Applied ML Engineer

Sentient, Remote - APAC ( Singapore ) +4

The role

We’re looking for an Applied ML Engineer to build systems at the intersection of machine learning research and production software.

This is an end-to-end engineering role. You should be comfortable reading a research paper, identifying what is actually testable, building the smallest useful experiment, evaluating it rigorously, and turning the result into a production system that users can interact with.

What you need

What we’re looking for

Strong Python engineering skills and hands-on experience with PyTorch and Hugging Face Transformers.
A strong understanding of ML evaluation, including dataset design, baselines, metrics, calibration, false positives, false negatives, statistical uncertainty, and reproducibility.
Ability to read ML research papers and implement methods from first principles rather than relying entirely on existing packages.
Experience building production software beyond notebooks, including APIs, asynchronous jobs, databases, logging, testing, and deployment.
Comfort working with open-weight models and understanding how modern LLM inference systems operate.
Ability to work across backend and frontend boundaries. Our product surface is primarily React/TypeScript, and you should be able to make complex experiments and results understandable to users.
Strong technical judgment about what experimental evidence does and does not support. For example, evidence that one model was derived from another is not necessarily evidence that it was directly trained on that model's outputs.
High agency and a strong sense of ownership. You are comfortable identifying problems, proposing solutions, and driving work forward without waiting for detailed instructions.
Comfortable working in a fast-moving startup environment where priorities can evolve quickly and individuals are expected to operate across functions.

Experience in any of the following is a plus

Model provenance, fingerprinting, watermarking, distillation detection, red-teaming, safety evaluations, or interpretability.
Activation and representation analysis, probing, model hooks, logits, hidden states, or other model-internals work.
Evaluation and inference infrastructure such as DSPy, LiteLLM, Temporal, Ray, vLLM, PostgreSQL/pgvector, or similar systems.
Next.js, React, TypeScript, data visualization, or experiment dashboards.
Running and serving open-weight models on GPUs and reasoning about latency, throughput, memory, precision, and cost tradeoffs.
Designing adversarial evaluations or testing systems against deliberate attempts to evade detection.

Applied ML Engineer

The role

Applied ML Engineer

The role

What you will do

What you’ll do

You will

What you need

What we’re looking for

Experience in any of the following is a plus

This role is not

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Sentient