Applied ML Engineer (AI)

Location: Remote roles are available in Singapore, Hong Kong, and the UK; onsite options are Austin and San Francisco.

Company

hirify.global builds products and engineering systems for evaluating, verifying, and understanding machine learning models.

What you will do

• Reproduce and evaluate model-provenance and verification methods using open-weight and API-accessible models.
• Design evaluation datasets, probes, scoring methods, baselines, calibration tests, experiment harnesses, and reporting systems.
• Work with model weights, logits, hidden states, activations, model APIs, and inference infrastructure.
• Build evaluation infrastructure with runners, judges, persistence, experiment orchestration, and reports.
• Turn research workflows into product experiences covering experiment configuration, runs, traces, comparisons, and review workflows.
• Ship production-quality APIs, background jobs, observability, tests, documentation, and verification workflows for Construct and Eldros.

Requirements

• Strong Python engineering skills with hands-on experience in PyTorch and Hugging Face Transformers.
• Strong understanding of ML evaluation, including dataset design, metrics, calibration, false positives and negatives, statistical uncertainty, and reproducibility.
• Ability to read research papers and implement methods from first principles.
• Production software experience with APIs, asynchronous jobs, databases, logging, testing, and deployment.
• Understanding of open-weight models and modern LLM inference systems, with the ability to work across backend and frontend boundaries.
• Ability to work with React and TypeScript product interfaces, exercise strong technical judgment, and operate with ownership in a fast-moving startup.

Nice to have

• Experience with model provenance, fingerprinting, watermarking, distillation detection, red-teaming, safety evaluations, or interpretability.
• Experience with activation and representation analysis, probing, model hooks, logits, or hidden states.
• Experience with DSPy, LiteLLM, Temporal, Ray, vLLM, PostgreSQL, pgvector, Next.js, data visualization, or experiment dashboards.
• Experience serving open-weight models on GPUs and analyzing latency, throughput, memory, precision, and cost trade-offs.
• Experience designing adversarial evaluations or testing systems against deliberate evasion.

Culture & Benefits

• Work spans research, experimentation, engineering, and product rather than a single technical layer.
• Priorities can evolve quickly, with individuals expected to identify problems, propose solutions, and drive work forward independently.
• The role emphasizes measured evidence, reproducibility, clear technical reports, and systems that produce trustworthy results.
• Employment is full time with a remote work arrangement available in specified APAC and EMEA locations.