Instacart Machine Learning Engineer Interview Guide

Most candidates prep for this role like it's a generic ML engineering loop. From hundreds of mock interviews, the pattern we see is people over-indexing on logistics and delivery ETA problems while underestimating how much the interview (and the day job) centers on ads ranking and search relevance. The specialization listed on the req is "Ads Quality," but the actual work bleeds into search, fulfillment ETA, and sponsored product placement all at once.

Instacart Machine Learning Engineer Role

Your models power the system that decides which sponsored products appear in search results, at what price, and in what order, while simultaneously serving organic ranking and delivery ETA predictions from the same platform. The shadow-mode rollout process is a good window into what "ownership" means here: you configure the A/B experiment framework, write the logging, monitor latency and error rates on live traffic, and debug the Spark-based validation steps when they break in CI. Success after year one looks like shipping a model change that moved a measurable business metric (CTR, conversion, revenue per impression) through a production pipeline you built or improved yourself.

A Typical Week

The surprise isn't that you spend time on infrastructure. It's that feature store migrations, shadow-mode deployment configs, and experiment launch docs eat into the same days as model training, sometimes in the same afternoon. Friday knowledge-sharing sessions cover papers on multi-objective ranking that directly shape the next sprint's ads-versus-organic tradeoff work, so they function more like design input than optional reading.

Projects & Impact Areas

Ads quality and search relevance are deeply entangled at Instacart. The Wednesday cross-functional sync in the schedule above exists because product wants to know if a single ranking model can improve both organic results and sponsored product placement, which means you're reasoning about advertiser bid prices and user relevance signals in the same feature set. Fulfillment and delivery ETA prediction run alongside this work (Thursday's design review on graph neural networks for store-shopper-delivery zone estimation is a real example), and some MLE roles now touch GenAI-powered features as Instacart explores LLM integrations.

Skills & What's Expected

The underrated skill is writing production-quality Python services, not just prototyping in notebooks. Instacart scores software engineering as high as ML expertise, and the coding rounds punish candidates who can't structure clean, testable code under time pressure. Business acumen is the other differentiator: interviewers push you to connect model improvements to ads auction mechanics and marketplace economics, not just report offline NDCG gains. A PhD and publication record do carry weight (the role description explicitly prefers them), but they won't save you if your code isn't production-grade.

Levels & Career Growth

The jump between levels hinges on scope of influence. At the IC level, you own individual model features and ship them through the full pipeline. Moving up requires cross-team impact, like designing the experiment framework other engineers depend on or setting technical direction for a model family. The most common blocker, from what candidates and hiring managers report, is staying in the modeling comfort zone without picking up the infrastructure and cross-functional leadership work that higher levels demand.

Work Culture

Instacart's work policy has been in flux. The company advertises "Flex First" (remote from US or Canada), but internal culture notes point to a hybrid expectation of three days per week in the San Francisco office, Tuesday through Thursday. Clarify the current policy with your recruiter before assuming fully remote.

Post-IPO (CART, August 2023), the priority shift toward profitability and ads monetization is tangible. Projects that don't tie to revenue or retention face harder scrutiny, which is worth knowing before you join expecting pure research freedom.

Instacart Machine Learning Engineer Compensation

RSUs vest over four years with a one-year cliff, so your first twelve months deliver zero equity. Both base salary and RSU grants are negotiable, which means you should treat the total comp package as one conversation rather than fixating on either component alone.

The strongest move you can make is to bring a competing offer. Instacart benchmarks aggressively and has room to adjust when you can show a credible alternative. Come prepared to articulate your market value with specifics, not vibes, and ask your recruiter upfront whether any location-based adjustments apply to your particular offer before you start the back-and-forth.

Instacart Machine Learning Engineer Interview Process

The most common rejection pattern spans multiple gaps, not just one. Candidates who flame out tend to show weak ML fundamentals and poor product sense simultaneously. You can survive a shaky coding round if your system design is sharp, but struggling to explain why you'd pick one evaluation metric over another while also failing to connect your model choices to grocery delivery or ads monetization outcomes is a combination that sinks most borderline cases.

The Hiring Manager Screen deserves more prep than you'd expect. It covers behavioral, ML depth, and product sense in 45 minutes, which means the HM is forming a technical opinion about you before the onsite even starts. Come ready to walk through a past project with specifics: what metric you optimized, what tradeoff you accepted, and what broke in production.

