Should I focus on data science or ML engineering as my entry path?

Pick the shape that matches your portfolio. Strong SQL + product instincts + stats fluency points to product DS (Meta E3, Airbnb IC2, Netflix DS). Strong PyTorch + research-engineering + paper-fluency points to MLE / research-engineer (Anthropic, OpenAI, DeepMind). The two paths diverge at mid-level and converge again at staff+. Trying to interview for both at once is the failure mode — your portfolio reads as unfocused.

Do I need a PhD to get into AI labs like Anthropic or OpenAI?

Strongly preferred for research-track roles at top-tier labs, not absolute. Anthropic's careers page (anthropic.com/careers) and OpenAI's research-eng listings explicitly hire non-PhD candidates with strong research-engineering portfolios — typically people with a co-authored paper at NeurIPS/ICML/ICLR or a substantive open-source contribution to a frontier ML library. The non-PhD path requires a paper-quality artifact OR a senior research-engineer referral. Pure-engineering MTS roles at these companies hire BS/MS more readily.

Is Kaggle still relevant in 2026?

Yes, with caveats. A medal (top 10%) on a featured competition is a legible portfolio signal at FAANG analytics-DS roles and at applied-ML roles. It is weaker signal at AI labs that prioritize research-engineering and novel modeling. Kaggle is also a good way to develop the iterate-against-leaderboard discipline that translates to real ML work. The trap: spending two years grinding Kaggle without ever shipping a deployed model or a paper.

Should I learn JAX or stick with PyTorch?

PyTorch first — it is the dominant ML framework at most tech companies in 2026 and is what FAANG MLE interviews implicitly assume. JAX is a differentiator at Anthropic and Google DeepMind specifically (both have JAX-heavy public stacks per their engineering blogs). The right pattern for junior: PyTorch fluency at production depth, JAX literacy from one substantive project. The 'why JAX' answer (functional purity, vmap, pmap, XLA compilation) is a senior-conversation entry point.

How important are LLM and foundation-model skills at junior?

Increasingly weighted. Hello Interview's 2025–2026 hiring posts and several FAANG / AI-lab engineering blogs explicitly weight LLM-fluency at junior. The bar in 2026: comfort fine-tuning a Llama-4 or Qwen-3 model on a real task with PEFT (LoRA via peft library), familiarity with the OpenAI / Anthropic API, ability to design a basic eval, and a written opinion on prompt engineering vs fine-tuning. Junior roles at AI labs explicitly require this; FAANG analytics-DS roles weight it lower.

What's the difference between MLE and Research Engineer at AI labs?

MLE typically means 'productionizing models, building infra, deployment.' Research Engineer means 'partner with researchers to run experiments, implement papers, design evals.' Anthropic, OpenAI, and DeepMind use both titles distinctly. Compensation is similar; work shape differs. Research Engineer is the closer relative of academic research; MLE is the closer relative of senior software engineer. Read the JD carefully — the same title means different things at different companies.

How long should I prepare before applying?

Quality is the variable, not time. The candidates who get hired are the ones whose portfolio + interview prep + ML fluency are at the level the company wants — typically 12–24 months of focused work post-MS or post-bootcamp. Applying without that bar wastes interview cycles and burns recruiter relationships at AI-tier companies, where networks are tight. Better to delay applying by 3 months to ship one more substantive project than to apply early and fail the screen.

Data Scientist / ML Engineer Hub

Junior Data Scientist / ML Engineer Guide for Tech Companies (2026)

By Blake Crosley · Last verified 2026-04-29

In short

A junior data scientist or ML engineer (typically 0–3 years) is hired on portfolio strength + interview signal — not credentials. The bar that clears screening at FAANG-tier and AI-lab companies in 2026 has three components: a shipped analysis or ML project with named outcomes (Kaggle medal, paper, deployed model on Hugging Face Hub), SQL fluency at the level of medium LeetCode-SQL, and Python + scikit-learn + PyTorch literacy. Total comp at junior FAANG-tier clusters $190k–$280k including stock; AI-lab tier (Anthropic, OpenAI, Scale AI) sits $250k–$400k for entry-level Member of Technical Staff roles, often exceeding FAANG senior comp at hire.

