Perception Engineer, Plant Edge
Own the multi-camera pipeline for Visqa: DeepStream, Holoscan, TensorRT, camera synchronisation and sub-100 ms defect classification on Jetson-class hardware.
Careers
Work on software that decides whether a tablet is made right.
Pildeon is pre-launch and small. These are the roles the first design partnerships need. Every role involves time in a tablet plant, gowned, next to a press. If that is unappealing, this is not the right company.
Scenario: Run one immediate-release tablet batch through granulation, compression, coating and inspection inside the approved design space, with QA approval before release. A design walkthrough, not a customer record.
Roles
Where we need people
Roles, not open-headcount claims. Each maps to a product or a layer of the platform.
Spectral and Chemometrics Engineer
Own Spectryn's NIR and PAT models: formulation-specific calibration, confidence bounds, drift detection, RAPIDS and cuML pipelines, and the method-linked evidence chain.
Control and Validation Engineer
Own the policy engine and the bounded write path into presses, coaters and granulators, and the GAMP 5 validation package with design partners.
Simulation Engineer, Batch Twin
Own Twindra: GPU DEM granulation, CFD coating, compression and ejection risk, dissolution surrogates in Omniverse, Warp and Modulus, fitted against real batches.
Robotics Engineer, Handling
Own Kinetos: sim-first IBC and tooling handling policies in Isaac, containment-aware behaviours, genealogy verification at every move.
Applied LLM Engineer, Conformance
Own Conforma: retrieval over cGMP, GAMP 5, Part 11, ICH, USP and SOPs with enforced citations, NeMo and Guardrails, deviation drafting under change control.
OSD Process Scientist
A formulator or process engineer who wants to encode what they know: design spaces, formulation families, the twin's physics and the translation between the plant and the platform.
Deployment Engineer, Plants
Take a site from edge node to bounded autonomy: connectors, tag mapping, shadow mode, review-console training and the baseline reconciliation with QA.
Design-Partner Lead
Own the first three to five design partnerships end to end, from the line review to the written success metric.
How we work
Six things about working here
Said plainly, so you can self-select.
Plants, not slides
Decisions are made next to the press with the people who run it.
Targets are called targets
We do not claim results we have not measured, internally or externally.
QA is a colleague
Validation is a gating stakeholder and we would rather meet them first.
Bounded by design
We build systems that cannot do the thing that would be unacceptable, rather than systems told not to.
Small and senior
Each person owns a product or a layer. Ownership is the job.
Write it down
Design records, model cards, change control, run records. If it is not written, it did not happen.
Run timeline · scenario
What you would be building
The run engine behind this record, and the agents, models, connectors and audit trail that produce it.
run_b2c7e1
Line 2 · rotary tablet press · scenario
SUCCEEDED
-
01
ingest.batch_record
SUCCEEDED
1.2 s
Plant Orchestrator · Pulled the master batch record, the approved design-space limits for this formulation and tooling family, and the site autonomy policy from MES; locked the target envelope for the run.
-
02
twin.rehearse
SUCCEEDED
41 s
Plant Orchestrator · Twindra simulated 36 candidate setpoint vectors on the as-made batch twin (granulation DEM, compression and ejection risk, coating CFD, dissolution surrogate) and ranked them on predicted content uniformity, capping risk and takt.
-
03
blend.uniformity
SUCCEEDED
6 m 10 s
Blend-and-Dispense · Spectryn scored in-line NIR spectra against the formulation's chemometric model; the blend reached its uniformity end point with confidence bounds inside the site acceptance limit and blending stopped on evidence rather than on a fixed time.
-
04
granulate.dry
SUCCEEDED
3 m 48 s
Granulate-and-Dry · Inlet air temperature and drying airflow trimmed inside the approved window to bring loss-on-drying to the target moisture band for compression; the move was bounded, logged and inside the design space.
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05
compress.press
SUCCEEDED
2 m 02 s
Compress-and-Fill · Main compression force and feed-frame speed adjusted inside the approved window to hold tablet weight and hardness on target while the twin's capping-risk estimate stayed under the site threshold.
