Robotics AI Engineer, Tactile Perception & Representation

We’re a stealth robotics startup in Palo Alto hiring an engineer to define and ship a canonical Tactile Tensor and the reference SDK + conformance suite that makes tactile data reproducible, interoperable, and directly usable for robotics perception and foundation-model training. Critical requirement: deterministic, byte-stable serialization + strict versioning, plus tokenization-ready interfaces (tensors → stable token streams) for Transformer-style robotics pipelines—without heavy dependencies. What you’ll do - Define the Tactile Tensor: units, coordinate frames, timestamps, shapes, uncertainty, required metadata, and forward/backward compatibility rules. - Build a lightweight reference SDK (Python and/or C++) that validates, serializes/deserializes, and produces identical outputs across platforms. - Specify training-grade data contracts: deterministic windowing/patching, normalization/quantization, and token schemas that are stable across sensors and logging setups. - Ship a public-facing spec + examples + CI conformance tests so external robotics labs/OEMs can implement against it with confidence. - Architect the tensor representation to ensure physical invariances (e.g., coordinate-frame independence, scale-invariant contact patches) so that policies trained on one robot's geometry generalize to another. Requirements - PhD in a relevant field (Robotics, Computer Science, Applied Mathematics, Electrical Engineering, or similar), or 3+ years of equivalent industry experience. - Excellent software engineering fundamentals (API design, packaging, CI, testing, docs). - Python and/or C++ proficiency (both ideal). - Proven ability to design deterministic serialization and conformance tests (identical inputs → identical bytes across platforms). - Experience with high-rate numeric data formats (Arrow/Parquet/Zarr/Protobuf/FlatBuffers or similar). - Ability to design metadata + lineage for robotics datasets (device ID, calibration artifact ID, robot/config versions, provenance). - Familiarity with ML data pipelines; ability to define tokenization/embedding conventions for transformer training without bundling full ML stacks. - Experience designing data schemas that explicitly handle and flag physical sensor artifacts (saturation, dropout, thermal drift, and variable sampling rates) without crashing downstream model inference. Preferred - Experience authoring standards/specs, file formats, or widely-used SDKs. - HPC/embedded/performance background; strong “minimal dependency” philosophy. - Experience with data integrity/attestation (hashing/signing, provenance chains) for tamper-evident robotics logs. Key Deliverables - PDF Spec: Tactile Tensor schema, metadata/lineage rules, determinism + versioning/migration, conformance criteria. - Reference SDK: lightweight schema objects, validators, deterministic serializer/deserializer, minimal dependencies. - Dataset Container Spec: reproducible storage + examples (streaming + offline parity; robotics log friendly). - ML Interfaces: modular tokenization hooks + reference tokenization recipes (windowing/patching + quantization conventions). - CI Suite: golden files, byte-stability, backward/forward compatibility tests, reference implementations. Contract-to-hire with a clear path to full-time and founding equity for the right fit. Remote: Ja Quelle: https://ch.linkedin.com/jobs/view/robotics-ai-engineer-tactile-perception-representation-at-haptovia-robotics-4346841309

Level: mid level