problems get caught before a dataset reaches a customer.
Who You Are:
- 3+ years building software as a full-stack or product engineer (or equivalent impact) — you’re comfortable owning a feature from UI to data.
- Fluent in a modern front-end stack (e.g., TypeScript/React) and at least one backend language (e.g., Python, Node, Go).
- A demonstrated 0→1 builder — you’ve taken tools or products from nothing to in-use, and you thrive in ambiguity without a detailed spec.
- Strong product sense and a bias to ship: you scope ruthlessly, get a v1 in front of users, and iterate.
- Comfortable working directly with data — querying databases (e.g., SQL, MongoDB) and reshaping or validating datasets.
Even better if you have:
- You’ve been an early or founding engineer, built internal tools teams loved, or shipped side projects end to end.
- You have real UX/design instincts — your tools are fast, clear, and pleasant to use, not just functional.
- You think in terms of the user, clean interfaces, and shipping — and you write maintainable code with strong ownership.
- Exposure to video/media or large-dataset systems (ingestion, storage, metadata/search, ETL) is a plus, not a requirement.
Why This Role: Own real surface area from day one — build the products that power petabyte-scale robotics datasets and the deliveries frontier AI labs depend on. Maximum breadth: front-end, back-end, data, and product, on problems that are still greenfield. High ownership and direct impact across delivery, operations, sales, and research — paired tightly with the TPM who owns the delivery programs you build for.
Compensation Range: $150K - $200K
We need to output sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. But we should only include sections with real specific content. We have content for About the role (company + role description). Responsibilities (What you will be doing). Requirements/Qualifications (Who You Are + Even better if...). Skills maybe we can extract from fluency etc. Could combine Requirements and Qualifications? The instruction says use headings for those specific names. We could have separate sections: Requirements, Qualifications, Skills. But we need to avoid thin sections. Perhaps we can combine Requirements and Qualifications into one? But headings are prescribed; we must use those headings if we include them. We could include Requirements and Qualifications as separate but ensure they have substantive content. We can split: Requirements: the core requirements (3+ years, full-stack, etc). Qualifications: the "Even better if you have:" items. Skills: maybe list the specific tech stacks (TypeScript/React, Python/Node/Go, SQL/MongoDB). However that may be thin but still substantive. Let's decide.
We have:
- About the role: combine company description and role overview.
- Responsibilities: the bullet list under "What you will be doing".
- Requirements: the bullet list under "Who You Are".
- Qualifications: the bullet list under "Even better if you have".
- Skills: maybe we can list the specific technologies mentioned: TypeScript, React, Python, Node, Go, SQL, MongoDB. Could also mention front-end stack, backend language, data querying. That seems reasonable.
- Pay: compensation range.
- Benefits: none -> omit.
- Schedule: none -> omit.
We must not include filler like "commensurate with experience". Not present.
Now produce HTML fragment:
About the role
Mecka AI is building the data infrastructure layer for robotics and embodied AI. We design and operate global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems. We work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware. The Role: We are hiring a Software Developer to build — end to end — the internal products and tooling that turn Mecka’s ego-data pipeline into fast, reliable, at-spec dataset deliveries to frontier AI labs. This is a 0→1 role: you’ll take a rough delivery problem, design the interface and the system behind it, ship a first version fast, and iterate with the people using it. You’ll own whole surfaces rather than a single layer — the review and QA UIs our labeling and delivery teams live in, the dashboards sales uses to track a delivery, the export and validation logic underneath, and the data plumbing that ties it together. Front-end one week, data pipeline the next, a scrappy internal tool the week after. You’ll work hand-in-hand with the Delivery Operations team — including our Robotic Data & Deliveries TPM — turning recurring delivery pain into products people actually want to use. When a workflow is slow, manual, or fragile, you’re the one who builds the thing that fixes it.
Responsibilities
- Internal Products & Delivery Tooling: Design and build the internal products our teams use every day — dataset review and QA interfaces, delivery-tracking dashboards, data viewers, and the workflows that string them together — owning both the UI and the logic behind it.
- Turn one-off scripts and manual steps into real tools with a usable interface, so delivery, sales, labeling, and QA move faster without engineering babysitting.
- 0→1 Product Building: Take fuzzy problems from a conversation to a shipped v1 quickly, then iterate with real users — internal teams today, and the experience AI-lab customers touch when they receive a delivery. Make pragmatic build-vs-buy and scope calls; ship the 80% that unblocks people now and harden it as it proves out.
- Data & Platform Plumbing: Build the pipelines and APIs that package, validate, index, and serve ego datasets — the systems that make petabyte-scale video and sensor data usable for delivery, labeling, QA, and model training. Keep the tools you ship reliable and honest about data quality, so problems get caught before a dataset reaches a customer.
