We are seeking an exceptional
Senior Machine Learning Engineer
to design, build, and evaluate high-performance ML systems. This role focuses on creating high-quality datasets, benchmarking tasks, and evaluation workflows that advance the capabilities of large language models (LLMs) and other AI systems.
This opportunity is ideal for engineers with
competitive ML experience (Kaggle, DrivenData, etc.)
, strong modelling intuition, and a proven ability to convert complex real-world problems into robust, production-ready ML pipelines.
Role Overview
As a Senior ML Engineer, you will:
Frame innovative ML problems to enhance LLM capabilities
Design, build, and optimize models for classification, prediction, NLP, recommendation, and generative tasks
Run rapid experimentation cycles, evaluate model performance, and iterate continuously
Conduct advanced feature engineering, preprocessing, and dataset curation
Implement adversarial testing, robustness checks, and bias evaluations
Fine-tune, evaluate, and deploy transformer-based or generative models
Maintain clear documentation of datasets, experiments, and model decisions
Stay updated on modern ML research, tools, and evaluation methodologies
Required Qualifications
3+ years
of professional experience in machine learning development
Degree in
Computer Science, Engineering, Mathematics, Statistics
, or a related field
Demonstrated competitive ML experience (Kaggle, DrivenData, etc.)
Proven record of
top-tier performance
in ML competitions (medals, rankings, finalist placements)
Strong Python skills and experience with
PyTorch/TensorFlow
Solid understanding of ML fundamentals: statistics, optimization, architectures, evaluation
Experience with distributed training, reproducible ML pipelines, and experiment tracking
Cloud experience (
AWS, GCP, or Azure
)
Strong communication skills to clearly explain modelling decisions
Fluency in English
B
ased in India
Preferred Qualifications
Kaggle
Grandmaster/Master
or multiple Gold Medals
Experience creating ML benchmarks, evaluation frameworks, or challenge problems
Background in
LLMs, generative AI
, or multimodal learning
Large-scale distributed training experience
Open-source contributions, technical blogs, or research publications
Technical leadership or mentorship experience
Experience with vector databases, LLM fine-tuning, or generative AI workflows
Hands-on MLOps tools:
Weights & Biases, MLflow, Airflow, Docker
Experience optimizing inference performance and deploying models at scale
Why Join
Work on high-impact ML challenges and cutting-edge AI research
Collaborate with world-class technical teams across forecasting, tabular ML, multimodal analytics, and experimentation
Flexible engagement options:
30–40 hrs/week or full-time
Fully remote, async-friendly environment optimized for deep technical work
Inclusive hiring process with reasonable accommodations available