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06
coat.spray
SUCCEEDED
4 m 25 s
Coat-and-Dry · Spray rate and inlet temperature re-phased against bed temperature and exhaust humidity to hold the film-coat window and avoid twinning and mottling; no excursion from the validated recipe envelope.
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07
inspect.tablets
SUCCEEDED
continuous
Defect-and-Inspect · Visqa classified chips, cracks, capping, spots, mottling and print defects on six synchronised cameras, within the sub-100 ms design budget, and linked a rising chip rate to coating spray rather than to tooling.
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08
spectra.release_evidence
SUCCEEDED
38 s
Quality-and-Potency · Spectryn assembled the method-linked evidence chain for content uniformity and moisture with confidence bounds and citations to the validated analytical method, ready for QA review.
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09
approve.qa
APPROVAL
3 m 14 s
Plant Orchestrator · Batch release is above every autonomy level Pildeon offers. The package was held for the named QA reviewer, who approved it in the review console; the decision, the reviewer identity and the timestamp are in the Part 11 audit trail.
-
10
release.genealogy
SUCCEEDED
52 s
Quality-and-Conformance · Conforma wrote the time-aligned batch genealogy (every sensor state, recommendation, approved action, equipment response and QC result) and the cited release package to the immutable audit trail.
Process
How hiring runs
Four conversations, one of them technical and one of them with a plant in mind.
- Intro. What you want to own and why OSD.
- Technical. A working session on a real problem from the platform or a product.
- Plant. How you would run a shadow-mode deployment, with QA in the room.
- Offer. Scope, equity, compensation and start date, in writing.
What we can say now
What we offer
Honest, because the company is early.
A product or a layer that is yours
Not a ticket queue. The thing you build is the thing a plant runs.
Founding-team equity
Early hires are compensated as early hires. Terms are in the offer, in writing.
Pre-launch
Headquarters, benefits structure and funding status are not published yet and will be stated when they exist. Ask; we will tell you where things stand.
Accelerated computing
Physical AI, at the press
Tablets leave a rotary press by the hundreds of thousands an hour. Perception has to be local, deterministic and fast, so Pildeon runs GPU inference on the plant edge and keeps training, simulation and optimisation on DGX, HGX or OVX, on-prem where the data cannot leave the plant.
Edge perception
Multi-camera chip, crack, spot and print inspection and NIR and PAT fusion on the plant edge. Design targets: under 100 ms per camera frame group, under 500 ms for a fused risk score.
Validated model serving
Vision, spectral, time-series and process-reasoning models behind one orchestrator, versioned, tenant-isolated, with bounded actions and audit schemas enforced at the serving layer.
Training and fine-tuning
Defect vision, coating appearance, NIR and PAT chemometrics, press and coater time-series models and process-window models, trained under GxP change control.
As-made batch twin
GPU-accelerated DEM granulation, CFD coating spray and drying, compression and ejection risk and a dissolution surrogate. Design target: nightly design-space sweeps per formulation family.
Synthetic rare faults
Capping, lamination, sticking, mottling, segregation and print-defect scenarios generated for class balance, always labelled synthetic and evaluated apart from real GMP evidence.
Optimisation and handling
Campaign and changeover sequencing, drying and coating energy allocation and line balancing with cuOpt; sim-first IBC and tooling handling policies trained and rehearsed with Isaac.
Autonomy
Four levels, set per agent and per setpoint
A tablet plant does not go from fixed recipes to autonomy in one step, and validated parameters never change without sign-off. Pildeon makes the level explicit, auditable and reversible at any time.
| Level | What the agent does | What the human does | Typical time to reach |
|---|---|---|---|
| L0 · Shadow | Observes, scores and predicts; writes nothing | Runs the line as today; compares predictions to QC results | Weeks 1 to 6 of a design partnership |
| L1 · Assist | Recommends setpoint moves and explains why | Process engineer enters the move; QA reviews the log | After shadow-mode accuracy is shown |
| L2 · Supervised | Proposes a bounded move; it executes on approval | Approves each move in the review console | After assist-mode ROI is shown |
| L3 · Bounded | Moves low-risk compression, coating and inspection setpoints inside the approved design space | Handles exceptions; approves every release | After validation and change-control review |
There is no unattended level. Batch release and any move outside the approved design space always wait for a named QA reviewer, and every model or prompt change runs under GxP change control.