Requirements
- 3+ years building software as a full-stack or product engineer (or equivalent impact) — you’re comfortable owning a feature from UI to data.
- Fluent in a modern front-end stack (e.g., TypeScript/React) and at least one backend language (e.g., Python, Node, Go).
- A demonstrated 0→1 builder — you’ve taken tools or products from nothing to in-use, and you thrive in ambiguity without a detailed spec.
- Strong product sense and a bias to ship: you scope ruthlessly, get a v1 in front of users, and iterate.
- Comfortable working directly with data — querying databases (e.g., SQL, MongoDB) and reshaping or validating datasets.
Qualifications
- You’ve been an early or founding engineer, built internal tools teams loved, or shipped side projects end to end.
- You have real UX/design instincts — your tools are fast, clear, and pleasant to use, not just functional.
- You think in terms of the user, clean interfaces, and shipping — and you write maintainable code with strong ownership.
- Exposure to video/media or large-dataset systems (ingestion, storage, metadata/search, ETL) is a plus, not a requirement.
Skills
- TypeScript, React
- Python, Node, Go
- SQL, MongoDB
Pay
Compensation Range: $150K - $200K
We should not include Benefits or Schedule.
Check for any leftover boilerplate: we removed
for prose. We need to preserve all substantive information, not invent or summarize away detail. Remove boilerplate like EEO, AI recruitment note, etc. Also remove "A Note on Applying", "Inclusive Hiring at Mecka", "Use of Artificial Intelligence in Recruitment". Also remove "About Mecka AI" maybe we keep as part of About the role? The instruction: "Do NOT repeat the job title as a heading (the page already shows it)". So we can have About the role heading and include description of company and role. We need to ensure we don't include filler like "commensurate with experience". Not present. Let's extract content: About Mecka AI: paragraph about building data infrastructure layer for robotics and embodied AI, design and operate global systems for data capture, labeling, hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems. Work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware. The Role: hiring a Software Developer to build — end to end — the internal products and tooling that turn Mecka’s ego-data pipeline into fast, reliable, at-spec dataset deliveries to frontier AI labs. This is a 0→1 role: you’ll take a rough delivery problem, design the interface and the system behind it, ship a first version fast, and iterate with the people using it. You’ll own whole surfaces rather than a single layer — the review and QA UIs our labeling and delivery teams live in, the dashboards sales uses to track a delivery, the export and validation logic underneath, and the data plumbing that ties it together. Front-end one week, data pipeline the next, a scrappy internal tool the week after. You’ll work hand-in-hand with the Delivery Operations team — including our Robotic Data & Deliveries TPM — turning recurring delivery pain into products people actually want to use. When a workflow is slow, manual, or fragile, you’re the one who builds the thing that fixes it. What you will be doing: bullet list: - Internal Products & Delivery Tooling: Design and build the internal products our teams use every day — dataset review and QA interfaces, delivery-tracking dashboards, data viewers, and the workflows that string them together — owning both the UI and the logic behind it. - Turn one-off scripts and manual steps into real tools with a usable interface, so delivery, sales, labeling, and QA move faster without engineering babysitting. - 0→1 Product Building: Take fuzzy problems from a conversation to a shipped v1 quickly, then iterate with real users — internal teams today, and the experience AI-lab customers touch when they receive a delivery. Make pragmatic build-vs-buy and scope calls; ship the 80% that unblocks people now and harden it as it proves out. - Data & Platform Plumbing: Build the pipelines and APIs that package, validate, index, and serve ego datasets — the systems that make petabyte-scale video and sensor data usable for delivery, labeling, QA, and model training. Keep the tools you ship reliable and honest about data quality, so problems get caught before a dataset reaches a customer. Who You Are: - 3+ years building software as a full-stack or product engineer (or equivalent impact) — you’re comfortable owning a feature from UI to data. - Fluent in a modern front-end stack (e.g., TypeScript/React) and at least one backend language (e.g., Python, Node, Go). - A demonstrated 0→1 builder — you’ve taken tools or products from nothing to in-use, and you thrive in ambiguity without a detailed spec. - Strong product sense and a bias to ship: you scope ruthlessly, get a v1 in front of users, and iterate. - Comfortable working directly with data — querying databases (e.g., SQL, MongoDB) and reshaping or validating datasets. Even better if you have: - You’ve been an early or founding engineer, built internal tools teams loved, or shipped side projects end to end. - You have real UX/design instincts — your tools are fast, clear, and pleasant to use, not just functional. - You think in terms of the user, clean interfaces, and shipping — and you write maintainable code with strong ownership. - Exposure to video/media or large-dataset systems (ingestion, storage, metadata/search, ETL) is a plus, not a requirement. Why This Role: Own real surface area from day one — build the products that power petabyte-scale robotics datasets and the deliveries frontier AI labs depend on. Maximum breadth: front-end, back-end, data, and