Coding evaluators
Review generated code for correctness, maintainability, security, and fit with real repository constraints.
Code review · preference ranking · rationale writing
Technical talent for AI training
Hire engineers who can teach coding models what good looks like.
Particular Systems sources and technically screens software engineers for coding evaluation, agent training, and model-quality work. You get a small evidence-backed shortlist, not a resume dump.
A new, founder-led practice from Particular Systems.
Role calibrationScreened against your actual work
Candidate packetTechnical evidence, fit, and open questions
Decision stepOptional paid work sample before commitment
The hiring gap
A resume shows experience. It does not show evaluation judgment.
AI training work asks engineers to do more than write code. They must inspect unfamiliar systems, follow a changing rubric, explain tradeoffs, and make consistent calls on imperfect model output.
- ReadUnderstand production code beyond a toy exercise.
- JudgeSeparate a plausible answer from a robust one.
- ExplainWrite rationales another reviewer can audit.
- CalibrateApply the same bar across a changing task set.
The candidate packet
Review the signal before the interview.
Every shortlist explains why a candidate is worth your time. The packet separates verified evidence, role fit, assessment notes, and what still needs to be tested.
The format shown is illustrative. Each screen is built around the client's role, stack, and rubric.
Role under review
Coding evaluator
VerdictAdvance to paid sample
Evidence reviewed
- Production backend ownership
- Repository-level code review
- Written technical rationale
Review focusCorrectness under failure
strong: identifies retry race
traces impact across service boundary
validate: rubric consistency at volumeInitial role focus
Engineers for the human layer of model quality.
The practice starts with software work where technical judgment matters more than raw annotation volume.
AI training engineers
Author and evaluate high-quality technical examples for supervised fine-tuning and post-training workflows.
SFT authoring · rubric use · output evaluationAgent task designers
Design repository-level tasks that test tool use, debugging, implementation, and multi-step engineering work.
Task design · reference solutions · failure analysisTechnical QA leads
Calibrate reviewers, resolve edge cases, track error patterns, and keep a technical bar consistent across a team.
Calibration · audit · reviewer feedbackThe method
From role brief to evidence-backed decision.
No black-box match score. Each step produces something your hiring team can inspect.
- 01Output: screening brief
Calibrate the role
Define the work, stack, seniority, evaluation bar, location, and engagement constraints.
- 02Output: reviewed longlist
Source from evidence
Use targeted outreach, technical communities, repository work, referrals, and role-specific search.
- 03Output: candidate packets
Run the technical screen
Review relevant work, probe engineering judgment, and document strengths, risks, and open questions.
- 04Output: hiring decision
Test before scale
Coordinate interviews or a paid sample, gather the evidence, and refine the bar before adding headcount.
Small on purpose
The person selling the search stays responsible for the signal.
Particular Systems builds applied AI products and technical workflows. Bhupendra Shekhawat leads this hiring practice and stays involved in role calibration, sourcing review, and the final shortlist. There is no account-management handoff.
Start at the right scale
One critical hire or a small evaluation team.
A narrow shortlist for a defined technical role.
Best when the role is clear and you want direct help calibrating, sourcing, and technically reviewing candidates.
- Role and signal definition
- Targeted candidate search
- Evidence-backed shortlist
- Interview or trial coordination
A small group for time-bounded training or evaluation work.
Best when you need several engineers against the same rubric and want to validate quality before expanding.
- Shared screening rubric
- Individual technical packets
- Paid pilot support
- Calibration before expansion
Questions buyers ask first
Clear before the search begins.
Is this a talent marketplace?
No. There is no self-serve database and no volume of profiles to sort through. We run a focused search against an agreed role and return a small technically reviewed shortlist.
Do you already have a giant bench of engineers?
No, and we will not pretend otherwise. This is a new, selective practice. We combine targeted sourcing with role-specific review instead of selling access to an unverified pool.
How do you screen technical judgment?
The screen follows the role. It may include work-history evidence, repository or code review, a structured technical conversation, rubric calibration, and an optional paid work sample. The packet records both positive signal and unresolved risk.
Can you support contract and permanent hiring?
Yes. A search can lead to a contract trial, a project team, or a permanent hire. We agree the employment model, geography, availability, and commercial terms before sourcing begins.
Where do you source?
The search can be global or geography-specific. Channels can include targeted outreach, technical communities, public engineering work, referrals, and relevant professional networks. We do not represent public activity as proof of availability.
What does a first engagement look like?
We begin with a short role-calibration call or written brief. You then receive a proposed search scope, screening method, expected outputs, commercial terms, and a realistic sourcing window before deciding whether to proceed.
Start with the work
Give us the role, the work, and the bar.
A useful first note explains what the engineer will evaluate or produce, what makes the work hard, and how you will know they are good.
hello@particularsystems.com