Instacart Machine Learning Engineer Interview Questions

from __future__ import annotations

from typing import Iterable, List, Tuple, Dict


def dedupe_and_rank(
    candidates: Iterable[Tuple[str, float]]
) -> List[Tuple[str, float]]:
    """Deduplicate (ad_id, pctr) candidates by keeping max pCTR per ad_id.

    Sort by pCTR descending, then ad_id ascending.

    Args:
        candidates: Iterable of (ad_id, predicted_pCTR).

    Returns:
        List of (ad_id, max_predicted_pCTR) sorted as specified.
    """
    best: Dict[str, float] = {}
    for ad_id, pctr in candidates:
        # Keep the maximum pCTR for each ad_id.
        prev = best.get(ad_id)
        if prev is None or pctr > prev:
            best[ad_id] = pctr

    # Sort by (-pctr, ad_id).
    ranked = sorted(best.items(), key=lambda x: (-x[1], x[0]))
    return ranked


if __name__ == "__main__":
    sample = [("ad7", 0.12), ("ad2", 0.40), ("ad7", 0.30), ("ad1", 0.40)]
    print(dedupe_and_rank(sample))
    # Expected: [('ad1', 0.4), ('ad2', 0.4), ('ad7', 0.3)]

from __future__ import annotations

import math
from typing import Iterable, List, Tuple, Dict, Any


def ndcg_at_k(
    rows: Iterable[Dict[str, Any]],
    k: int = 10,
    query_keys: Tuple[str, str] = ("user_id", "search_session_id"),
    score_key: str = "score",
    label_key: str = "label",
) -> float:
    """Compute mean NDCG@k across queries.

    Args:
        rows: Iterable of dicts with at least query_keys, score_key, label_key.
        k: Cutoff.
        query_keys: Keys that define a query, default (user_id, search_session_id).
        score_key: Model score key.
        label_key: Relevance grade in {0,1,2,3}.

    Returns:
        Mean NDCG@k across queries. Queries with no gain return 0 contribution.

    Notes:
        - Stable sort ensures deterministic behavior under score ties.
        - Handles queries with fewer than k candidates.
    """
    if k <= 0:
        raise ValueError("k must be positive")

    # Group candidates by query.
    groups: Dict[Tuple[Any, ...], List[Tuple[float, int]]] = {}
    for r in rows:
        qid = tuple(r[q] for q in query_keys)
        score = float(r[score_key])
        label = int(r[label_key])
        groups.setdefault(qid, []).append((score, label))

    def dcg(labels_sorted: List[int]) -> float:
        total = 0.0
        for i, rel in enumerate(labels_sorted[:k]):
            # gain = 2^rel - 1, discount = log2(i+2)
            gain = (2 ** rel) - 1
            discount = math.log2(i + 2)
            total += gain / discount
        return total

    ndcgs: List[float] = []
    for _, cand in groups.items():
        # Predicted ranking: sort by score desc, stable for ties.
        cand_sorted = sorted(cand, key=lambda x: x[0], reverse=True)
        pred_labels = [lab for _, lab in cand_sorted]

        # Ideal ranking: sort by label desc.
        ideal_sorted = sorted(cand, key=lambda x: x[1], reverse=True)
        ideal_labels = [lab for _, lab in ideal_sorted]

        dcg_val = dcg(pred_labels)
        idcg_val = dcg(ideal_labels)
        ndcg = 0.0 if idcg_val == 0.0 else (dcg_val / idcg_val)
        ndcgs.append(ndcg)

    return 0.0 if not ndcgs else sum(ndcgs) / len(ndcgs)


if __name__ == "__main__":
    # Tiny sanity check.
    data = [
        {"user_id": 1, "search_session_id": "s1", "score": 0.9, "label": 3},
        {"user_id": 1, "search_session_id": "s1", "score": 0.8, "label": 0},
        {"user_id": 1, "search_session_id": "s1", "score": 0.7, "label": 2},
        {"user_id": 2, "search_session_id": "s2", "score": 0.1, "label": 0},
        {"user_id": 2, "search_session_id": "s2", "score": 0.2, "label": 0},
    ]
    print("mean ndcg@2:", ndcg_at_k(data, k=2))

from __future__ import annotations

import numpy as np
from typing import Tuple


def _sigmoid_stable(z: np.ndarray) -> np.ndarray:
    """Numerically stable sigmoid."""
    z = np.asarray(z, dtype=np.float64)
    out = np.empty_like(z)
    pos = z >= 0
    neg = ~pos
    out[pos] = 1.0 / (1.0 + np.exp(-z[pos]))
    ez = np.exp(z[neg])
    out[neg] = ez / (1.0 + ez)
    return out


def weighted_logloss_and_grad(
    logits: np.ndarray,
    labels: np.ndarray,
    weights: np.ndarray,
    eps: float = 1e-15,
) -> Tuple[float, np.ndarray]:
    """Compute weighted binary log loss and gradient w.r.t. logits.

