Senior Applied ML Scientist – Search & Recommendation

We are looking for a senior applied ML scientist to join our team. Get in touch!

Location: Helsinki Onsite (Preferable), Finland & EU Remote / Hybrid

The Opportunity

Comparables.ai builds the intelligence infrastructure for financial decision-making.

We operate a dataset covering 350M+ companies and power company discovery, peer identification, valuation benchmarking, and market research workflows. Search and recommendation quality are core to our product.

We’re hiring a Senior Applied ML Scientist to design, fine-tune, and scale high-performance hybrid retrieval and ranking systems.

What You’ll Work On

Design and optimize hybrid search systems combining:
- Lexical retrieval
- Sparse representations
- Dense embedding-based retrieval
Build and deploy LLM-based retrieval and ranking models .
Fine-tune transformer and LLM architectures for:
- Retrieval & Ranking tasks
- Financial domain adaptation
Develop novel LLM-based retrieval & ranking approaches , beyond off-the-shelf embeddings.
Design recommendation strategies for:
- Company similarity
- Valuation comparables
- Industry benchmarking
- Market intelligence workflows
Improve embedding representations for structured and unstructured financial data.
Define and optimize relevance metrics ( , , NDCG) tied directly to product outcomes.
Run structured offline evaluations and online experiments.
Balance quality, latency, and cost in large-scale production systems.
Collaborate closely with backend and AI engineers to productionize models reliably.

What We’re Looking For

5+ years of applied ML experience in production search, ranking, or recommendation systems.
Strong expertise in:
- Hybrid retrieval architectures
- Sparse and dense retrieval systems
- Transformer-based and LLM-based retrieval and ranking tasks
- Fine-tuning LLMs for retrieval and ranking tasks
Experience building or adapting LLMs for domain-specific applications.
Experience working with large-scale textual datasets.Strong Python skills and hands-on experience with PyTorch, HuggingFace, Sentence-Transformers, PEFT (LoRA), Tevatron, FlagEmbedding
Proven experience shipping ML systems that impact user-facing metrics.
Solid understanding of system constraints:
- Scalability
- Indexing strategies
- Inference latency
- Cost optimization

Strong Plus

Experience in financial or structured knowledge domains.
Experience with Elasticsearch and vector databases (Weaviate, Milvus, Pinecone, Vespa).
Experience building custom cross-encoders or re-ranking models.
Experience running large-scale A/B experiments.

Why This Role Matters

Search and recommendation quality directly determine the value of our product. You will shape how financial professionals discover companies, benchmark valuations, and analyze markets — at global scale.

Massive proprietary dataset (350M+ companies)
High-impact ML problems in a real-world financial domain
Strong technical ownership
Strong growth opportunity in a rapidly scaling AI SaaS company.

Ready to apply?

Send your resume and a brief introduction telling us why you'reexcited about this role.

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