Orbitshift
OrbitShift Machine Learning Engineer
OrbitShift AI is an enterprise-grade AI platform that automates sales intelligence, account planning, and RFP responses to accelerate deal cycles.
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What is OrbitShift AI?
OrbitShift AI is a multi-agent AI platform purpose-built for enterprise consultative sales, helping teams automate research and generate deep account intelligence. The platform analyzes financial reports, executive movements, and strategic agendas to identify high-value opportunities that traditional CRMs miss. Its suite includes accountOS, knowledgeOS, and rfpOS, which together automate everything from account planning to complex RFP responses.
You'll be a good fit if you have
- Deep expertise in transformer architectures including attention mechanisms, positional encodings, tokenization strategies, scaling laws, and approaches to mitigate training instabilities.
- Proven experience with model training and fine-tuning using PyTorch or JAX; familiarity with the Hugging Face ecosystem (transformers, datasets, PEFT, TRL) is essential.
- Strong command of fine-tuning techniques such as SFT, RLHF, DPO, LoRA, and QLoRA, ensuring production-ready model performance.
- Proficiency with RAG architectures and experience with vector databases (e.g., Pinecone, Weaviate, FAISS, Qdrant) alongside embedding models.
- A track record of publishing or contributing to research at top-tier ML/NLP/AI venues (NeurIPS, ICML, ICLR, ACL, EMNLP) or equivalent research engineering experience at premier research labs (e.g., Google DeepMind, Microsoft Research, Meta FAIR, OpenAI).
Key Responsibilities
- Research to Production: Translate state-of-the-art ML research into reliable, scalable production systems. Identify relevant papers, reproduce results, and adapt novel ideas to address specific enterprise constraints.
- Model Development: Design, train, and evaluate language models and task-specific ML components, including fine-tuning techniques such as supervised fine-tuning (SFT), RLHF, DPO, instruction tuning, and domain adaptation for enterprise sales contexts.
- RAG & Retrieval Systems: Build and optimize retrieval-augmented generation pipelines by developing effective chunking strategies, selecting appropriate embedding models, hybrid search, and re-ranking approaches.
- Agentic Reasoning: Research and implement advanced reasoning patterns-such as chain-of-thought, tool-use, reflection, and planning and integrate them into a multi-agent AI platform.
This is not a cold application
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Revenue Intelligence
Moving enterprise sales from reactive planning to continuous, AI-led execution.
Why Orbitshift
Enterprise sales reps currently spend over 70% of their time on manual research and admin work rather than actual selling. OrbitShift AI flips this by acting as an autonomous research engine.
By synthesizing real-time signals like hiring spikes, leadership changes, and financial updates, OrbitShift AI tells sales teams exactly where to focus today, ensuring revenue growth becomes deliberate rather than accidental.
Solving the information overload in enterprise sales.
OrbitShift AI in the news
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