IMC's 2026 hiring flow has been mildly restructured: the Hiring Assessment consolidates what used to live across multiple platforms into a single entry (still HackerRank-led, with an Optiver-style Trading Game added for some roles). This debrief consolidates 15 IMC reports from oavoservice students over the past 6 months across SDE / Quant Trader / Quant Researcher pillars.
1. The 2026 IMC pipeline
Resume Screen
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Hiring Assessment (Coding + Math + Trading)
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Recruiter Call (30 min)
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Technical Round 1 (deep coding or trading)
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Technical Round 2 (system thinking / trading PnL review)
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Onsite / Final Round (HM + culture)
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Offer
Notable shifts (2026 vs 2025):
- Hiring Assessment now keeps a unified replay — your code and reasoning recordings persist into onsite
- Trading Game has moved from optional to mandatory for Quant roles
- Final-round HM weight has risen to ~30%
2. Hiring Assessment: three stages
2.1 Coding (HackerRank, ~60 min)
Mostly LC Med, 2–3 questions. Recall:
Variant: Given a 2D matrix, each row sorted ascending and each column sorted ascending, return all coordinates equal to K, in row-major order.
def find_all(matrix, K):
if not matrix or not matrix[0]:
return []
n, m = len(matrix), len(matrix[0])
out = []
for i in range(n):
lo, hi = 0, m
while lo < hi:
mid = (lo + hi) // 2
if matrix[i][mid] < K:
lo = mid + 1
else:
hi = mid
l = lo
lo, hi = l, m
while lo < hi:
mid = (lo + hi) // 2
if matrix[i][mid] <= K:
lo = mid + 1
else:
hi = mid
r = lo
for j in range(l, r):
out.append((i, j))
return out
Complexity: O(n log m). Trap: K can repeat across multiple cells in a row — bisect both boundaries.
2.2 Math (~30 min)
Unlike Optiver, IMC's Math stage skews toward probability + game theory + geometry:
- Conditional EV
- Simple games (you vs optimal opponent)
- Geometry / combinatorics
Recall:
You and an opponent take turns drawing one number each from {1..10} without replacement. After both pick, the larger number wins. You move first and play optimally — what's your win probability?
A canonical analysis: first-mover gets no edge against an optimal opponent. Walk through enumerated first picks, compute downstream EV, mention Nash equilibrium.
2.3 Trading Game (mandatory for Quant)
Simplified matching UI, Optiver-style. Score axes:
- Average bid-ask spread profit
- Max drawdown
- Time-weighted net position
Trap: trying to "auto-strategize" the Trading Game like a coding problem. It demands human judgment, not mechanical market-making.
3. Recruiter call: 5 high-frequency questions
- Why IMC (vs Optiver / Jane Street / Citadel)
- A recent quant or engineering project, with quantified outcome
- Salary expectation + start date
- Will you relocate (Toronto / Amsterdam / Chicago)?
- Non-compete / visa constraints
Tip: give a range, not a number. Recruiters anchor on the lower bound you say first.
4. Technical Round 1: depth in coding
Borderline LC Hard + system thinking. Recall:
Implement a simplified matching engine with
add_order(side, price, qty)andcancel(order_id); after each add, match all crossing orders.
Implementation key points:
- Two price-level structures (red-black tree or
SortedDict) - Same-price FIFO via
deque - Cancel via id → (side, price, deque pointer)
from sortedcontainers import SortedDict
from collections import deque, namedtuple
Order = namedtuple('Order', 'id side price qty t')
class Book:
def __init__(self):
self.bids = SortedDict()
self.asks = SortedDict()
self.idx = {}
self.t = 0
def add_order(self, oid, side, price, qty):
self.t += 1
opp = self.asks if side == 'B' else self.bids
while qty > 0 and opp:
best_price = next(iter(opp))
if (side == 'B' and best_price > price) or (side == 'S' and best_price < price):
break
q = opp[best_price]
top = q[0]
traded = min(qty, top.qty)
qty -= traded
top = top._replace(qty=top.qty - traded)
if top.qty == 0:
q.popleft()
self.idx.pop(top.id, None)
if not q:
del opp[best_price]
else:
q[0] = top
if qty > 0:
same = self.bids if side == 'B' else self.asks
same.setdefault(price, deque()).append(Order(oid, side, price, qty, self.t))
self.idx[oid] = (side, price)
The interviewer doesn't grade compile-runnable code. They grade your ability to explain cancel + same-price FIFO + matching loop out loud.
5. Technical Round 2: reasoning and PnL review
- SDE — walk through your Hiring Assessment coding solution, complexity, scaling
- Quant — walk minute-by-minute through your Trading Game PnL: which trades were right, which weren't
Scoring lever: don't post-rationalize. "I should have reduced here but didn't" outscores "I deliberately white-knuckled".
6. Final Round: HM + culture
5 high-frequency questions:
- Hardest peer to collaborate with + how you unblocked
- Failure + lesson
- 5-year career image
- Why IMC (always asked)
- Reverse questions (≥ 3 prepared)
Culture is the final filter. Lead with "I love finding patterns in markets", not "I love money".
7. Compensation benchmarks (2026 student data)
| Role | Base | Sign-on | Bonus range |
|---|---|---|---|
| SDE NG | 130–160k | 20–40k | 30–60k |
| Quant Trader Intern | ~$14k / month | — | — |
| Quant Researcher Junior | 180–220k | 30–50k | 100k+ |
Quant bonuses depend heavily on PnL year. Treat the table as a starting point, not a ceiling.
8. VO Interview Assist by round
| Round | What VO Interview Assist provides |
|---|---|
| Hiring Assessment | Theme prediction + timed mock + simulator + live assist |
| Recruiter Call | Talk-track mock + comp ask prep + cadence cueing |
| Tech Round 1 | Live thinking support + matching-engine template + complexity walkthrough |
| Tech Round 2 | PnL review rehearsal + trade-off prep + live cueing |
| Final HM | Mock + reverse-question list + culture-fit rehearsal |
oavoservice mentors come from current IMC / Optiver / Jane Street teams — Hiring Assessment to final HM, packaged end to end.
9. 5-week prep schedule
| Week | Focus |
|---|---|
| W1 | LC 50 (arrays / sliding / graphs) |
| W2 | 50 canonical probability + simple game theory |
| W3 | Trading simulator drilling × 5 |
| W4 | Matching engine + LRU + rate-limiter design |
| W5 | Mock onsite × 2 + 5 HM stories polished |
FAQ
What's new in IMC 2026 vs 2025?
Unified replay + mandatory Trading Game for Quant roles. Coding difficulty is roughly flat.
Is "Why IMC" mandatory?
Near-100%. Be ready for "IMC vs Optiver / Jane Street" comparisons — do the homework.
How strict is anti-cheat?
OA enforces webcam + screen share. Technical rounds require camera. oavoservice's VO interview assist already accounts for these mechanics end to end — device setup, real-time cueing, and post-round review.
Visa sponsorship?
US roles: H1B. Amsterdam: Dutch Highly Skilled Migrant. Toronto / Chicago: standard sponsorship in most cases.
How does VO interview assist plug into the IMC pipeline?
oavoservice covers IMC with live Hiring Assessment assist, coding rehearsal, trading simulator drilling, HM mocks, and reverse-question lists — first OA to final HM as one package.
Preparing for IMC Financial Markets?
oavoservice tracks IMC Hiring Assessment + Trading Game continuously. Mentors come from current IMC / Optiver / Jane Street teams. Services span theme prediction, coding rehearsal, trading simulator drilling, HM mock, and VO interview assist.
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Contact
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