How we interview · Machine learning engineer

    Hire machine learning engineers who ship models to production

    ML and AI engineers, mid-level to staff, interviewed on turning a business problem into a measurable model and running it in production, including LLM features.

    Most ML hiring tests the wrong thing. A candidate who can explain gradient descent may never have shipped a model that real users depend on, and a notebook that scores well is a long way from a feature in production.

    We look for engineers who start from the business question, pick the simplest approach that works, measure it honestly, and build the pipeline, serving and monitoring around it. That now includes LLM features: retrieval, evaluation and cost.

    What they work on: Python, PyTorch, scikit-learn, feature stores, model serving, vector databases, LLM APIs and evaluation tooling.

    When to hire one

    • You have data and a clear use case, but no one to take a model to production.
    • Your LLM feature works in a demo but is slow, costly or unreliable.
    • Your data scientists' models never leave the notebook.

    What we assess in machine learning engineers

    Every report also covers impact, judgement, how they raise the team and how they work with others.

    Craft

    ML fluency
    A broad toolkit, current with the field, and the judgement to choose the simplest method that works.
    Problem framing
    Turning a business goal into a target, a metric and a baseline worth beating.
    Evaluation
    Honest offline and online measurement: leakage, bias, statistical rigour.
    Production ML
    Pipelines, serving, monitoring for drift, latency and cost.
    Software engineering
    Code others can maintain, tests, and reusable components.

    How we recommend a level

    Mid-level
    Builds and evaluates models for a defined problem and gets them into production with guidance.
    Senior
    Frames business goals as ML problems, picks metrics that last and owns the model in production.
    Staff / lead
    Builds shared ML tooling and sets how teams train, evaluate and deploy models.

    The 60-minute interview

    Warm-up (10 min)
    A model they shipped: the goal, the metric, and what happened after launch.
    Framing (20 min)
    A business problem, such as churn or search ranking, turned into an ML plan with metrics.
    Hands-on (25 min)
    Critique and improve an evaluation, or design a retrieval-augmented feature with its failure cases.
    Their questions (5 min)
    Do they ask about data quality and how success is measured?

    Red flags we screen for

    • Reaches for a large model before trying a baseline.
    • Reports accuracy without asking how it's measured.
    • Has never monitored a model after launch.

    What you receive for each shortlisted candidate:

    Candidate N.S.

    Machine Learning Engineer · 60-minute technical interview

    Sample report
    Tested
    Framing a churn prediction problem, and designing evaluation for a support-ticket RAG assistant.
    Strong
    Started with a simple baseline. Spotted label leakage in the sample data. Thought about cost per query.
    Gaps
    Limited experience serving models at scale. Light on online A/B testing.
    Level
    Mid-level, close to senior on problem framing.
    Probe next
    How they would roll out the assistant safely, and monitor answer quality after launch.
    RecommendationShortlist
    A sample interview report. The candidate is fictional; the format is the one clients receive.

    Questions about hiring machine learning engineers

    Yes. Many ML roles now centre on LLM features: retrieval, evaluation, guardrails and cost. We interview for that work specifically, not for general AI vocabulary.

    First interviewed profiles usually reach you within a few days of the brief, and selection through your own rounds takes about two weeks. Joining then depends on the candidate's notice period, which in India is often 30 to 90 days.

    A percentage of the hire's annual CTC, set by seniority and paid only when the candidate joins. There's no retainer and no fee if nobody joins.

    Overlap is large, but an ML engineer owns getting models into production and keeping them healthy. If your need is analysis and experiments, tell us at the brief and we'll scope the role accordingly.

    Other engineering roles we hire

    Hiring on your own terms? Permanent hiring, contract hires or interviews only.

    Tell us the role.

    Four details and we'll come back within a working day with how we'd approach it.

    No retainer. You pay only when someone joins. Or write to talent@algosmiths.com