Key takeaways

FAANG-tier junior DS / MLE total comp $190k–$280k including stock per levels.fyi 2026 data; Meta E3 Data Scientist $200k–$280k (levels.fyi/companies/facebook/salaries/data-scientist), Google L3 ML Engineer $200k–$290k (levels.fyi/companies/google/salaries/machine-learning-engineer), Anthropic entry MTS $250k–$400k+ (anthropic.com/careers, levels.fyi/companies/anthropic).
Portfolio bar that clears screening: ONE substantive end-to-end project — a Kaggle competition with a medal (top 10%), a published paper at a real venue (NeurIPS / ICML / ACL / EMNLP / CVPR workshops accept junior work), or a model deployed on Hugging Face Hub with documented eval. Tutorial replicas (Titanic survival, MNIST classifier) don't count.
Interview format at FAANG DS: 1 recruiter + 1 SQL screen (LeetCode-SQL medium) + 4–5 onsite (1 SQL deep, 1 product/case, 1 ML/stats, 1 coding, 1 behavioral). At AI-labs (Anthropic, OpenAI): less SQL, more 'implement attention from scratch in 30 min' + ML-system design + research-fluency conversation.
Python + pandas + scikit-learn + PyTorch fluency is table-stakes; JAX is differentiator at AI labs (Anthropic and Google DeepMind have JAX-heavy stacks per their public blog posts). Knowing 'why JAX' (functional purity, vmap, pmap) is a senior-MTS-conversation entry point at AI-tier companies.
Statistics depth — sample-size calculations, confounders, the difference between A/B test power and effect-size detection — separates the candidate who 'ran a model' from the candidate who can 'design an experiment.' Meta's E3 Data Scientist interview is the canonical stress test on this dimension.

What junior data scientists and ML engineers actually do

The day-to-day at a FAANG-tier or AI-lab junior role splits into two archetypes that are increasingly distinct in 2026:

Product / Analytics Data Scientist (Meta, Airbnb, Netflix Studio). Build and ship A/B tests with a PM and an engineer. Write the SQL to define the metric, run the experiment, write the readout, defend the conclusion in a metrics review. Junior scope: own one experiment end-to-end per sprint with a senior reviewing the analysis. Examples: 'Did the new onboarding flow lift day-7 retention?' / 'Is the recommendation-quality regression we observed a real effect or seasonality?'
ML Engineer (Anthropic, OpenAI, Google DeepMind, Databricks). Train, evaluate, and ship models. Write the data-loading pipeline. Run the training job. Compute the eval metrics. Push the checkpoint to the model registry. Junior scope: own one experiment per week or two, with a senior owning the research direction. Examples: 'Run the ablation matrix on the mid-training mix' / 'Build the new code-eval harness against HumanEval+ and MBPP.'

Three patterns to expect:

SQL is load-bearing at every analytics-DS company. Meta, Airbnb, Netflix, Stripe, Uber — all of them run on SQL-defined metrics. A junior DS who 'doesn't really do SQL' fails the screen. Hello Interview's data-science interview guide (hellointerview.com/blog) covers the SQL bar.
The eval pipeline is part of the job at AI labs. Anthropic and OpenAI explicitly weight 'can you design a good eval' as a junior-research-engineer skill. The OpenAI public eval framework (github.com/openai/evals) and Anthropic's evals research posts on anthropic.com/research are required reading.
Reproducibility is non-negotiable. Weights & Biases (wandb.ai/site/articles) and MLflow are the canonical experiment-tracking layers. A junior who can't produce a reproducible run loses team trust quickly.

The portfolio bar that clears screening

The dominant junior-screen failure mode is the 'tutorial portfolio' — Titanic, MNIST, three Kaggle Getting Started competitions. Recruiters at FAANG-tier and AI-lab companies see 100+ of these per week. The portfolio shapes that clear:

One Kaggle medal (top 10%) on a competition with a real held-out test set. Bonus if it's a featured competition (Kaggle's own medal-eligible tier). Solo or small-team where you can name your contribution concretely. The signal: 'this candidate can iterate on a leaderboard with real validation discipline, not just memorize a tutorial.'
One paper at a real venue. NeurIPS / ICML / ICLR / ACL / EMNLP / NAACL / CVPR workshops accept junior-quality work. arXiv-only is acceptable but weaker — peer review adds signal. Co-authored is fine; first-author is stronger. A junior who has shepherded one paper through review demonstrates research-engineering fluency at a level most bootcamp graduates cannot.
One model on Hugging Face Hub with documented eval. Fine-tune a Llama-3 or Qwen-2.5 model on a real domain task. Push to HF Hub with a model card following the Hugging Face Hub template (huggingface.co/docs/hub/model-cards). Eval against a real benchmark — not 'I tested it on my own questions.' This signals 'can navigate the modern open-model ecosystem, can publish, can document.'
Substantive open-source ML contribution. A merged PR into a 1k+ star ML repo (transformers, scikit-learn, PyTorch, jax, lightning, axolotl). Not a typo fix. Examples: a non-trivial bug fix in a transformers tokenizer, a new metric in scikit-learn, a JAX example fixed for the latest API. The signal is 'this candidate can navigate someone else's ML codebase.'

