Job Description:
Machine learning engineer
You will be responsible for the backbone of our intelligence: the models and data that power Eve. This role focuses on optimizing, productionizing, and managing our entire ML infrastructure, from data ingestion and model evaluation to highly efficient inference. You will ensure our AI is not only smart but also fast, cost-effective, and reliable.
Responsibilities:
- Inference Optimization: Maximize the performance of our local and cloud-deployed models. This includes implementing quantization (QwQ), batching, and caching, and leveraging tools like vLLM and ONNX to reduce latency and cost.
- RAG & Vector DB Management: Own the data ingestion pipelines for our Retrieval-Augmented Generation (RAG) system. Manage the vector database (ChromaDB), evaluate and fine-tune embedding models, and optimize retrieval performance.
- Speech Pipeline: Manage and improve the performance of our speech recognition and synthesis models. This includes fine-tuning on custom data and optimizing for real-time streaming.
- MLOps & Evaluation: Build and maintain a robust MLOps pipeline. Create automated evaluation harnesses to benchmark model quality, drift, and performance regressions. Develop dashboards to monitor key metrics like accuracy, latency, and cost-per-inference.
Preferred Qualifications:
- 3+ years of experience focused on ML infrastructure, MLOps, and model optimization. This role is the backbone of our performance.
- Direct experience with the speech technology stack. Ideally, they have worked with real-time Speech-to-Text (like Whisper or, ideally, Parakeet) and Text-to-Speech (like FishAudio or XTTS) systems in production.
- Expertise in GPU-based inference optimization. Must have hands-on experience with tools like NVIDIA Triton Inference Server, TensorRT, vLLM, or ONNX to squeeze maximum performance from our models.
- Experience building and maintaining data ingestion and processing pipelines for RAG systems. They should understand the nuances of chunking strategies, embedding model evaluation, and optimizing retrieval relevance.
- Strong MLOps background, including building automated model evaluation harnesses, monitoring for model drift, and managing CI/CD for machine learning systems.
Benefits Package (Canada & UAE)
Global (all roles)
- Stock options (standard 4‑year vest, 1‑year cliff; refresh grants annually)
- Competitive salaries
- Quarterly employee travel coupon
- Paid time off: 20 days + local holidays (min), flexible sick days
- Medical, dental, and vision insurance (company‑sponsored)
- Life insurance and disability benefits
- Fitness discounts / wellness stipend
- Tech equipment (laptop + peripherals; home‑office stipend)
- Community involvement: 4 hours/month paid volunteer time
- Company‑sponsored tech talks and happy hours
Canada‑specific
- RRSP matching (company match up to 4% base)
- Health Spending Account (HSA) or Lifestyle Spending Account (LSA)
- Parental leave top‑ups aligned to provincial standards
- Flexible work: hybrid/remote within jurisdiction
UAE‑specific
- Health insurance compliant with UAE regulations (dependents eligibility by level)
- Annual flight allowance (by level), relocation support
- End‑of‑service gratuity per UAE labor law
- Housing/transport allowances (as cash or consolidated TC)
- Flexible work: hybrid/remote within jurisdiction
