Role: Data & Machine Learning Engineer

• End-to-end owners. You own outcomes, not tasks. If something you're responsible for falls through a crack, that's on you. Not because someone assigned it, but because you wouldn't have it any other way.
• Clear, direct communicators. Bad news doesn't get better with age. Context doesn't transfer through vague Slack messages. You surface problems early and communicate with precision.
• Uncompromising on quality. You have a quality bar that's yours, not your manager's. You won't ship something you wouldn't stand behind, even under deadline pressure.
• AI-obsessed. You see AI as how work gets done, not a nice-to-have. If you're still doing something manually that AI could handle, you feel that as friction, not normalcy.
• Structured thinkers. When faced with an ambiguous problem, you break it down, reason through the trade-offs, and arrive at a position. You don't wait for someone to tell you the answer.
• Experienced enough to use AI systematically. You have enough depth in your craft that AI makes you dangerous, not dependent. You direct it, evaluate its output, and know when it's wrong.

• Own the data layer behind AI systems real businesses depend on, across multiple domains, not one product for years
• Build genuine depth in the parts of ML engineering that matter in production: data quality, feature design, evaluation, reproducibility, inference cost and latency
• Work directly with founders, annotation teams, and client stakeholders. Your judgment on data shapes what the model can do, not just how it gets trained
• Learn how dataset design, labelling quality, and evaluation connect to business outcomes, because on every project the client is paying for outcomes

• Build and maintain data pipelines using Python and SQL.
• Transform raw text into categorical variables and structured features.
• Work closely with annotators to understand labelling guidelines, edge cases and data-quality issues.
• Convert annotated data into formats suitable for model training, inference and evaluation.
• Implement model workflows using predefined architectures and training methods.
• Support recommender systems, including collaborative-filtering approaches.
• Build and optimize inference pipelines for vision and vision-language models such as Grounding DINO and VLMs.

• 4+ years of overall engineering experience with strong software fundamentals. ML engineering on top of weak engineering doesn't work.
• Strong hands-on experience with Python, SQL and data engineering.
• Experience processing unstructured text and engineering categorical features.
• Experience working with annotated datasets and data-labelling teams.
• Practical knowledge of recommender systems and collaborative filtering.
• Understanding of computer-vision and vision-language models from an inference perspective.
• Familiarity with data validation, model evaluation and reproducible ML workflows.
• Strong analytical, problem-solving and communication skills.

• Hands-on experience with PyTorch: training loops, fine-tuning, and debugging model behaviour
• Working with embeddings and similarity search at scale (pgvector, FAISS, or a dedicated vector database)
• Experience with multimodal datasets: paired image-text data, annotation tooling, and quality control at scale
• Optimizing model inference: batching, quantization, GPU utilization, and latency budgets
• Built internal tooling for data and ML work: annotation dashboards, dataset versioning, evaluation harnesses

• Uses Claude Code or Cursor as the primary development environment, shipping production code through AI-assisted workflows daily
• Maintains CLAUDE.md and project context files so AI tools know your conventions, architecture, and constraints
• Plan-first for multi-file changes: AI reads the codebase, you approve the approach, then execute
• Uses AI to accelerate the data work itself: drafting transformation logic, generating test fixtures and edge cases, stress-testing pipelines, triaging model errors
• Tests after every AI-generated change. AI writes fast; you keep it honest