Software Engineer

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.

We're hiring Software Engineers who own problems end to end — not a single layer of the stack. You'll ship production software that powers Mecka's data platform and the tools our operators, researchers, and customers use every day: web apps, APIs, internal products, and the plumbing underneath.

This is a builder seat for engineers with roughly 3–7 years of experience. Ideal backgrounds are SaaS product engineering and startup/scale-up environments where you scoped ruthlessly, shipped v1s, and owned features in production. You'll turn fuzzy requirements into reliable, usable software.

You'll land on a focus area based on strength and business need (see Nice to haves). Breadth, product sense, and ownership matter more than matching every bullet.

• Ship end to end. Take features from design through implementation, testing, and production — frontend, backend, and the integrations between them.

• Build tools people use. Internal products, dashboards, and workflow apps for labeling, capture, delivery, QA, and ops — interfaces that are fast and clear for non-technical users.

• Own services and APIs. Backend services with correct permissions, retries, data integrity, observability, and background workflows for data-heavy operations.

• Work close to the data. Help package, validate, index, and serve large video/sensor datasets so labeling, QA, research, and customer delivery stay reliable.

• Automate the manual. Replace spreadsheet-heavy or one-off scripts with durable tools and processes alongside the teams who live in those workflows.

• Iterate with users. Get a v1 in front of operators and stakeholders quickly; harden what proves out.

• 3–7 years building and shipping production software (or equivalent impact), ideally in SaaS and startup / scale-up environments.

• Comfortable owning a feature across the stack — modern frontend (e.g. TypeScript/React) and at least one backend language (e.g. Python, Node, Go).

• Strong product sense and a bias to ship: scope ruthlessly, put work in users' hands, iterate.

• Design and build high-throughput services upload/ingest huge amounts of data, processing, indexing, retrieval, and access control.

• Practical experience with databases (e.g. SQL/PostgreSQL, MongoDB) and production APIs (REST/GraphQL).

• Ownership of systems in production: monitoring, debugging, and root-cause fixes — not just greenfield demos.

• Clear communication with non-technical partners (ops, product, finance, delivery).

Lean into one or more of these — they map to how the current openings differ. Strong fit in any one area is enough; you don't need all of them.

• Internal products / 0→1 delivery tooling: dataset review & QA UIs, delivery-tracking dashboards, export/validation workflows for ego-data deliveries to AI labs.

• Full-stack ops applications: labeling, capture, and country/ops management apps; clean UX for international, non-technical teams.

• Web & media / SDK: viewers and players for egocentric capture (multi-stream playback, timeline sync, large-media performance); API/SDK design for hardware and data consumers.

• Data platform backend: high-throughput ingest, processing, indexing, and serving of large video/sensor datasets; reliability, cost, and observability at scale.

• Payments & revenue systems:payouts, payment orchestration, reconciliation, invoicing, and finance tooling where correctness and auditability matter.

• AI as a force multiplier: daily use of AI coding tools; practical LLM integrations in internal tooling (validation, search, workflow triggers).

• Early/founding engineer experience, video/media systems, cloud (AWS/GCP/Azure) + CI/CD, or fintech/transactional backends.

• Real surface area from day one on problems that are still greenfield.

• Direct impact across delivery, operations, research, and the frontier labs that depend on our data.

• Hybrid work with teams in Toronto, New York, and San Francisco to build the infrastructure for physical AI as it moves from lab demos into the world.

If you don't check every box above but believe you can do the job, we encourage you to apply — we're looking for capability and trajectory, not a perfect checklist match.

We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, we will partner with you to meet your needs.

We may use AI-assisted tools to help evaluate applications against the requirements of this role. These tools support our recruiting team in identifying qualified candidates but do not replace human judgment or limit which applications may be reviewed or considered.