The GitHub profile matters. Recruiters look at the recent activity graph and the README of pinned repos. Two years of consistent contributions to one or two real ML projects beats thirty abandoned tutorial notebooks. Quality and continuity over quantity.

The interview at junior level: company-by-company

The shape of a junior DS / MLE interview at FAANG-tier and AI-lab companies in 2026, drawn from public Hello Interview reports, Glassdoor data, and candidate retrospectives on Reddit r/MachineLearning and r/datascience:

Company	Format	SQL weight	ML / research weight	Coding weight
Meta DS (E3)	1 recruiter + 1 SQL screen + 4 onsite (1 SQL, 1 product / case, 1 stats, 1 behavioral)	Highest among peers	Stats-leaning, not deep-learning-leaning	Light — Python pandas only
Google MLE (L3)	1 phone screen + 4 onsite (2 coding, 1 ML system / stats, 1 behavioral); hiring committee	Moderate	ML fundamentals deep; less LLM-specific at L3	Highest algorithmic bar
Netflix MLE (L4 — entry)	1 recruiter + 1 technical + 4 onsite incl. ML system + culture	Moderate	Deep — Netflix hires senior even at "entry"	Strong
Anthropic MTS	1 recruiter + 1 technical + 4–5 onsite (research-eng round, ML coding round, eval-design round, behavioral)	Light	Highest weight — research fluency tested	Moderate; less leetcode
OpenAI MTS	1 recruiter + 1 technical + 4–5 onsite (similar to Anthropic — research + coding + ML system)	Light	Highest weight — research fluency tested	Moderate
Databricks MLE (L3)	1 phone + 4 onsite (2 coding, 1 ML system, 1 distributed-systems-leaning ML, 1 behavioral)	Moderate (Spark SQL)	Strong on distributed ML, MLflow	Strong

The pattern: at analytics-DS-shape companies (Meta, Airbnb, Netflix Studio), SQL + product judgment + stats dominates. At AI-labs (Anthropic, OpenAI, Google DeepMind), research fluency and eval design dominate. At ML-platform companies (Databricks, Scale AI), distributed ML and infrastructure fluency matter. Hello Interview's DS / MLE level cross-reference (hellointerview.com/blog/understanding-job-levels-at-faang-companies) lays out the rubrics.

Compensation: real bands and what's actually offered

Total comp at junior FAANG-tier and AI-lab in 2026 (US, per levels.fyi):

Company	Level	Base	Total comp
Meta DS	E3	$140k–$185k	$200k–$280k
Google MLE	L3	$140k–$190k	$200k–$290k
Netflix MLE	L4 (entry)	$190k–$260k	$280k–$420k (single-band, no stock-comp split)
Anthropic MTS	entry	$200k–$300k	$250k–$400k+ (heavy equity)
OpenAI MTS	entry	$200k–$310k	$300k–$500k+ (heavy PPU equity)
Databricks MLE	L3	$150k–$200k	$220k–$340k
Scale AI	entry MLE	$170k–$220k	$240k–$380k
Hugging Face	entry	$140k–$180k (remote-friendly)	$180k–$260k

The notable structural fact in 2026: AI-labs (Anthropic, OpenAI, Scale AI) pay entry-level total compensation that often exceeds FAANG senior comp. OpenAI's PPU (Profit Participation Unit) program in particular has produced reported entry-level total comp in the $400k–$700k range in public levels.fyi reports — though PPU value depends on company outcomes. Netflix uses a single-band 'all cash unless you elect stock' model, which pushes nominal salary higher than peer FAANG. Pay-transparency-disclosed ranges in California and Washington postings are the most authoritative source per role.

