Senior Data Science/Machine Learning Engineer

Summary

Join Turing's UltraLab, a fast-paced R&D hub, as a Lead Applied Scientist to design and deliver AI-first prototypes solving real-world problems. This role blends technical innovation with client engagement, advising on early-stage AI solutions and working with executive stakeholders. You will architect AI prototypes, provide client-facing technical leadership, catalyze innovation, focus on delivery, communicate effectively, integrate cross-functionally, and mentor junior team members. The ideal candidate possesses strong machine learning and LLM expertise, system design skills, and excellent communication abilities. Turing offers a competitive compensation, flexible working hours, and a full-time remote opportunity.

Requirements

• Strong background in supervised/unsupervised ML, including classification, regression, clustering, and evaluation
• Expertise with PyTorch, TensorFlow, or equivalent
• Proven experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines
• Practical knowledge of LLM-based architectures and usage of APIs (OpenAI, Anthropic, Cohere, etc.)
• Comfortable with prompt engineering and diagnosing model behavior
• Exposure to LangChain, CrewAI, Autogen, or custom multi-agent orchestration systems
• End-to-end system design for real-time and batch inference systems
• Experience with feature engineering, data pipelines, and scalable infrastructure
• Strong Python skills with robust practices in testing, documentation, and Git workflows
• Exposure to NumPy, pandas, scikit-learn, Transformers libraries, etc
• Experience deploying solutions in AWS/GCP/Azure environments
• Familiarity with Docker, MLflow, SageMaker, or other MLOps stacks
• Designing evaluation pipelines for GenAI use cases
• Awareness of ethical risks, fairness, and responsible deployment in high-stakes contexts

Responsibilities

• Architect AI Prototypes Across Projects
• Drive technical architecture across multiple AI initiatives, especially those involving agentic systems, RAG pipelines, and GenAI capabilities
• Understand customer needs deeply and guide bespoke prototype customization with practical implementation tradeoffs
• Advisory & Client-Facing Technical Leadership
• Represent the technical arm in customer meetings and product discovery calls
• Frame, challenge, and reframe problems with business stakeholders
• Propose directionally correct solutions while teasing out hidden needs, constraints, and delivery goals
• Innovation Catalyst
• Identify new directions for GenAI exploration based on emerging trends and customer pain points
• Drive hypothesis-driven experimentation with measurable outcomes
• Delivery-Focused Prototyping
• Lead early-stage solution design with attention to feasibility, scale, and downstream implementation paths
• Work with engineering to scope MVPs and iterate quickly toward high-quality demos
• Communication & Influence
• Translate complex AI concepts into business-relevant insights
• Confidently push back on suboptimal approaches (e.g., premature fine-tuning) and steer stakeholders toward more impactful alternatives
• Cross-Functional Integration
• Collaborate across engineering, product, science, and GTM teams to align efforts toward use-case impact
• Mentorship & IP Development
• Mentor junior scientists/engineers, review work, and promote learning through code walkthroughs, system design sessions, and feedback cycles
• Contribute to thought leadership via blogs, whitepapers, or IP filings rooted in customer project learnings

Preferred Qualifications

• 8–12 years of relevant experience (can flex slightly based on strength)
• Experience in customer-facing, pre-sales, or delivery management roles involving AI/ML
• Strong communication and stakeholder management skills
• Prior exposure to enterprise-grade deployments or working with executive-level clients
• Experience in hybrid ML+LLM solutioning and business metric optimization

Benefits

• Competitive compensation
• Flexible working hours
• Full-time remote opportunity