    Args:
        logits: Shape (n,).
        labels: Binary {0,1}, shape (n,).
        weights: Nonnegative weights, shape (n,).
        eps: Clipping for log stability.

    Returns:
        (loss, grad_logits) where loss is averaged by sum(weights).
    """
    z = np.asarray(logits, dtype=np.float64)
    y = np.asarray(labels, dtype=np.float64)
    w = np.asarray(weights, dtype=np.float64)

    if z.shape != y.shape or z.shape != w.shape:
        raise ValueError("logits, labels, weights must have the same shape")
    if np.any((y != 0) & (y != 1)):
        raise ValueError("labels must be binary 0/1")
    if np.any(w < 0):
        raise ValueError("weights must be nonnegative")

    p = _sigmoid_stable(z)
    p = np.clip(p, eps, 1.0 - eps)

    per_ex_loss = -w * (y * np.log(p) + (1.0 - y) * np.log(1.0 - p))

    w_sum = float(np.sum(w))
    if w_sum == 0.0:
        return 0.0, np.zeros_like(z)

    loss = float(np.sum(per_ex_loss) / w_sum)

    # d/dz of logloss is (p - y), apply weight and same normalization.
    grad = (w * (p - y)) / w_sum
    return loss, grad


if __name__ == "__main__":
    logits = np.array([0.0, 2.0, -2.0])
    labels = np.array([0, 1, 0])
    weights = np.array([1.0, 5.0, 1.0])
    loss, grad = weighted_logloss_and_grad(logits, labels, weights)
    print("loss:", loss)
    print("grad:", grad)

WITH per_impression AS (
  SELECT
    i.store_id,
    DATE(i.occurred_at) AS event_date,
    i.impression_id,
    CASE
      WHEN EXISTS (
        SELECT 1
        FROM ad_clicks c
        WHERE c.impression_id = i.impression_id
          AND c.occurred_at >= i.occurred_at
          AND c.occurred_at < i.occurred_at + INTERVAL '24 hours'
      ) THEN 1
      ELSE 0
    END AS has_click_24h
  FROM ad_impressions i
  -- Optional: add date filter for performance in real pipelines
  -- WHERE i.occurred_at >= CURRENT_DATE - INTERVAL '14 days'
)
SELECT
  store_id,
  event_date,
  COUNT(*) AS impressions,
  SUM(has_click_24h) AS clicked_impressions,
  1.0 * SUM(has_click_24h) / NULLIF(COUNT(*), 0) AS ctr
FROM per_impression
GROUP BY 1, 2
ORDER BY 2, 1;

Two areas compound in ways that catch people off guard: the ML & Ads Ranking questions assume you already think in terms of bid-price-times-relevance scoring specific to Instacart's Sponsored Products auction, and the System Design questions then ask you to operationalize that thinking against real constraints like inventory that vanishes mid-session across 1,400+ retail partners. The prep mistake most candidates make, from what we've seen, is studying generic recommendation systems instead of ads auction dynamics, where you need to reason about cannibalization between organic grocery results and sponsored placements that share the same search page.

Practice with Instacart-specific questions and full solutions at datainterview.com/questions.

How to Prepare for Instacart Machine Learning Engineer Interviews

Instacart pulled in $3.74 billion in revenue with 10.8% year-over-year growth, and the company's strategic bets tell you exactly what ML engineers will spend their time on. Ads, enterprise retailer tools (Instacart Platform), and AI-powered features like Ask Instacart are where investment is flowing. Depending on which team you join, you could be training ranking models for sponsored product placements, building search relevance systems across regional catalogs, or working on health and nutrition initiatives that tie grocery data to public health outcomes.

