Remote - APAC ( Singapore ) is the location for the Applied ML Engineer position at sentient, offering an end-to-end engineering career for professionals who enjoy translating machine learning research into reliable production systems. This role suits engineers experienced in Python, PyTorch, and modern inference infrastructure who want to bridge the gap between experimental research and user-facing products.
Role Overview
The Applied ML Engineer position at sentient operates entirely remotely within the APAC region, centered around Singapore. The professional in this role connects machine learning research with production software by designing experiments, evaluating models, and building reliable systems for users.
Key Responsibilities
Reproduce and evaluate research methods using open-weight and API-accessible models.
Design evaluation datasets, scoring methods, probes, baselines, calibration tests, and experiment harnesses.
Interact directly with model weights, logits, hidden states, activations, model APIs, and inference infrastructure.
Build and extend evaluation infrastructure such as runners, judges, persistence, reporting, and experiment orchestration.
Convert research workflows into product experiences that feature experiment configuration, runs, traces, comparisons, reports, and review workflows.
Investigate how verification methods perform under model modifications like fine-tuning, merging, quantization, distillation, safety removal, and evasion.
Design controlled experiments that separate meaningful signals from confounders and artifacts.
Write technical reports distinguishing measured evidence, interpretations, and hypotheses.
Ship production-quality systems equipped with APIs, background jobs, observability, testing, and documentation.
Requirements
Strong Python engineering skills paired with hands-on experience using PyTorch and Hugging Face Transformers.
Solid understanding of machine learning evaluation, covering dataset design, baselines, metrics, calibration, false positives and negatives, statistical uncertainty, and reproducibility.
Ability to read machine learning research papers and implement methods directly from first principles.
Experience developing production software beyond notebooks, including APIs, asynchronous jobs, databases, logging, testing, and deployment.
Familiarity with open-weight models and the operational mechanisms of modern LLM inference systems.
Capability to work across backend and frontend boundaries, particularly utilizing React and TypeScript.
Strong technical judgment regarding experimental evidence and high agency with a strong sense of ownership.
Nice to Have
Background in model provenance, fingerprinting, watermarking, distillation detection, red-teaming, safety evaluations, or interpretability.
Experience with activation and representation analysis, probing, model hooks, logits, hidden states, or other model-internals work.
Familiarity with evaluation and inference infrastructure tools like DSPy, LiteLLM, Temporal, Ray, vLLM, and PostgreSQL/pgvector.
Experience with Next.js, React, TypeScript, data visualization, or experiment dashboards.
Practice running and serving open-weight models on GPUs while reasoning about latency, throughput, memory, precision, and cost tradeoffs.
Experience designing adversarial evaluations or testing systems against deliberate evasion attempts.
Skills and Tools
Python
PyTorch
Hugging Face Transformers
React
TypeScript
Next.js
DSPy
LiteLLM
Temporal
Ray
vLLM
PostgreSQL/pgvector
About the Company
sentient operates as a fast-moving startup environment where priorities evolve rapidly and team members function across multiple domains.
How to Apply
Interested candidates should apply on the official company careers page.
