Hire ML Engineersin 2 weeks
BearPlex ML engineers build production machine learning systems (feature pipelines, model training, MLOps, online inference) for data-intensive enterprises. We embed engineers into your team in 14 days.
What a ML Engineer actually does at BearPlex
An ML engineer at BearPlex owns the complete production lifecycle of machine learning systems: distinct from LLM engineers (who specialize in language models) and data scientists (who lean toward exploratory analysis). That includes feature engineering pipelines (Airflow, Dagster, dbt), model training and experiment tracking (MLflow, Weights & Biases), online serving infrastructure (BentoML, Seldon, SageMaker), feature stores when warranted (Feast, Tecton), monitoring for data drift and model decay (Evidently, Arize, WhyLabs), and CI/CD for models (testing, validation, blue-green deployment). They work across the full ML stack: classical ML for tabular problems where it still beats LLMs (XGBoost, LightGBM, Random Forests), deep learning for vision and time-series (PyTorch, TensorFlow), and increasingly hybrid systems where classical ML and LLMs work together. Our ML engineers have shipped recommendation systems, fraud detection pipelines, demand forecasting models, computer vision systems, and time-series forecasting at production scale across logistics, financial services, healthcare, and retail.
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Built a fraud detection pipeline processing 50M+ transactions/day with 92% precision and 88% recall: replacing rules-based system that had 70/65.
Owns the demand forecasting system for a Fortune 500 retailer: 14-day SKU-level forecasts driving $200M annual inventory decisions.
Migrated client's recommendation system from offline batch to online inference: 40ms p99 latency, 12% lift in click-through rate.
Built end-to-end MLOps platform on Databricks: model registry, CI/CD, automated retraining triggered by drift, currently powering 23 production models.
Skills matrix
The capabilities every BearPlex ML Engineer brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| Feature engineering & pipelines | Expert | Airflow · Dagster · dbt · Feast |
| Classical ML (XGBoost, LightGBM, scikit-learn) | Expert | XGBoost · LightGBM · scikit-learn · Random Forests |
| Deep learning (PyTorch, TensorFlow) | Expert | PyTorch · PyTorch Lightning · TensorFlow · Hugging Face Transformers |
| Model serving & inference (online + batch) | Expert | BentoML · Seldon Core · SageMaker endpoints · Triton Inference Server |
| MLOps (CI/CD for ML) | Expert | MLflow · Weights & Biases · DVC · GitHub Actions |
| Feature store design | Advanced | Feast · Tecton · Vertex AI Feature Store · Custom on Postgres |
| Model monitoring & drift detection | Advanced | Evidently · Arize · WhyLabs · Custom dashboards |
| Recommendation systems | Advanced | Two-tower retrieval · Wide & Deep · Implicit feedback models · Vector search |
| Time-series forecasting | Advanced | Prophet · DeepAR · Temporal fusion transformers · ARIMA when appropriate |
| Computer vision | Advanced | YOLO family · Detectron2 · Segment Anything Model · Custom CNNs |
| A/B testing & experiment design | Advanced | Statistical power analysis · Bayesian A/B · Multi-armed bandits |
| Production Python & data engineering | Expert | Python 3.11+ · Polars · DuckDB · Spark when needed |
How we vet ML engineers
Technical screen
60-minute call covering production ML experience, system design, and a live debugging exercise on a sanitized BearPlex codebase. We're looking for engineers who can explain why their model failed in production, not just demonstrate it works in a notebook.
Live coding
2-hour paired session building a small ML pipeline from scratch (feature engineering, model training, evaluation harness) with constraints (no scikit-learn pipelines, must handle missing data correctly). We watch for code organization, testing instincts, and architectural judgment.
Systems design
90-minute design session on a production-realistic ML system (e.g., 'design real-time fraud scoring for 100K TPS with sub-50ms p99 latency'). We push on feature freshness, monitoring, retraining strategy, and graceful degradation when models fail.
Reference check + paid trial work
We talk to two prior managers or technical peers. The engineer then completes 1-2 days of paid sample work on a real BearPlex client engagement (with appropriate isolation). Only if all four steps pass do they join the embedded pod.
What clients say
“BearPlex's ML engineer was operating in our codebase like an internal team member by week two. The first model she shipped is still our highest-revenue feature.”
“Most ML hires we've made spent six months getting productive. BearPlex's engineer was shipping production code in week three.”
“We'd had three failed attempts to ship our recommendation system before BearPlex. They got it to production in 90 days, and it's been running for two years.”
Hiring ML engineers: questions answered
Different specialization. ML engineers work across the full ML stack (classical ML, deep learning, time-series, vision) and excel at production ML systems for tabular and structured-data problems. LLM engineers specialize in language model systems (RAG, agents, fine-tuning, evaluation). Both can do each other's work in a pinch, but the deep specialization matters in production. We hire dedicated specialists for each.
Both, with judgment about when each wins. For tabular problems with structured features, XGBoost or LightGBM beats deep learning roughly 80% of the time, and our engineers know this. We don't fashion-chase. Deep learning where it actually wins (vision, sequence modeling, complex temporal patterns); classical ML everywhere else; LLM-augmented hybrid systems where the strengths complement.
Yes: feature engineering and data pipeline work is a core part of production ML. Our ML engineers are competent in Airflow, Dagster, dbt, Spark, and the typical data engineering stack. For pure data engineering work (warehouse modeling, ETL platform building) without ML focus, we recommend our Data Pipelines & MLOps service instead.
14 days from initial intake to embedded. Day 0 is a 60-minute scoping call. Days 1-7 we match an engineer based on your tech stack, domain (FinTech vs healthcare vs retail), and team culture. Days 8-14 the engineer reads your codebase, sets up local dev, attends standups as observer, and starts shipping by end of week 2.
21 days from start. If the engineer isn't a fit during the first 21 days, you don't pay for their time and we replace them with another engineer at no cost. We've had to invoke this twice in 47 placements.
Most BearPlex ML engineering engagements run 6-18 months. The shortest is a single 90-day War Room sprint for a focused build. The longest currently active is 30 months: same engineer, embedded full-time with the client's team.
Yours. We work with whatever you already have: SageMaker, Vertex AI, Databricks, on-prem MLflow, custom platforms. We push back when an architectural choice will hurt you in production, but we're not platform-aligned. For greenfield projects we have opinions, but those are recommendations, not mandates.
Primarily Lahore, Pakistan (HQ) with client-facing presence in Austin and Doha. Time zone overlap with US clients is 5-9 hours; we structure engagements with daily 2-3 hour overlap windows for synchronous work, async written handoff for the rest.
All engineers sign individual NDAs with the client in addition to the BearPlex master agreement. They use the client's infrastructure (VPC, IAM, source control) where possible. Code written during the engagement belongs to the client. We never train models on client data without explicit written agreement.
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