Most candidates blow their "why Instacart" answer by talking about loving grocery delivery or the convenience of the app. Interviewers have heard that a thousand times. What actually lands: show you understand the specific ML constraints of the domain you're interviewing for, whether that's real-time inventory volatility in ads auctions, cold-start problems for new products in search, or economics-driven modeling for pricing. Referencing their bespoke compensation philosophy or a specific engineering blog post signals you've gone deeper than the careers page.

Try a Real Interview Question

from typing import List, Sequence, Tuple


def calibrate_isotonic(p: Sequence[float], y: Sequence[int], q: Sequence[float]) -> List[float]:
    """Fit isotonic regression calibration on (p, y) and apply to query scores q.

    Args:
        p: Predicted probabilities, length n.
        y: Binary labels (0/1), length n.
        q: Query probabilities to calibrate.

    Returns:
        Calibrated probabilities for each value in q.
    """
    pass

from typing import List, Sequence, Tuple


def calibrate_isotonic(p: Sequence[float], y: Sequence[int], q: Sequence[float]) -> List[float]:
    """Fit isotonic regression calibration on (p, y) and apply to query scores q.

    Uses the Pool Adjacent Violators Algorithm (PAVA) on observations sorted by p.
    The fitted function is piecewise constant and non-decreasing. For prediction, it
    uses right-continuous buckets: for a query score x, return the value for the last
    bucket whose right endpoint is >= x; equivalently, find the first bucket with
    right endpoint >= x.
    """

    if len(p) != len(y):
        raise ValueError("p and y must have the same length")
    n = len(p)
    if n == 0:
        return [0.0 for _ in q]

    # Sort by predicted probability.
    pairs = sorted(zip(p, y), key=lambda t: t[0])
    ps = [float(t[0]) for t in pairs]
    ys = [int(t[1]) for t in pairs]

    for i, pi in enumerate(ps):
        if pi < 0.0 or pi > 1.0:
            raise ValueError(f"p[{i}] must be in [0,1]")
    for i, yi in enumerate(ys):
        if yi not in (0, 1):
            raise ValueError(f"y[{i}] must be 0 or 1")

    # Compress identical p values into initial blocks.
    blocks: List[Tuple[float, float, int]] = []
    # Each block: (left_p, right_p, count, sum_y)
    i = 0
    while i < n:
        j = i
        sum_y = 0
        while j < n and ps[j] == ps[i]:
            sum_y += ys[j]
            j += 1
        count = j - i
        blocks.append((ps[i], ps[j - 1], count, sum_y))
        i = j

    # PAVA merge to enforce non-decreasing block means.
    # We'll maintain parallel stacks for efficiency.
    lefts: List[float] = []
    rights: List[float] = []
    counts: List[int] = []
    sums: List[float] = []

    for left, right, cnt, s in blocks:
        lefts.append(left)
        rights.append(right)
        counts.append(cnt)
        sums.append(float(s))

        while len(counts) >= 2:
            m2 = sums[-1] / counts[-1]
            m1 = sums[-2] / counts[-2]
            if m1 <= m2:
                break
            # Merge last two blocks.
            new_left = lefts[-2]
            new_right = rights[-1]
            new_cnt = counts[-2] + counts[-1]
            new_sum = sums[-2] + sums[-1]

            lefts[-2] = new_left
            rights[-2] = new_right
            counts[-2] = new_cnt
            sums[-2] = new_sum

            lefts.pop()
            rights.pop()
            counts.pop()
            sums.pop()

    # Final fitted blocks and their means.
    means = [sums[k] / counts[k] for k in range(len(counts))]

    # Predict using right-continuous buckets.
    # For x <= rights[0], return means[0]. For x > rights[-1], return means[-1].
    def predict_one(x: float) -> float:
        if x <= rights[0]:
            return means[0]
        if x > rights[-1]:
            return means[-1]
        lo, hi = 0, len(rights) - 1
        while lo < hi:
            mid = (lo + hi) // 2
            if x <= rights[mid]:
                hi = mid
            else:
                lo = mid + 1
        return means[lo]

    return [float(predict_one(float(x))) for x in q]

From what candidates report, Instacart's coding rounds reward readable, production-style Python over clever one-liners. Their MLE roles span ads, search, logistics, and economics, so expect problems that test your ability to translate domain-specific math (ranking metrics, auction logic, ETA estimation) into clean implementations. Build that muscle with regular practice at datainterview.com/coding.

Test Your Readiness

This quiz covers the ads ranking, system design, and experimentation topics that show up across Instacart's MLE loop. Spot your weak areas, then drill them at datainterview.com/questions.