Senior Machine Learning Engineer

HackerRank assesses technical skill for thousands of hiring teams, and this role works on three problems the posting names openly, which is rare and makes it much easier to judge whether you want the job. Chakra is an autonomous AI interviewer that conducts, adapts to and evaluates technical interviews end to end. Integrity is detecting fraud and suspicious behaviour across multiple signal types, where the methods candidates use to game assessments change frequently so the models have to keep up. Evaluation is measuring technical skill in a world where AI writes the code, and the posting admits directly that the old proxies no longer hold and the new ones have not been defined yet. You would design and ship production machine learning across all three, own the full lifecycle from problem framing and data strategy through experimentation, deployment and iteration, build the evaluation infrastructure and benchmarking pipelines, and set the production bar for new signal categories from scratch. Mentoring junior ML engineers is part of it.

What the posting requires

- 4+ years building and shipping machine learning systems that run in production at scale
- Proficiency in Python, with practical experience building data pipelines and deploying models
- Systems thinking, which the posting defines precisely: model accuracy, data pipelines, serving infrastructure and customer outcomes are one problem, not four
- Treating evaluation methodology as seriously as model performance, on the stated view that a metric measured wrong is worse than no metric

Counts as a bonus, not a requirement

- Multimodal systems across vision, natural language, audio or behavioural signal pipelines
- Large language model experience including fine tuning, RLHF or multi turn agentic systems
- A background in adversarial machine learning, fraud detection or anomaly detection
- Publications or open source contributions in detection, robustness or evaluation methodology

What you would actually be doing

- Designing and shipping production machine learning across the Chakra, integrity and evaluation domains
- Owning the full lifecycle: problem framing, data strategy, experimentation, deployment and iteration
- Building evaluation infrastructure and benchmarking pipelines that measure model quality reliably before and after deployment
- Defining the architecture and production bar for new signal categories from scratch
- Mentoring junior machine learning engineers and establishing team practice for monitoring, feedback loops and quality standards

Location and working pattern

Hybrid in Bangalore.

The company is explicit about who thrives here

The posting says you will do well if messy, undefined problems interest you more than optimising inside clean ones, if ambiguity energises you when the right framing is itself part of the work, and if defining what a system should be appeals more than maintaining what already exists. It also offers direct access to leadership and fast feedback loops.

A good fit if

You have production machine learning experience and want genuinely unsolved problems. Detecting AI assisted cheating and redefining how coding skill is measured are live, open questions, and few Indian roles work on them directly.

Think twice if

You want a stable, well specified roadmap, or your machine learning work has been mostly research without deployment. The requirement is specifically shipping systems that run in production at scale.