Pre-screened and vetted in New Jersey.
Junior AI/ML Engineer specializing in LLM agents, RAG, and distributed systems
“Python backend engineer focused on high-throughput document/PDF processing systems, building end-to-end pipelines that extract structured content for downstream NLP use cases. Demonstrates strong practical MLOps-adjacent infrastructure skills: Kubernetes deployments, GitLab CI, GitOps workflows, and an incremental migration to AWS using EC2/Lambda tradeoffs. Deep hands-on optimization experience (selective OCR, layout-aware extraction, parallelism, caching, idempotency, and backpressure/autoscaling).”
Mid-level Machine Learning Engineer specializing in Bayesian inference and reinforcement learning
Senior AI/ML Systems Architect specializing in cloud-native MLOps and GenAI
Principal/Lead Data Engineer specializing in large-scale pipelines, NLP, and graph databases
Mid-Level Full-Stack Engineer specializing in AI and 3D computer vision
“Built and productionized an LLM-driven document verification workflow for a construction firm’s submittals process, moving from a Vercel/Next.js prototype to a FastAPI + LangChain/LangGraph backend with background workers and multi-server deployment. Uses LLM tools (e.g., OpenAI Codex/Cloud Code) for rapid development and log-driven root cause analysis, and partners with customer teams on evaluation metrics and iterative improvements.”
Junior Software Engineer specializing in backend systems and data platforms
Mid-Level Software Engineer specializing in data engineering and machine learning for FinTech
Mid-level Software Engineer specializing in FinTech data platforms and full-stack analytics
Junior Full-Stack AI Engineer specializing in Agentic AI and RAG systems
Senior Marketing Manager specializing in demand generation and partner marketing
Mid-level Full-Stack Software Engineer specializing in AI/LLM and cloud-native platforms
Mid-level Machine Learning Engineer specializing in Generative AI and foundation models
Mid-level Software Engineer specializing in GenAI, RAG, and distributed systems
Junior Financial Markets Analyst specializing in quantitative research and FinTech
“Analytics-focused candidate with internship experience at eToro and strong finance/product analytics exposure. They’ve worked on market sizing for Nordic stock launches, replicated a classic behavioral finance study using Python and CRSP data, and built cohort, retention, and churn analyses that informed onboarding and engagement recommendations.”
Mid-level Software Engineer specializing in distributed systems and ML infrastructure
Mid-level Robotics Engineer specializing in ROS2 autonomy and simulation
Senior Full-Stack Game Engineer specializing in multiplayer Unity and mobile systems
“Unity/C# game developer with hands-on experience shipping large-scale multiplayer mobile games, including titles cited at 1M+ and 10M+ downloads. Combines real-time networking and physics optimization expertise with AI/MR research experience, including an IEEE-published sports coaching system using pose estimation, SMPL-X, and LSTM models. Particularly strong in latency-sensitive, cross-platform interactive systems spanning mobile, multiplayer, and mixed reality.”
Mid-level Data Scientist specializing in GenAI, RAG, and forecasting
“ML/NLP engineer focused on large-scale data linking for e-commerce-style catalogs and customer records, combining transformer embeddings (BERT/Sentence-BERT), NER, and FAISS-based vector search. Has delivered measurable lifts (e.g., +30% matching accuracy, Precision@10 62%→84%) and built production-grade, scalable pipelines in Airflow/PySpark with strong data quality and schema-drift handling.”
Junior Machine Learning Engineer specializing in multimodal systems and LLMs
“Built and productionized a domain-specific LLM-powered RAG knowledge assistant at JerseyStem for answering questions over large internal document corpora, owning the full stack from FAISS retrieval and LoRA/QLoRA fine-tuning to AWS autoscaling GPU deployment. Drove measurable gains (28% accuracy lift, 25% latency reduction) and improved reliability through hybrid retrieval, grounded decoding, preference-model reranking, and Airflow-orchestrated pipelines (35% faster runtime), while partnering closely with non-technical stakeholders to define success metrics and ensure adoption.”
Mid-Level Full-Stack Software Engineer specializing in cloud, mobile, and GenAI