software databcieeg ecog decodingneural signal processingreal time firmware
Complete evaluation framework
What to assess and how to score it
Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Technical proficiency
35% weight
Probe fluency in neural signal pipelines: bandpass and CAR filtering, spike sorting, LSL or BCI2000, C++ or Python decoders, latency budgets under 20 ms.
Evidence to listen for
Command of the languages, frameworks, and data tools the role actually uses
Understands correctness, performance, and failure modes, not just syntax
Has opinions on testing and can justify them
Reads and reasons about code they did not write
Five-point scoring guide
1
Poor
Cannot work independently; fundamentals are missing.
2
Needs Improvement
Weak fundamentals; output needs heavy review.
3
Satisfactory
Competent for the role; needs guidance on complex or unfamiliar work.
4
Very Good
Strong practitioner; handles hard problems with little guidance.
5
Excellent
Names specific decoders (LDA, Kalman, deep nets) with sampling rates, channel counts, and measured closed-loop latency figures from their own builds.
02
Evaluation factor
Systems and trade-offs
25% weight
Ask what they shipped: implanted or wearable systems, firmware on Blackrock or Neuropixels rigs, clinical trial software, session counts and uptime during human or primate sessions.
Evidence to listen for
Reasons about scale, latency, cost, and failure before writing code
Names the trade-off they chose and what they gave up
Understands the data lifecycle end to end
Anticipates what breaks at ten times the volume
Five-point scoring guide
1
Poor
No thinking beyond the immediate task; no awareness of scale or failure.
2
Needs Improvement
Limited architectural awareness; struggles with design decisions.
3
Satisfactory
Works within an existing design; makes sound local decisions.
4
Very Good
Designs for scale and maintainability; articulates trade-offs clearly.
5
Excellent
Points to specific deployed systems, participant sessions run, and the recalibration or drift handling they wrote to keep them working.
03
Evaluation factor
Evidence and rigour
25% weight
Test rigour on decoder validation: offline versus online performance gaps, cross-session generalisation, artefact rejection, held-out blocks, bitrate or ITR metrics rather than accuracy alone.
Evidence to listen for
Validates results rather than trusting output
Knows how their work is measured and what a bad result looks like
Can describe a time their own analysis or model was wrong and how they caught it
Careful about data quality, leakage, and silent failure
Five-point scoring guide
1
Poor
Ships unvalidated work; no notion of how correctness is checked.
2
Needs Improvement
Validates superficially; misses obvious quality or leakage issues.
3
Satisfactory
Reasonable checks in place; rigour drops under time pressure.
4
Very Good
Validates thoroughly; can name a real error they caught in their own work.
5
Excellent
Distinguishes offline gains from real closed-loop benefit, quantifies degradation across days, and cites metrics such as ITR or target acquisition time.
04
Evaluation factor
Collaboration and communication
15% weight
Look for work alongside neuroscientists, neurosurgeons, and regulatory staff: IRB documentation, IEC 62304 or FDA software submissions, and how they handled participant-facing session constraints.
Evidence to listen for
Explains technical work to non-technical stakeholders
Gives and takes code or peer review constructively
Documents enough that the work survives their absence
Aligns with team process rather than working around it
Five-point scoring guide
1
Poor
Cannot work in a team; resistant to feedback.
2
Needs Improvement
Communication issues create rework; lone-wolf tendencies.
3
Satisfactory
Adequate team member; documentation and review participation are light.
4
Very Good
Communicates well; reliable reviewer and collaborator.
5
Excellent
Describes concrete handoffs with clinical and hardware teams, plus documentation written for IRB, QMS, or verification review.
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