(Mid-level role | 2–5 years' experience)

About the firm

January Capital is an established venture capital manager headquartered in Singapore.

At January

Capital, we seek to invest in technology businesses that will power growth in the Asia-Pacific region over the coming decades. We're inspired by those individuals who are willing to dedicate themselves to solving some of the region's biggest challenges and challenge the status quo. Today, we are proud to have partnered with more than 60 exceptional companies, including Tazapay, Great Question, Skedulo, GO1, Akulaku, Shopback, Marqo and aCommerce. We manage more than US$450 million of assets on behalf of our investors, who comprise leading institutions, foundations, family offices and individuals.

About the role

Data is at the core of everything we do at January Capital. We are building a continuously updating world model of ambitious people and the companies they create—a temporal graph that spans careers, not just current roles. The core thesis is that people are more persistent and predictive than the companies they found.

As a Machine Learning Engineer, you'll build and operate the model infrastructure behind scoring and signal detection across our product surfaces—from entity resolution and embeddings, to the signal-scoring models that power our sourcing engine, to serving all of it reliably in production. You'll work closely with our Data Scientists, who develop model logic and features, and our Data Engineer, who owns pipelines and the warehouse, to turn research and prototypes into deployed, monitored systems the investment team can rely on daily.

Working closely with the Data Science team and the Data Engineer, you'll contribute across three key areas:

Model infrastructure

- Design and maintain the model serving layer, training pipelines,



feature stores and inference APIs that power scoring and signal detection across the graph.

Build the infrastructure for entity resolution and embedding generation that feeds the identity graph, and own model deployment end-to-end, including versioning, monitoring, rollback, and latency/throughput SLAs. Signal & scoring models – Build and productionize models that score signal strength and help First Signal surface the "moment of maximum signal." Partner with Data Scientists to move analysis and prototypes into robust, tested production code.

Platform & reliability – Instrument model performance, drift detection and data quality checks in production. Collaborate with the Data Engineer on schema and pipeline decisions that affect model inputs, and contribute to broader engineering infrastructure choices—the team is small, so scope isn't strictly siloed.

You'll have ownership of the model infrastructure for a proprietary, production temporal graph—not a side project. You'll work directly with a small, high-context Data Science team and the founder, gain exposure to how a venture fund's investment process is powered by data end-to-end, and receive a competitive salary and equity in a product built to become a standalone company.

What we are looking for

Required

- 2–5 years as an ML Engineer or Backend Engineer who has shipped ML systems into production, not just notebooks.
- Robust Python engineering skills; comfortable owning a service end-to-end.
- Experience across the ML deployment lifecycle: training pipelines, model serving, monitoring, and rollback.
- Comfortable with SQL and general data pipeline concepts.
- Bias for shipping a working system over perfecting the architecture first.

Nice to have

- Experience with graph-based ML—embeddings, GNNs, entity resolution at scale.
- Familiarity with NLP-enriched data pipelines or vector search.
- Prior experience at an early-stage startup or small team, comfortable operating without heavy process.

📌 Machine Learning Engineer (India)
🏢 January Capital
📍 India