product, on problems that are still greenfield. High ownership and direct impact across delivery, operations, sales, and research — paired tightly with the TPM who owns the delivery programs you build for. Compensation Range: $150K - $200K We need to output sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. But we should only include sections with real specific content. We have content for About the role (company + role description). Responsibilities (What you will be doing). Requirements/Qualifications (Who You Are + Even better if...). Skills maybe we can extract from fluency etc. Could combine Requirements and Qualifications? The instruction says use headings for those specific names. We could have separate sections: Requirements, Qualifications, Skills. But we need to avoid thin sections. Perhaps we can combine Requirements and Qualifications into one? But headings are prescribed; we must use those headings if we include them. We could include Requirements and Qualifications as separate but ensure they have substantive content. We can split: Requirements: the core requirements (3+ years, full-stack, etc). Qualifications: the "Even better if you have:" items. Skills: maybe list the specific tech stacks (TypeScript/React, Python/Node/Go, SQL/MongoDB). However that may be thin but still substantive. Let's decide. We have: - About the role: combine company description and role overview. - Responsibilities: the bullet list under "What you will be doing". - Requirements: the bullet list under "Who You Are". - Qualifications: the bullet list under "Even better if you have". - Skills: maybe we can list the specific technologies mentioned: TypeScript, React, Python, Node, Go, SQL, MongoDB. Could also mention front-end stack, backend language, data querying. That seems reasonable. - Pay: compensation range. - Benefits: none -> omit. - Schedule: none -> omit. We must not include filler like "commensurate with experience". Not present. Now produce HTML fragment:
About the role
Mecka AI is building the data infrastructure layer for robotics and embodied AI. We design and operate global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems. We work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware. The Role: We are hiring a Software Developer to build — end to end — the internal products and tooling that turn Mecka’s ego-data pipeline into fast, reliable, at-spec dataset deliveries to frontier AI labs. This is a 0→1 role: you’ll take a rough delivery problem, design the interface and the system behind it, ship a first version fast, and iterate with the people using it. You’ll own whole surfaces rather than a single layer — the review and QA UIs our labeling and delivery teams live in, the dashboards sales uses to track a delivery, the export and validation logic underneath, and the data plumbing that ties it together. Front-end one week, data pipeline the next, a scrappy internal tool the week after. You’ll work hand-in-hand with the Delivery Operations team — including our Robotic Data & Deliveries TPM — turning recurring delivery pain into products people actually want to use. When a workflow is slow, manual, or fragile, you’re the one who builds the thing that fixes it.
Responsibilities
- Internal Products & Delivery Tooling: Design and build the internal products our teams use every day — dataset review and QA interfaces, delivery-tracking dashboards, data viewers, and the workflows that string them together — owning both the UI and the logic behind it.
- Turn one-off scripts and manual steps into real tools with a usable interface, so delivery, sales, labeling, and QA move faster without engineering babysitting.
- 0→1 Product Building: Take fuzzy problems from a conversation to a shipped v1 quickly, then iterate with real users — internal teams today, and the experience AI-lab customers touch when they receive a delivery. Make pragmatic build-vs-buy and scope calls; ship the 80% that unblocks people now and harden it as it proves out.
- Data & Platform Plumbing: Build the pipelines and APIs that package, validate, index, and serve ego datasets — the systems that make petabyte-scale video and sensor data usable for delivery, labeling, QA, and model training. Keep the tools you ship reliable and honest about data quality, so problems get caught before a dataset reaches a customer.
Requirements
- 3+ years building software as a full-stack or product engineer (or equivalent impact) — you’re comfortable owning a feature from UI to data.
- Fluent in a modern front-end stack (e.g., TypeScript/React) and at least one backend language (e.g., Python, Node, Go).
- A demonstrated 0→1 builder — you’ve taken tools or products from nothing to in-use, and you thrive in ambiguity without a detailed spec.
- Strong product sense and a bias to ship: you scope ruthlessly, get a v1 in front of users, and iterate.
- Comfortable working directly with data — querying databases (e.g., SQL, MongoDB) and reshaping or validating datasets.
Qualifications
- You’ve been an early or founding engineer, built internal tools teams loved, or shipped side projects end to end.
- You have real UX/design instincts — your tools are fast, clear, and pleasant to use, not just functional.
- You think in terms of the user, clean interfaces, and shipping — and you write maintainable code with strong ownership.
- Exposure to video/media or large-dataset systems (ingestion, storage, metadata/search, ETL) is a plus, not a requirement.
Skills
- TypeScript, React
- Python, Node, Go
- SQL, MongoDB
Pay
Compensation Range: $150K - $200K
We should not include Benefits or Schedule. Check for any leftover boilerplate: we removed