Remote Machine Learning Engineer jobs open to India.

What We're Looking For 5+ years of software/ML engineering experience, including hands-on work building or maintaining evaluation, testing, or measurement infrastructure for ML systems. Direct experience with foundation model evaluation and benchmarking beyond using APIs — experience gained at a foundation model lab or similarly frontier research environment is strongly preferred. Demonstrated ability to own an evaluation solution end-to-end — from initial design and dataset/methodology creation through pipeline build, deployment, and production monitoring — with minimal hand-off. Direct experience designing evals for LLMs and/or AI agents — e.g., benchmark design, LLM-as-judge pipelines, human annotation platforms, or A/B and offline/online eval frameworks. Strong programming skills in Python, PyTorch / Tensorflow and experience building production-grade data/ML pipelines. Hands-on knowledge of key model training and evaluation platforms, particularly AWS (e.g., SageMaker, Bedrock) and Databricks (e.g., MLflow, Unity Catalog, Delta Lake). Solid grounding in applied statistics — comfortable reasoning about sample size, variance, significance testing, and the limitations of aggregate metrics. Working knowledge of how LLMs are trained and adapted (pretraining, SFT, RLHF/RLAIF/DPO) and how eval signal feeds into that lifecycle. Experience with agentic architectures — tool calling, multi-step planning, memory, orchestration frameworks — and the unique evaluation challenges they introduce. Familiarity with eval and observability tooling (e.g., internal or open-source frameworks for tracing, dataset versioning, experiment tracking). Excellent cross-functional collaboration skills; able to translate ambiguous product/research questions into concrete, measurable eval criteria. A bias toward rigor and skepticism — you instinctively question whether a metric is actually measuring what it claims to. Bonus Points We prioritize candidates with direct experience in the following areas: Post-training & evals: hands-on experience with post-training techniques (SFT, RLHF, RLAIF, DPO) and the evaluation methodologies used to validate them, ideally gained at a frontier model labs Model benchmarking: experience building, running, or maintaining benchmark suites used to track and compare frontier model capabilities across training runs and releases. Data quality: experience with the data quality side of model training — curation, filtering, deduplication, and quality scoring of pretraining and post-training datasets.

CheggChegg is a student-focused online learning company whose Busuu brand offers AI-powered language courses with feedback from a community of native speakers.

Software Engineer, Machine Learning

12 days ago

salary not disclosedopen to WorldwideFull-time

Python · SQL

5 years of experience, Production ML model deployment, Python, SQL, Real-time data pipelines, Ad tech experience, Big data solutions, Model evaluation and optimization, Cross-functional collaboration, Experimentation frameworks, Kedro, PyTorch, Databricks

MNTNMNTN provides performance advertising software for Connected TV, enabling brands to run direct-response campaigns effectively.

Principal Machine Learning Systems Engineer, Search Platform

28 days ago

salary not disclosedopen to IndiaPrincipalNot specified

10+ years production ML systems, search/IR/NLP/LLMs, embeddings and indexing, distributed data systems

AtlassianAtlassian is a Sydney-based B2B software company specializing in collaboration tools for software development and project management, serving a global market.