Senior DS/ML engineer

Location: Remote
Team: Turing UltraLab – AI Prototyping & Innovation
Type: Full-Time

About the Role

Turing's UltraLab is a fast-paced R&D hub focused on designing and delivering AI-first prototypes that solve real-world problems. We are looking for a Lead Applied Scientist who not only excels in machine learning and LLM-based systems but also brings strong customer-facing advisory skills and a strategic delivery mindset.

This is not just an IC role. You will act as a trusted advisor, blending technical innovation with client engagement, guiding early-stage AI solutioning and working closely with executive stakeholders. You should thrive in ambiguity, ask the right questions, challenge assumptions, and lead iterative technical explorations across diverse use cases.

Key Responsibilities 1. 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.

2. 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.

3. Innovation Catalyst

• Identify new directions for GenAI exploration based on emerging trends and customer pain points.

• Drive hypothesis-driven experimentation with measurable outcomes.

4. 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.

5. 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.

6. Cross-Functional Integration

• Collaborate across engineering, product, science, and GTM teams to align efforts toward use-case impact.

7. 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.

Core Skills & Experience Technical Foundation

• 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.

LLM & Agentic Systems

• 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.

System Design & Data Engineering

• End-to-end system design for real-time and batch inference systems.

• Experience with feature engineering, data pipelines, and scalable infrastructure.

Software Engineering Excellence

• Strong Python skills with robust practices in testing, documentation, and Git workflows.

• Exposure to NumPy, pandas, scikit-learn, Transformers libraries, etc.

Cloud, MLOps & Deployment

• Experience deploying solutions in AWS/GCP/Azure environments.

• Familiarity with Docker, MLflow, SageMaker, or other MLOps stacks.

Evaluation & Responsible AI

• Designing evaluation pipelines for GenAI use cases.

• Awareness of ethical risks, fairness, and responsible deployment in high-stakes contexts.

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.

Why Join Turing UltraLab

• Tackle real-world AI problems, not academic demos.

• Build cutting-edge prototypes with high business visibility.

• Work in a team that values questioning assumptions and shaping solutions collaboratively.

• Operate with startup speed backed by global infrastructure.

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