The first meeting
What each stakeholder asks first
Pildeon has no customer quotes to show yet, and will not invent any. These are the questions the three buyers in an OSD plant ask in the first meeting, and the answer each gets.
"What does this do to my reject rate, my deviation count and my release time, and how will you prove it on my line rather than on a slide?"
Site or manufacturing director · economic buyerAnswer: shadow mode first, baseline captured before any autonomous action, a written ROI target before a paid pilot.
"Will it ever move a setpoint outside the design space we validated, and can I see why it recommended every move it made?"
Formulation or process engineer (MSAT) · championAnswer: no, by construction. Every recommendation carries its reasoning trace and its citation.
"Where is the Part 11 audit trail, who signs the release, and what happens to my validated state when the model changes?"
QA, quality and compliance lead · approverAnswer: an immutable audit spine, a named QA reviewer on every release, and every model change under GxP change control.
Guardrails
An agent that can move a compression force needs a leash
Pildeon writes to production equipment in a GMP plant. Every capability is scoped to the approved design space, every write is policy-checked, every action is written to an immutable Part 11 audit trail the site owns, and no agent can release a batch.
- Bounded action space. Each agent can only write to an explicit setpoint allow-list, inside the limits the site's validation approved.
- Policy engine before every write. Autonomy level, design-space limits, batch state, model version and reviewer assignment are all evaluated before a setpoint moves.
- Human-in-the-loop gates. Batch release, any move outside the design space and any change to a validated parameter wait for a named QA reviewer.
- Immutable audit trail. Append-only, hash-chained, exportable, retained on the site's own storage, with reviewer identity and timestamp on every decision.
- Hard fallback. Loss of the edge node, the network or the model returns the line to the equipment's last known-good recipe.
- Tenant and IP isolation. Formulations, recipes and models never cross a customer boundary. Validated on-prem deployment is available for regulated producers.
Compliance posture
| Standard | Scope | Status |
|---|---|---|
| 21 CFR Part 11 | Audit trail, e-signatures, records | RUNNING Designed in from day one |
| GAMP 5 | Validation package for the platform | RUNNING In progress, planned for the first design partners |
| cGMP · ICH Q8 to Q13 | Design space, QbD, continuous manufacturing | RUNNING Design-aligned; site validation decides |
| SOC 2 Type I | Cloud control plane | QUEUED Planned, first six months |
| SOC 2 Type II | Cloud control plane | QUEUED Planned, months six to twelve |
| Data residency | Validated on-prem, air-gapped option | SUCCEEDED Available by design |
FAQ
Straight answers
The questions site directors, MSAT engineers and QA leads actually ask in the first meeting.
Yes, but only inside an explicit setpoint allow-list bounded by the design space your validation approved, and only at the autonomy level your site has set. Level 0 is shadow mode: Pildeon observes and predicts and writes nothing. Most sites spend their first weeks there.
No. The approved design space is a wall the policy engine enforces at call time. A move outside it, or a change to a validated parameter, is a change-control event that waits for QA sign-off. No autonomy level removes that gate.
A named QA reviewer, every time. Pildeon assembles the evidence chain and the cited release package; the reviewer approves or rejects it in the review console, and the decision, identity and timestamp land in the Part 11 audit trail.
The line returns to the equipment's own last known-good recipe. Pildeon is a supervisory layer on top of your existing control systems, never a replacement, so an outage degrades the line to the way it runs today, not to a stop.
Only if you choose cloud training. Formulations, recipes, spectra and defect libraries are tenant-isolated and never used to train another customer's models. Validated on-prem deployment with an air-gapped plant edge is available.
Shadow mode on one wedge workflow first, with baseline rejects, deviations, yield, energy and release time captured before any autonomous action. Assist mode once prediction accuracy is shown against QC results. A paid validated pilot only when there is a written ROI target. Timelines depend on the site's validation and change-control calendar.
Get started
Tell us what you want to own
Send a note with the role, or the product, and what you have built that is closest to it.