Machine Learning Engineer

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