Hybrid Dense + Sparse Search & Constraint Parsing
Combining BM25 lexical retrieval with domain-tuned vector embeddings and deterministic numeric/unit/negation parsers across Elasticsearch, OpenSearch, Vespa, Algolia, or our Talos engine.
Senior Search, Recommendation & Applied ML Engineers—delivering fixed-scope systems or embedded with your team
Built for CTOs, VPs of Engineering, and Heads of ML at $20M–$250M commerce, marketplace, and B2B catalog companies. Ship hybrid retrieval, Learning-to-Rank, and offline/online evaluation harnesses without a 6-month recruiting cycle.
Core Engineering Domain
We do not build generic wrappers. We engineer retrieval, ranking, and evaluation.
Search and recommendations share the same mathematical foundation: structured attribute extraction, dense/sparse candidate generation, hard constraint filtering, Learning-to-Rank, and rigorous offline/online measurement.
Learning-to-Rank (LTR) & Cross-Encoder Reranking
Second-stage rerankers trained on click, cart, and purchase logs that balance semantic relevance, inventory depth, margin, and cold-start exploration within <120ms p95 latency.
Two-Tower & Graph Recommendation Pipelines
Substitute retrieval, complete-the-look compatibility graphs, and real-time session-based cart ranking that eliminate out-of-stock and incompatible-spec recommendations.
Evaluation Harnesses & CI/CD Metric Gates
Golden query/intent sets (200–1,000 queries), LLM-as-judge calibrated against human raters, offline NDCG@10 / MRR / Constraint Recall regression gates, and online interleaving / A/B harnesses.
Tier 2 & Tier 3 Engagement Models
Transparent rates across fixed-scope builds, fractional principals, and resident engineers
Choose a fixed-deliverable system build (Tier 2) or embed senior retrieval and ML engineers directly into your sprint rituals (Tier 3).
| Engagement Model | Rate / Price (USD) | Term / Timeline | What We Deliver |
|---|---|---|---|
| Evaluation Harness & Experimentation Setup (Tier 2) | $8,000–$18,000 | 2–4 weeks | Golden query/intent sets (200–1,000 queries), automated NDCG@10 / MRR / Constraint Recall CI suite, LLM-as-judge calibration, and A/B interleaving setup |
| Semantic Search or Recsys Build (Tier 2) | $12,000–$35,000 build (+ $1,500–$3,500/mo hosted) · $15,000–$40,000 | 4–8 weeks | Turnkey hybrid retrieval/reranking search layer or two-tower + LTR recommendation pipeline with before/after evaluation. |
| Fractional Principal ML / Search Architect (Tier 3) | $4,000–$8,000/mo | 3-month minimum (15–30 hrs/mo) | Architecture design, evaluation review, model/prompt critique, search/recsys vendor governance, and mentoring in-house engineers |
| Resident AI / ML Engineer (Tier 3) | $16,000–$26,000/mo FT ($100–$165/hr) or $9,000–$14,000/mo HT | 3-month min · Billed monthly upfront (Net-7) | Senior Search, Recsys or Applied ML Engineer embedded in your Slack/GitHub/sprints, supervised weekly by a RankTalos Principal Architect. Billed monthly upfront (Net-7); 20% first-year comp conversion buyout option after 6 months. |
| Embedded Delivery Pod (Tier 3) | $35,000–$55,000/mo | 8–12 week delivery sprints | 1 Fractional Principal Architect + 1–2 Senior ML Engineers executing an end-to-end search, recommendation, or ranking roadmap |
Principal-Supervised Residency
How Resident AI/ML Engineers work inside your team
We never body-shop unmanaged freelancers. Every Resident Engineer is paired with a RankTalos Principal Architect who reviews system design, PRs, and offline/online experiment readouts every week.
- Principal Supervision Included
- Every Resident Engineer includes weekly architecture and code review (2 hrs/week) from a RankTalos Principal Architect, plus access to our internal evaluation harnesses and retrieval libraries.
- Onboarding Lead Time
- 2–3 weeks to match a vetted Senior Search, Recsys, or Applied ML Engineer to your cloud, vector DB, and search stack.
- Billing & Term
- 3-month minimum commitment, invoiced monthly upfront (Net-7) or with a 1-month refundable deposit—no open-ended staffing markups.
- Contract-to-Hire Buyout Option
- After 6 months of continuous placement, you may convert a Resident Engineer to a full-time employee for a 20% conversion fee on first-year base compensation.
- Clean-Room IP Assignment
- All client-specific models, ranking pipelines, and enriched catalog schemas (Foreground IP) assign 100% to your company upon payment.
ML engineering & residency questions
Why hire a RankTalos Resident ML Engineer instead of using Toptal or a generic staffing agency? Generalist staffing marketplaces send unsupervised freelancers who spend 6–8 weeks figuring out how to evaluate search or recommendations. Our Resident Engineers are domain specialists in Information Retrieval and Recommender Systems, arrive with our pre-built NDCG/constraint evaluation harness, and are supervised weekly by a RankTalos Principal Architect.
Can we start with an Evaluation Harness or Architecture Sprint before embedding an engineer? Yes—most VPs of Engineering start with either a $8,000–$18,000 Evaluation Harness Setup (2–4 weeks) or a Fractional Principal Retainer ($4,000–$8,000/mo), then embed a Resident Engineer ($16,000–$26,000/mo FT or $9,000–$14,000/mo HT) to execute the roadmap.
How does the 6-month contract-to-hire buyout work? Recruiting a Staff/Senior Search or Recsys ML Engineer takes 5–6 months and a 25–30% executive search fee on day one without seeing them write a line of production code. With RankTalos, you work with the engineer in your Slack and GitHub for 6 months first, and can convert them full-time for 20% of first-year base compensation.
What stacks do your engineers work in? Python, PyTorch, TypeScript/Node, Go, Rust; Elasticsearch/OpenSearch, Vespa, Qdrant, pgvector, Milvus; Algolia, Constructor, Vertex AI Search; Snowflake, Databricks, BigQuery, dbt, and Airflow.