Machine Learning Engineer
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Machine Learning Engineer (Data & AI)
Remote
US Time Zones (EST–PST)
Role Overview
We are looking for a skilled Machine Learning Engineer with a strong data engineering foundation to build, train, and deploy ML models and data pipelines across a range of complex environments. This role sits at the intersection of data and AI — you will be responsible for everything from sourcing, cleaning, and structuring data to training models, evaluating performance, and getting solutions into production. The ideal candidate thinks rigorously about data quality, understands the full ML lifecycle, and is equally comfortable working with large datasets as they are fine-tuning models or building scalable inference pipelines.
Key Responsibilities
Design, build, and maintain robust data pipelines for ingestion, transformation, and feature engineering
Develop, train, evaluate, and iterate on machine learning models across classification, regression, clustering, and NLP tasks
Fine-tune and adapt pre-trained LLMs and foundation models for specific use cases and datasets
Build and manage MLOps infrastructure including model versioning, experiment tracking, and deployment pipelines
Work with structured and unstructured data at scale — including text, tabular, and time-series data
Monitor model performance in production and implement retraining and drift-detection strategies
Collaborate with engineering and product teams to translate data insights into actionable AI features
Document data schemas, model architectures, and pipeline logic clearly and thoroughly
Required Qualifications
Strong Python skills with hands-on experience in core ML libraries (scikit-learn, PyTorch, TensorFlow, or similar)
Solid data engineering experience — SQL, ETL pipelines, and working with large-scale datasets
Practical experience with model training, evaluation, hyperparameter tuning, and deployment
Familiarity with LLMs and transformer-based architectures; experience with fine-tuning or prompt engineering in production contexts
Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, DVC, or similar)
Strong grasp of statistical concepts, data quality principles, and model performance metrics
Must have prior remote work experience, be fluent with remote collaboration tools and platforms (such as Slack, Zoom, Google Workspace, Asana, or similar), and have ideally worked with US or UK-based companies. Applications without this experience will not be considered.
Preferred Qualifications
Experience with distributed data processing frameworks (Spark, Dask, or similar)
Familiarity with vector databases and embedding-based retrieval systems
Background working with real-time or streaming data pipelines (Kafka, Flink, or similar)
Exposure to cloud-native ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML)
Experience with data governance, lineage tracking, or compliance-aware data workflows
Tools & Technology
Python, SQL, and core ML/data libraries (PyTorch, scikit-learn, Pandas, NumPy)
MLOps: MLflow, Weights & Biases, DVC, or equivalent
Data warehouses and lakes: Snowflake, BigQuery, Redshift, or similar
LLM platforms: Hugging Face, OpenAI, Anthropic, or similar
Cloud infrastructure: AWS, GCP, or Azure
Google Workspace, Slack, Zoom, and remote collaboration tools
Please note: It is crucial that you complete the application form in full. As part of the application process, you will be required to record a video. If your application is successful, you will receive an email confirming next steps — the video is the first step of the interview process. If you do not record a video, we will not be able to consider you for ANY open roles.
We connect top talent with vetted employers, competitive pay, and real growth opportunities.
Machine Learning Engineer (Data & AI)
Remote
US Time Zones (EST–PST)
Role Overview
We are looking for a skilled Machine Learning Engineer with a strong data engineering foundation to build, train, and deploy ML models and data pipelines across a range of complex environments. This role sits at the intersection of data and AI — you will be responsible for everything from sourcing, cleaning, and structuring data to training models, evaluating performance, and getting solutions into production. The ideal candidate thinks rigorously about data quality, understands the full ML lifecycle, and is equally comfortable working with large datasets as they are fine-tuning models or building scalable inference pipelines.
Key Responsibilities
Design, build, and maintain robust data pipelines for ingestion, transformation, and feature engineering
Develop, train, evaluate, and iterate on machine learning models across classification, regression, clustering, and NLP tasks
Fine-tune and adapt pre-trained LLMs and foundation models for specific use cases and datasets
Build and manage MLOps infrastructure including model versioning, experiment tracking, and deployment pipelines
Work with structured and unstructured data at scale — including text, tabular, and time-series data
Monitor model performance in production and implement retraining and drift-detection strategies
Collaborate with engineering and product teams to translate data insights into actionable AI features
Document data schemas, model architectures, and pipeline logic clearly and thoroughly
Required Qualifications
Strong Python skills with hands-on experience in core ML libraries (scikit-learn, PyTorch, TensorFlow, or similar)
Solid data engineering experience — SQL, ETL pipelines, and working with large-scale datasets
Practical experience with model training, evaluation, hyperparameter tuning, and deployment
Familiarity with LLMs and transformer-based architectures; experience with fine-tuning or prompt engineering in production contexts
Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, DVC, or similar)
Strong grasp of statistical concepts, data quality principles, and model performance metrics
Must have prior remote work experience, be fluent with remote collaboration tools and platforms (such as Slack, Zoom, Google Workspace, Asana, or similar), and have ideally worked with US or UK-based companies. Applications without this experience will not be considered.
Preferred Qualifications
Experience with distributed data processing frameworks (Spark, Dask, or similar)
Familiarity with vector databases and embedding-based retrieval systems
Background working with real-time or streaming data pipelines (Kafka, Flink, or similar)
Exposure to cloud-native ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML)
Experience with data governance, lineage tracking, or compliance-aware data workflows
Tools & Technology
Python, SQL, and core ML/data libraries (PyTorch, scikit-learn, Pandas, NumPy)
MLOps: MLflow, Weights & Biases, DVC, or equivalent
Data warehouses and lakes: Snowflake, BigQuery, Redshift, or similar
LLM platforms: Hugging Face, OpenAI, Anthropic, or similar
Cloud infrastructure: AWS, GCP, or Azure
Google Workspace, Slack, Zoom, and remote collaboration tools
Please note: It is crucial that you complete the application form in full. As part of the application process, you will be required to record a video. If your application is successful, you will receive an email confirming next steps — the video is the first step of the interview process. If you do not record a video, we will not be able to consider you for ANY open roles.
We connect top talent with vetted employers, competitive pay, and real growth opportunities.