Machine Learning Engineer (LLM, NLP, AI)

We’re now looking for a Machine Learning Engineer with deep expertise in Natural Language Processing (NLP) and Large Language Models (LLMs). This is a hybrid role that blends custom model development with LLM API integration to ship intelligent, production-ready features. You’ll work across the full lifecycle—from preparing training data and fine-tuning models, to designing retrieval pipelines and deploying performant inference systems in the cloud.

Variant Products

• resume.co Live since May 2023, ±1M MAU

• pdf.net Live since Dec 2024, ±400k MAU

• contracts.net Live since January 2023, ±100k MAU

• mealplan.co – Summer 2025

What You’ll Do

• Integrate hosted LLM APIs (e.g. OpenAI, Anthropic) + custom models to support intelligent in-product behavior.

• Build and fine-tune transformer models using PyTorch, HuggingFace

• Design and deploy retrieval-augmented generation (RAG) pipelines with vector databases (e.g., pgvector) and graph-based reasoning (e.g., Neo4j).

• Develop scalable inference systems using vLLM, speculative decoding, and optimized serving techniques.

• Build modular, production-grade pipelines for training, evaluation, and deployment.

• Collaborate closely with product, design, and full-stack teams to ship features that bring AI to end users.

• Own infrastructure around Docker, Cloud Run, and GCP, ensuring speed, reliability, and observability.

What You Bring

• Strong Python engineering background with clean, tested, and maintainable code.

• Proven experience building with transformer-based models, including custom training and fine-tuning.

• Deep familiarity with HuggingFace, PyTorch, tokenization, and evaluation frameworks.

• Experience integrating and orchestrating LLM APIs (OpenAI, Anthropic) into user-facing products.

• Understanding of semantic search, vector storage (FAISS, pgvector), and hybrid symbolic-neural approaches.

• Experience designing or consuming graph-based knowledge systems (e.g., Neo4j, property graphs).

• Ability to build and debug scalable training and inference systems.

Bonus Points For

• Hands-on experience with Docker and production deployment on Google Cloud (GKE, Cloud Run).

• Experience with RLHF, reward models, or reinforcement learning for LLM alignment.

• Knowledge of document understanding, OCR, or structured PDF parsing.

• Exposure to monitoring and observability tools (e.g., Prometheus, Grafana, OpenTelemetry).

• Background in linguistics, semantics, or computational reasoning.

Salary & Contract

• Compensation: $60,000 — $100,000/year. Yearly bonus based on profit and performance.

• For the right person, we’re open to crafting a package that goes well beyond — including equity and fast-track opportunities.

• Paid via deel.com (standard contract).

• Asia/Pacific or EU timezone.

How to apply?

1. Apply to vlad@variant.net, include your resume.

2. We'll invite you for a call with our Lead AI Engineer.

3. If we see a potential match, you'll be invited to undertake a small challenge, requiring up to 8 hours of your time.