Title
AI Engineer
Quick Summary
HelixNova Labs is hiring an AI Engineer to turn applied research into reliable product features across web and mobile. You will design and ship models for natural language processing, recommendations, and predictive analytics, working closely with product, data, and platform teams to deliver measurable impact. We welcome strong graduates and early-career engineers who can demonstrate solid fundamentals and real projects.
Project Category or Industry
Artificial intelligence for SaaS and data-driven products.
Type
Full-time employment.
Experience Level
Entry to mid-level, with clear growth paths and mentorship for freshers.
Duration
Permanent role.
Location
Remote-first with optional hybrid meetups in Berlin and Singapore. Maintain at least four hours of overlap with teams operating between UTC and UTC+8.
Salary
USD 78,000–115,000 base depending on location and experience, plus equity refresh opportunities and comprehensive benefits.
Payment Mode
Monthly payroll where supported; compliant contractor arrangements available in select countries.
Hiring Company Name
HelixNova Labs
Required Skills or Tools
Strong Python and software engineering practices, familiarity with deep learning or modern ML stacks, and the ability to ship features end-to-end. Comfort with cloud, APIs, and experiment tracking is important; knowledge of prompt design and retrieval pipelines is a plus.
Project Description
HelixNova Labs develops AI capabilities that power user experiences such as semantic search, content understanding, personalization, and forecasting. As an AI Engineer, you will help scope product opportunities, prototype quickly, and harden solutions for production. The work spans data exploration, modeling, evaluation, and partnering with MLOps and platform teams to make releases safe, observable, and maintainable.
Core Responsibilities and Expected Deliverables
Translate business needs into model-backed features with clear success metrics.
Build and evaluate models for NLP, classification, ranking, and recommendations, including ablation studies and error analysis.
Implement retrieval-augmented pipelines and lightweight services that expose models via APIs.
Produce production-ready code, tests, and documentation; collaborate on code reviews and design docs.
Instrument models with monitoring for quality, drift, and latency; propose and execute improvement plans.
Deliver artifacts such as reproducible training code, experiment reports, endpoint implementations, dashboards, and runbooks.
Required Experience and Preferred Qualifications
Proficiency in Python and data tooling, with a grasp of probability, statistics, and evaluation methodology.
Experience with at least one of PyTorch, TensorFlow, or JAX; working knowledge of scikit-learn.
Comfort working with SQL and a data warehouse; exposure to feature engineering and embeddings.
Preferred: experience with vector databases, prompt engineering, and retrieval-augmented generation; knowledge of distributed training or quantization is a bonus.
Coursework, internships, or open-source contributions demonstrating practical impact will be valued.
Tools or Platforms to Be Used
Modeling and experimentation: PyTorch or TensorFlow, Hugging Face, scikit-learn, MLflow or Weights & Biases.
Data and services: Python, FastAPI, SQL, dbt or similar, Postgres or BigQuery.
Infrastructure: Docker, Kubernetes, GitHub Actions, AWS or GCP, Terraform in partnership with the platform team.
Observability: Prometheus, Grafana, and OpenTelemetry-compatible logging.
Language Requirement
Professional English is required. Additional languages are helpful for cross-regional collaboration.
Communication Style
Written-first culture using GitHub issues and pull requests for design and reviews, Slack for daily collaboration, and Zoom for stand-ups, demos, and incident retrospectives.
Time Commitment or Working Window
Standard 40 hours per week with flexible scheduling. Aim for a consistent daily block that overlaps at least four hours with the core team between 09:00 and 17:00 in your local time.
Payment Terms
Monthly payroll for employees. For contractors, invoices are processed on net-30 terms upon acceptance of deliverables and timesheets.
Evaluation Criteria
Applications are assessed on portfolio quality, code clarity, and impact. The process includes an initial screening, a practical exercise focused on modeling and evaluation, a systems discussion on deploying and monitoring model-backed services, and a final culture interview. References may be requested.
Other Requirements
New hires sign a confidentiality agreement and comply with security and data handling policies. Light time-tracking may be used for distributed coordination. Occasional shared on-call support for AI services may be required.
About HelixNova Labs
HelixNova Labs is a privately held product studio building AI-enabled features for digital businesses in fintech, commerce, and media. Headquartered in Berlin with a distributed team across EMEA and APAC, we combine rigorous engineering with applied research to ship reliable, user-centric experiences. Learn more at https://www.helixnova.ai and reach our hiring team at careers@helixnova.ai.