Lead ML Data Engineer, LLM Infra & Synthetic Data

Félix; New York (NY); Remote; USD 150,000 - 230,000; Full time

Benefits offered by this job

Unlimited PTO

Stock options grant

Annual performance bonus

Paid parental leave

401(k) with employer matching (US)

Job summary

Félix is hiring a highly technical Machine Learning Data Engineer to build data infrastructure powering LLMs and autonomous AI agents. You will orchestrate telemetry, design data pipelines, and ensure high-quality data feeds for continuous model improvement.

You’ll implement synthetic data generation, data quality checks, and integration with model training infra. Fully remote with a future hybrid path, competitive comp & stock options.

Qualifications

• Education as listed: Bachelor’s or Master’s in CS, DS, engineering or related field.
• 5+ years in Data Engineering, ML Engineering, or MLOps.
• Expert Python and strong SQL skills.
• Hands-on with LLM APIs (OpenAI/Anthropic) and open-source models.
• Experience with prompt engineering and synthetic data generation.
• Strong data orchestration experience and cloud/big data (GCP) exposure.

Responsibilities

• Build scalable automated data pipelines for LLM fine-tuning (SFT/RLHF/DPO).
• Develop telemetry, sampling, and evaluation for real-time LLMs and agents.
• Scale synthetic data workflows to augment training data and reduce bias.
• Implement data quality checks, deduplication and toxicity filtering.
• Integrate data pipelines with CI/CD for model updates with DevOps.
• Manage data flows into vector databases to support RAG workloads.

Skills

Python

SQL

LLM APIs

OpenAI

Anthropic

Hugging Face

LangChain

LlamaIndex

AutoGen

Prompt engineering

Education

Bachelor's or Master's degree in Computer Science / Data Science / Engineering

Tools

LangChain

LlamaIndex

AutoGen

Hugging Face

Kafka

Flink

Kinesis

Job description

Félix is hiring a highly technical Machine Learning Data Engineer to build data infrastructure powering LLMs and autonomous AI agents. You will orchestrate telemetry, design data pipelines, and ensure high-quality data feeds for continuous model improvement.

You’ll implement synthetic data generation, data quality checks, and integration with model training infra. Fully remote with a future hybrid path, competitive comp & stock options.