Felix

Machine Learning Data Engineer (LLM & Generative AI)

Reads like a ML Engineer role: putting models into production.

New York

Actively hiring, first seen by us 11 days ago

Pay: Pay not listedSimilar roles state $166K–$240K
Work setting: Not stated
Employment: Full-time — employment source
Employment type in the employer’s posting: FULL_TIME
From the employer’s posting

What you’ll work on

Full posting

We are seeking an innovative and highly technical Machine Learning Data Engineer to build the foundational data infrastructure that powers our Large Language Models (LLMs) and autonomous AI agents.

Build automated pipelines to extract, clean, and format data for LLM fine-tuning (SFT, RLHF, DPO)
Build telemetry systems to measure latency and accuracy of live LLMs and AI agents
Architect synthetic data generation workflows using foundation models
Implement data quality checks, deduplication, and toxicity filtering
Manage and optimize data flows into vector databases for RAG

From the employer’s posting

We are seeking an innovative and highly technical Machine Learning Data Engineer to build the foundational data infrastructure that powers our Large Language Models (LLMs) and autonomous AI agents. In this role, you will sit at the bleeding edge of generative AI, focusing on the end-to-end data lifecycle—from orchestrating real-time telemetry to architecting the pipelines that train and refine our models. You will be responsible for ensuring our models are fed with high-quality, diverse, and well-structured data to drive continuous performance improvements.

Design, build, and maintain highly scalable, automated data pipelines to extract, clean, and format massive datasets specifically for LLM fine-tuning (Supervised Fine-Tuning, RLHF, and DPO)

Develop robust telemetry and sampling systems to ingest real-time production data

Architect and scale synthetic data generation workflows

Implement automated data quality checks, deduplication strategies, and toxicity filtering

Manage and optimize data flows into vector databases to support Retrieval-Augmented Generation (RAG) capabilities for our agentic workflows

What you’ll bring

All qualifications

Core experience

Experience: 5+ years of professional experience in Data Engineering, Machine Learning Engineering, or MLOps.
Familiarity with vector search and vector databases (e.g., Pinecone, Milvus, Weaviate, Qdrant).
Experience with LLM evaluation frameworks (e.g., RAGAS, TruLens, DeepEval) and building automated LLM benchmarking suites.
Understanding of privacy-preserving techniques (PII redaction pipelines) for handling sensitive data in ML training.
Experience deploying streaming data pipelines (Kafka, Flink, or Kinesis) for real-time analytics.
Experience deploying streaming data pipelines (Kafka, Flink, or Kinesis) not just for analytics, but to feed real-time contextual data into our agentic workflows and streaming RAG systems.

Qualification wording

Experience: 5+ years of professional experience in Data Engineering, Machine Learning Engineering, or MLOps.

Familiarity with vector search and vector databases (e.g., Pinecone, Milvus, Weaviate, Qdrant).

Experience with LLM evaluation frameworks (e.g., RAGAS, TruLens, DeepEval) and building automated LLM benchmarking suites.

Understanding of privacy-preserving techniques (PII redaction pipelines) for handling sensitive data in ML training.

Experience deploying streaming data pipelines (Kafka, Flink, or Kinesis) for real-time analytics.

Experience deploying streaming data pipelines (Kafka, Flink, or Kinesis) not just for analytics, but to feed real-time contextual data into our agentic workflows and streaming RAG systems.

Education & alternatives

Requirements - Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related technical field. - Experience: 5+ years of professional experience in Data Engineering, Machine Learning Engineering, or MLOps.

Benefits in the posting

Full benefits wording

Competitive salary
Initial stock options grant
Annual performance bonus
Unlimited PTO
Paid parental leave
Health, dental, and vision plans
401(k) with employer matching, for US-based employees
Continuous learning opportunities
Empowering opportunities for growth in a dynamic entrepreneurial environment

From the employer’s posting.

About Felix

At Félix, we are building the indispensable financial companion for Latinos in the US.

In the employer’s words · Read in context

Apply at Felix

Posting record

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Actively hiringFirst seen by us 11 days ago, still listed, and not a repost of an earlier posting.

Status in our records: Live
First seen by us: Oct 23, 202611 days ago
Recorded sightings: 21
Last seen by us: Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Pay

$166K–$240K is typical

The middle half of what similar US postings state. This one doesn’t list pay.

From 1,279 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 11

Similar jobs usually come down between day 6 and day 69.

From 4,284 similar postings that came down. Coming down is not always a hire.

Felix is hiring

11 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Hiring at a steady pace
Takes jobs down at a usual pace

Hiring record

See how this role fits your experience

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Python
SQL
GCP
Kafka
Hugging Face

Where the posting names them

- Experience: 5+ years of professional experience in Data Engineering, Machine Learning Engineering, or MLOps. - Programming Languages: Expert-level proficiency in Python and advanced SQL. - Generative AI & LLMs: Demonstrated hands-on experience working with LLM APIs (OpenAI, Anthropic, etc.), open-source models (Hugging Face, Llama), and orchestration frameworks (LangChain, LlamaIndex, or AutoGen).

- Data Orchestration: Strong experience with workflow orchestration tools. - Cloud & Big Data: Experience with scalable cloud infrastructure (GCP) and distributed data processing frameworks. - These are the applicable requirements, although equivalent competencies in any of the above will also be considered.

