Job Description

Role : Machine Learning Engineer / Researcher

Location: In Person 5 days in Bangalore office. (Whitfield)

Apply using : https://hello.clr3.org/4f49f149e32200cbc6ad

We are looking for a

Machine Learning Engineer

who can take ambiguous, real-world problems and build

scalable, production-grade ML systems end-to-end

--from problem formulation and data pipelines to model deployment and monitoring.

You'll work on

blockchain intelligence and quantitative ML systems

, including transaction graph modeling, behavioral pattern detection, anomaly detection, and predictive systems on large-scale, high-velocity datasets. This role sits at the intersection of

ML modeling, data engineering, and production systems

.

This is not a "train-a-model-and-move-on" role. You'll be responsible for

shipping ML that runs reliably in production

, improving it over time, and ensuring it drives real business decisions. Prior blockchain or finance experience is helpful but not required--we value strong ML engineering fundamentals and the ability to learn new domains quickly.

As an early ML hire, you'll have

outsized ownership and influence

over our ML stack, architecture, and best practices.

What We Offer

Outcome-linked bonuses

-- your models power real decisions, and success is rewarded

Growth upside

-- build foundational ML systems with long-term impact

Fast career progression

-- own core ML infrastructure and systems as the team scales

Production impact

-- models you build will be deployed and actively used

High autonomy

-- freedom to design, experiment, and ship, with strong engineering support
Key Responsibilities

Own ML systems

end-to-end

-- data ingestion, feature engineering, model training, deployment, and monitoring Design and implement ML pipelines for

behavioral modeling, graph-based learning, time-series prediction, and anomaly detection

Build

scalable, maintainable, and reliable

ML services that run in production Work with large-scale and streaming data (transaction graphs, behavioral signals, real-time feeds) Translate research ideas and prototypes into

production-ready ML systems

Collaborate closely with backend, data, and quant teams to integrate ML into core products Implement proper evaluation, monitoring, retraining, and drift detection strategies Optimize models for performance, latency, and cost in real-world environments Maintain clear documentation for models, pipelines, and system design
Required Qualifications

2+ years of hands-on ML engineering experience

(industry, startups, internships, or applied research) Strong foundations in machine learning -- understanding model behavior, trade-offs, and failure modes Solid experience with

supervised/unsupervised learning

, deep learning (GNNs, transformers, sequence models), and classical ML Strong Python skills and experience with

PyTorch

(TensorFlow/JAX acceptable) Experience building

data pipelines, feature stores, or training workflows

Familiarity with model evaluation, validation, and preventing data leakage Experience working with

messy, real-world data

at scale Ability to independently own and deliver ML projects with minimal hand-holding Strong fundamentals in probability, statistics, and linear algebra
Preferred Qualifications (Nice to Have)

Experience with

graph ML / GNNs

or large-scale network analysis Time-series or sequence modeling experience Exposure to

blockchain analytics, DeFi, or financial data

Experience with

distributed systems

(Spark, Ray, Kafka, etc.) Familiarity with

real-time inference

, streaming ML, or low-latency systems Experience with model monitoring, drift detection, or MLOps tooling Contributions to open-source ML projects or production ML platforms
Who You Are

A

builder first

-- you care about ML that runs in production and delivers value Strong in

first-principles thinking

-- you understand why systems work, not just how to use them Comfortable operating in ambiguity -- you can define the problem and engineer the solution Pragmatic -- you balance model sophistication with reliability and scalability Ownership-driven -- you take responsibility for outcomes, not just code Curious and fast-learning -- you can ramp up quickly in new technical domains Growth-minded -- excited to help shape the ML culture, stack, and team
Job Type: Full-time

Pay: ?400,000.00 - ?1,200,000.00 per year

Work Location: In person