Failure modes at junior: what gets you screened out

Tutorial-replica portfolio. Titanic + MNIST + Iris. Recruiter screens past on first scan. The bar is one substantive project, not five tutorials.
'I use ChatGPT for everything' affect. Junior candidates who can't articulate the math behind gradient descent or why softmax has a temperature parameter fail the ML round. AI tools accelerate experienced practitioners; they do not substitute for fundamentals at the interview stage.
SQL weakness. Cannot explain the difference between INNER JOIN and LEFT JOIN under stress, or cannot write a window-function query. Failure mode at every analytics-DS shop. Meta E3 SQL screen is the canonical stress test.
Cannot explain a model they shipped. 'I trained an XGBoost model and got 0.85 AUC' — but cannot explain feature importance, cross-validation strategy, or what the AUC actually measures versus precision-recall. Senior interviewers probe one or two layers down; junior candidates who can't go deep on their own work fail.
No statistical literacy. Cannot explain Type I vs Type II error, sample-size calculation, or the difference between practical and statistical significance. Failure mode at every product-DS shop and at AI-labs that test eval-design.
Cargo-cult deep learning. Used a transformer because it's trendy, when the problem was a 1000-row tabular classification. Demonstrates pattern-following without judgment. Better: a thoughtfully-chosen scikit-learn pipeline with documented baselines.
Coding-screen weakness. ML depth does not compensate at Google L3 or OpenAI MTS. A junior who can't solve a medium LeetCode problem in 30 minutes does not advance.

Frequently asked questions

Should I focus on data science or ML engineering as my entry path?: Pick the shape that matches your portfolio. Strong SQL + product instincts + stats fluency points to product DS (Meta E3, Airbnb IC2, Netflix DS). Strong PyTorch + research-engineering + paper-fluency points to MLE / research-engineer (Anthropic, OpenAI, DeepMind). The two paths diverge at mid-level and converge again at staff+. Trying to interview for both at once is the failure mode — your portfolio reads as unfocused.
Do I need a PhD to get into AI labs like Anthropic or OpenAI?: Strongly preferred for research-track roles at top-tier labs, not absolute. Anthropic's careers page (anthropic.com/careers) and OpenAI's research-eng listings explicitly hire non-PhD candidates with strong research-engineering portfolios — typically people with a co-authored paper at NeurIPS/ICML/ICLR or a substantive open-source contribution to a frontier ML library. The non-PhD path requires a paper-quality artifact OR a senior research-engineer referral. Pure-engineering MTS roles at these companies hire BS/MS more readily.
Is Kaggle still relevant in 2026?: Yes, with caveats. A medal (top 10%) on a featured competition is a legible portfolio signal at FAANG analytics-DS roles and at applied-ML roles. It is weaker signal at AI labs that prioritize research-engineering and novel modeling. Kaggle is also a good way to develop the iterate-against-leaderboard discipline that translates to real ML work. The trap: spending two years grinding Kaggle without ever shipping a deployed model or a paper.
Should I learn JAX or stick with PyTorch?: PyTorch first — it is the dominant ML framework at most tech companies in 2026 and is what FAANG MLE interviews implicitly assume. JAX is a differentiator at Anthropic and Google DeepMind specifically (both have JAX-heavy public stacks per their engineering blogs). The right pattern for junior: PyTorch fluency at production depth, JAX literacy from one substantive project. The 'why JAX' answer (functional purity, vmap, pmap, XLA compilation) is a senior-conversation entry point.
How important are LLM and foundation-model skills at junior?: Increasingly weighted. Hello Interview's 2025–2026 hiring posts and several FAANG / AI-lab engineering blogs explicitly weight LLM-fluency at junior. The bar in 2026: comfort fine-tuning a Llama-4 or Qwen-3 model on a real task with PEFT (LoRA via peft library), familiarity with the OpenAI / Anthropic API, ability to design a basic eval, and a written opinion on prompt engineering vs fine-tuning. Junior roles at AI labs explicitly require this; FAANG analytics-DS roles weight it lower.
What's the difference between MLE and Research Engineer at AI labs?: MLE typically means 'productionizing models, building infra, deployment.' Research Engineer means 'partner with researchers to run experiments, implement papers, design evals.' Anthropic, OpenAI, and DeepMind use both titles distinctly. Compensation is similar; work shape differs. Research Engineer is the closer relative of academic research; MLE is the closer relative of senior software engineer. Read the JD carefully — the same title means different things at different companies.
How long should I prepare before applying?: Quality is the variable, not time. The candidates who get hired are the ones whose portfolio + interview prep + ML fluency are at the level the company wants — typically 12–24 months of focused work post-MS or post-bootcamp. Applying without that bar wastes interview cycles and burns recruiter relationships at AI-tier companies, where networks are tight. Better to delay applying by 3 months to ship one more substantive project than to apply early and fail the screen.

Sources

About the author. Blake Crosley founded ResumeGeni and writes about data science, machine learning, hiring technology, and ATS optimization. More writing at blakecrosley.com.