- Understanding of privacy-preserving techniques (PII redaction pipelines) for handling sensitive data in ML training. - Experience deploying streaming data pipelines (Kafka, Flink, or Kinesis) for real-time analytics. - Bilingual (English/Spanish)

- Bilingual (English/Spanish) - Experience deploying streaming data pipelines (Kafka, Flink, or Kinesis) not just for analytics, but to feed real-time contextual data into our agentic workflows and streaming RAG systems. What We Offer

- Programming Languages: Expert-level proficiency in Python and advanced SQL. - Generative AI & LLMs: Demonstrated hands-on experience working with LLM APIs (OpenAI, Anthropic, etc.), open-source models (Hugging Face, Llama), and orchestration frameworks (LangChain, LlamaIndex, or AutoGen). - Synthetic Data: Proven experience employing prompt engineering and programmatic techniques for high-quality synthetic data generation and validation.

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Job description

View original posting ↗

About Us

At Félix, we are building the indispensable financial companion for Latinos in the US. We combine an AI-powered, conversational-first interface with real-time financial infrastructure to make cross-border money movement as easy as sending a text. Starting with fast, affordable remittances powered by AI and crypto rails, we are expanding into credit, savings, and wallet services to support the complete immigrant financial journey. Our ambition is to deliver a white-glove financial experience with the simplicity of a conversation to a community the traditional financial system has historically overlooked.

We are a hyper-growth Series C company, backed by over $300 million in funding from top-tier global investors, including Andreessen Horowitz, QED, Castle Island, Switch Ventures, HTwenty, Monashees, General Catalyst Customer Value Fund. This isn't just about the numbers; it's a testament to the trust our investors have in our vision and our team. Additionally, Félix was selected as an “Endeavor Entrepreneur” and was a recipient of the CrossTech Fintech Startups Award.

Joining Félix means you will be part of a team building a legacy, a company that will outlive us all. This is a rare opportunity to apply your skills to a deeply meaningful mission—serving a community that has been underserved for too long because we are obsessed with our customers. We get things done with urgency and focus, driven by extreme ownership over our impact. We collaborate without ego, fostering radical transparency and fierce loyalty so we can grow together. Because we aim for insanely great rather than just good enough, we stay insatiably curious—always experimenting, building the future today, and delivering a product that truly makes our users' lives better.

About the Role

We are seeking an innovative and highly technical Machine Learning Data Engineer to build the foundational data infrastructure that powers our Large Language Models (LLMs) and autonomous AI agents. In this role, you will sit at the bleeding edge of generative AI, focusing on the end-to-end data lifecycle—from orchestrating real-time telemetry to architecting the pipelines that train and refine our models. You will be responsible for ensuring our models are fed with high-quality, diverse, and well-structured data to drive continuous performance improvements.

Responsibilities

Automated Fine-Tuning Pipelines: Design, build, and maintain highly scalable, automated data pipelines to extract, clean, and format massive datasets specifically for LLM fine-tuning (Supervised Fine-Tuning, RLHF, and DPO).
Real-Time Agent & LLM Evaluation: Develop robust telemetry and sampling systems to ingest real-time production data. Use this data to continuously measure, evaluate, and benchmark the real-time performance, latency, and accuracy of live LLMs and AI agents.
Synthetic Data Generation: Architect and scale synthetic data generation workflows. Leverage foundational models to create high-variance synthetic datasets to augment training data, cover edge cases, and mitigate bias where real-world data is scarce or privacy-restricted.
Data Quality & Curation: Implement automated data quality checks, deduplication strategies, and toxicity filtering to ensure the highest standard of data integrity entering the model training pipelines.
Infrastructure & MLOps Integration: Partner closely with ML Researchers and DevOps to integrate data pipelines with model training infrastructure, ensuring seamless continuous integration and continuous deployment (CI/CD) for model updates.
Vector Database Management: Manage and optimize data flows into vector databases to support Retrieval-Augmented Generation (RAG) capabilities for our agentic workflows.

Requirements

Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related technical field.
Experience: 5+ years of professional experience in Data Engineering, Machine Learning Engineering, or MLOps.
Programming Languages: Expert-level proficiency in Python and advanced SQL.
Generative AI & LLMs: Demonstrated hands-on experience working with LLM APIs (OpenAI, Anthropic, etc.), open-source models (Hugging Face, Llama), and orchestration frameworks (LangChain, LlamaIndex, or AutoGen).
Synthetic Data: Proven experience employing prompt engineering and programmatic techniques for high-quality synthetic data generation and validation.
Data Orchestration: Strong experience with workflow orchestration tools.
Cloud & Big Data: Experience with scalable cloud infrastructure (GCP) and distributed data processing frameworks.
These are the applicable requirements, although equivalent competencies in any of the above will also be considered.

Nice to Have

Familiarity with vector search and vector databases (e.g., Pinecone, Milvus, Weaviate, Qdrant).
Experience with LLM evaluation frameworks (e.g., RAGAS, TruLens, DeepEval) and building automated LLM benchmarking suites.
Understanding of privacy-preserving techniques (PII redaction pipelines) for handling sensitive data in ML training.
Experience deploying streaming data pipelines (Kafka, Flink, or Kinesis) for real-time analytics.
Bilingual (English/Spanish)
Experience deploying streaming data pipelines (Kafka, Flink, or Kinesis) not just for analytics, but to feed real-time contextual data into our agentic workflows and streaming RAG systems.

What We Offer

Competitive salary
Initial stock options grant
Annual performance bonus
Unlimited PTO
Paid parental leave
Work model: currently fully remote, with a future transition to a hybrid setup
Health, dental, and vision plans
401(k) with employer matching, for US-based employees
Continuous learning opportunities
Empowering opportunities for growth in a dynamic entrepreneurial environment

Equal Opportunity Employer

At Félix, we are committed to providing equal employment opportunities to all qualified employees and applicants without regard to race, religion, nationality, sex, sexual orientation, gender identity, age, or disability. This policy applies to all terms and conditions of employment, including recruitment, hiring, placement, promotion, training, compensation, benefits, and termination.

Want to learn more about our privacy practices? Check out our Privacy Policy.

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