WikiSenior ML Engineer
Apply on wwrAbout this role
<img src="https://we-work-remotely.imgix.net/logos/0171/4866/logo.gif?ixlib=rails-4.0.0&w=50&h=50&dpr=2&fit=fill&auto=compress" /> <p> <strong>Headquarters:</strong> California, US <br /><strong>URL:</strong> <a href="https://www.chess.com/about">https://www.chess.com/about</a> </p> <p><strong>About You</strong></p> <p>You believe in the power of ML and AI to build amazing, interesting, and insightful products and services for humanity… and you’d love to do that in chess!</p
Skills / categories
About data / ml roles
Data and ML roles split into three camps: analysts (SQL, dashboards, business questions), engineers (pipelines, infrastructure), and scientists (modeling, experimentation). The job title doesn't always tell you which one — read the description carefully.
Typical skills: SQL, Python, pandas, basic stats; ML roles add scikit-learn / PyTorch / TensorFlow; engineering roles add Airflow / dbt / Spark
Salary insights (US, rough)
Typical range for data / ml roles in the US is $85,000–$240,000/year, varying widely with seniority, company stage, and city.
Estimates only. For company-specific numbers, check levels.fyi (tech), Glassdoor, or ask in the interview.
How to prep for the interview
Data interviews split: analysts get SQL + case-study questions, engineers get pipeline-design + coding, scientists get stats + ML modeling + sometimes a take-home dataset. Read the JD carefully and prepare to the actual flavor — don't waste time on PyTorch for an analyst role.
Common questions across all three: "How would you measure success for [product feature]?", "Walk me through a project where the data told you something surprising", and SQL window functions (LAG, ROW_NUMBER, PARTITION BY — practice these specifically). For ML roles, expect a question about overfitting, bias-variance tradeoff, and how you'd debug a model that performs well in training and badly in production.
Where this role typically leads
Career paths in data/ML have gotten messier as the discipline has matured. The clearest progression: Junior Analyst → Senior Analyst → Analytics Manager on the analyst side, Junior Data Engineer → Senior → Staff on the engineering side, and Junior DS → Senior DS → Principal DS / ML Lead on the modeling side. Salary peaks are highest for ML engineers + research scientists at big-tech firms.
Cross-discipline moves are common: analysts who learn engineering often outearn engineers who didn't learn business context. Stay close to the business — the most valuable data people are the ones executives bring into strategy discussions, not the ones building dashboards alone.
Red flags to watch for
- "Data scientist" role that's really a SQL analyst. Read the responsibilities carefully — if it's all dashboards and ad-hoc queries, it's an analyst role with a science title.
- No mention of stakeholder collaboration. Data work in isolation is usually data work that gets ignored. The role needs to be embedded with a business team.
- "AI / ML" listed everywhere but no specifics. If they can't name the actual problems they want to solve with ML, the company is buzzword-shopping.
- Asking for "10+ years of Python" or impossible combinations. Either the JD was written by HR without input from a data team, or the company has unrealistic expectations.
Frequently asked questions
How do I apply to this role?
Click the "Apply on wwr" button at the top of this page. You'll be sent to the original posting where the employer accepts applications. Wikishopline doesn't collect resumes or process applications.
Is this listing current?
Wikishopline aggregates jobs daily from partner sources (wwr). Postings older than ~14 days are pruned, but always verify the role is still open on the employer's site before you spend time on a cover letter.
Does Wikishopline charge employers or applicants?
No. Aggregated jobs are free for both sides. Wikishopline also accepts $5 / 30-day paid postings at /jobs/submit for employers who want direct visibility — but the listing you're viewing was sourced from a partner.
What does a data / ml role typically involve?
Data and ML roles split into three camps: analysts (SQL, dashboards, business questions), engineers (pipelines, infrastructure), and scientists (modeling, experimentation). The job title doesn't always tell you which one — read the description carefully.
What's the typical salary range for data / ml roles in the US?
Roughly $85,000–$240,000 USD/year, depending on seniority, location, and company stage. This is a wide range on purpose — verify against levels.fyi or Glassdoor for the specific company.
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