u/Fit_Fortune953

I’m trying to break into AI Engineer / Applied AI roles and would really appreciate advice from people who have already landed an AI Engineer role, internship, or early-career opportunity.

For context, I have been building projects around RAG, LLM evaluation, agent workflows, and cost-aware model selection, but I’m trying to understand what actually moves the needle in the market.

What helped you the most?

Was it:

projects,

open source,

referrals,

networking,

writing/content,

resume optimization,

interview prep,

or something else?

Also, what would you do differently if you were starting again today?

Any honest advice would help.

I built an open-source benchmark called RealDataAgentBench (RDAB) that evaluates LLM agents on data science work across 4 dimensions: correctness, code quality, efficiency, and statistical validity.

After 1,180+ runs across 12 models and 39 tasks, the results are worth sharing here.

The headline finding:

Llama 3.3-70B (free via Groq) scores 0.798 overall. GPT-5 scores 0.780.

Llama costs $0.002/task. GPT-5 costs $0.671/task. That's 335× cheaper for better performance on this benchmark.

On modeling tasks specifically, Llama outperforms GPT-5 outright — driven by more methodical, step-by-step code structure.

Full leaderboard (ranked models only — ≥80% task coverage required):

Rank	Model	RDAB Score	Cost/Task	Stat Validity
1	GPT-4.1	0.875	$0.033	0.747
2	GPT-4.1-mini	0.872	$0.010	0.746
3	GPT-4o	0.851	$0.053	0.751
4	Grok-3-mini	0.827	$0.004	0.704
5	Llama 3.3-70B	0.798	$0.002	0.694
6	GPT-4o-mini	0.785	$0.012	0.770
—	GPT-5 ⚠️	0.780	$0.671	0.690
7	Gemini 2.5 Flash	0.662	$0.002	0.538
8	GPT-4.1-nano	0.624	$0.010	0.685

⚠️ = partial coverage, excluded from ranking

GPT-4.1-mini is statistically tied with GPT-4.1 and beats GPT-5 at 65× lower cost ($0.010 vs $0.671).

Other findings that surprised me:

1. Claude leads on statistical validity, GPT leads on correctness — and they're largely independent

Claude Sonnet scores 0.851 on stat validity (highest of any model). GPT-4.1-mini scores 0.937 on correctness (highest of any model). Correctness × stat validity correlate at r = 0.43 — largely orthogonal capabilities.

Getting the right number and knowing whether to trust it are different skills.

2. Statistical validity is category-dependent, not uniformly weak

• Statistical inference: 0.897
• EDA: 0.849
• ML engineering: 0.740
• Modeling: 0.603
• Feature engineering: 0.520

Models reach for statistical language when the task name signals it. Feature engineering is worst — models report importances without uncertainty bounds because nothing in the name says "statistics expected."

3. Claude Haiku burned 608,861 tokens on a task GPT-4.1 finished in 30,000

Same task. GPT-4.1 scored higher. Token count is a capability signal, not just a cost metric.

4. Single-run benchmarks lied about Grok-3-mini

At n=1, Grok-3-mini showed 0.00 correctness on 7 sklearn tasks — looked like a hard failure. At n=5, it averages 0.50–0.89 on modeling — the blind spot is probabilistic, not deterministic.

This is why the leaderboard uses multi-run CI instead of single-run point estimates.

What makes RDAB different from existing benchmarks:

Most benchmarks ask "did it get the right answer?" RDAB asks whether the agent did the analysis correctly, efficiently, in production-quality code, and with appropriate statistical rigor — all at once.

A model can score 1.0 on correctness and 0.25 on statistical validity on the same task. That delta is what RDAB measures.

Full scoring spec (every formula, regex, threshold, known limitation) is in SCORING_SPEC.md — independently reproducible without reading source code.

Run it yourself free in 60 seconds:

bash

git clone https://github.com/patibandlavenkatamanideep/RealDataAgentBench
cd RealDataAgentBench && pip install -e ".[dev]"
cp .env.example .env
# Add GROQ_API_KEY from console.groq.com (free, no credit card)
dab run --all --model groq --runs 5
# Total cost: ~$0.007

Links:

• GitHub: https://github.com/patibandlavenkatamanideep/RealDataAgentBench
• Live leaderboard (filterable by category + cost): https://patibandlavenkatamanideep.github.io/RealDataAgentBench
• Companion tool (benchmark your own CSV, no code needed): https://costguard-production-3afa.up.railway.app

Happy to answer questions about methodology, the scorer design, or any specific findings. Known limitations are documented in the README the stat validity scorer is lexical, synthetic datasets have known constraints, I've tried to be transparent about all of it.

#learnmachineLearning #LLM #benchmark #opensource #datascience

Most LLM agent benchmarks only ask: “Did it get the right answer?”I built RealDataAgentBench (RDAB) because that’s not enough. It evaluates whether LLM agents do data science in a statistically sound way — reporting uncertainty, using appropriate tests, avoiding causal overreach, etc.What it measures (4 independent dimensions)

• Correctness
• Code Quality
• Efficiency (tokens + steps)
• Statistical Validity ← the dimension almost everyone ignores

Key findings after 1,180+ runs across 12 frontier models + 39 tasks:

• Frontier models score 0.84–0.99 on correctness but as low as 0.52 on statistical validity (especially feature engineering & modeling tasks)
• gpt-4.1-mini currently leads overall (0.872) at ~65× lower cost than GPT-5
• Free Groq Llama-3.3-70B beats GPT-5 overall
• Claude models dominate statistical validity while GPT models win on raw correctness (the two dimensions are only moderately correlated)
• Claude agents frequently fall into massive token spirals (e.g. 600k+ tokens on one task)

Live Leaderboard: https://patibandlavenkatamanideep.github.io/RealDataAgentBench/

GitHub: https://github.com/patibandlavenkatamanideep/RealDataAgentBench

Companion tool (CostGuard): Upload your own CSV and get real-time cost + performance ranking → https://costguard-production-3afa.up.railway.appThe entire benchmark is fully open source, reproducible, and has:

• 39 tasks (33 synthetic + 6 real UCI/sklearn datasets)
• Multi-run CI with confidence intervals
• Category-aware scoring
• Transparent methodology + known limitations

I’m actively looking for feedback, contributors, and people who want to submit their own model results.If you work with LLM agents on structured/tabular data (RAG, data analysis agents, analytics copilots, etc.), I’d love to know:

• Does this match the failure modes you see in production?
• What other dimensions should we add next?

Would really appreciate stars, feedback, or just running a few tasks yourself. The CLI makes it stupidly easy (dab run eda_001 --model groq works for free).

Looking forward to your thoughts!