[Hiring] AI Engineer @Maneva

Role Description

Maneva is building the Thinking Factory: a factory floor that sees, understands, and acts on its own. Our Vision-to-Action AI turns video from off-the-shelf cameras into real-time decisions, with no rip-and-replace of existing infrastructure. We work with mid-market and enterprise manufacturers to deliver end-to-end outcomes, not dashboards:

• Higher yields: catch defects and process drift as they happen, and cut scrap and downtime.
• Closed-loop integration: connect vision insights directly to MES, PLC, and other production systems so findings trigger action.
• Safer, compliant floors: detect safety and compliance risks continuously, instead of relying on periodic audits.

Founded in 2022, Maneva raised a $27M Series A led by U.S. Venture Partners in June 2026 to push the frontier of our vision models, expand factory orchestration, and bring Thinking Factories to new industries and geographies. The models you build here will run on real production lines, where accuracy, latency, and reliability decide outcomes.

What you'll do:

• Define the technical strategy and architecture for vision models across detection, VLM, and VLA workloads.
• Train, fine-tune, and adapt models (YOLO variants, open VLMs, VLA policies) on proprietary data, including distillation, quantization, and LoRA-style adaptation.
• Build data engines: labeling pipelines, auto-labeling with foundation models, active learning, and synthetic or simulated data.
• Design rigorous evaluation: benchmarks, regression suites, failure-mode analysis, and online metrics tied to product outcomes.
• Ship models to production under latency, memory, and cost limits, on cloud GPUs and edge hardware (TensorRT, ONNX, Jetson or similar).
• Lead cross-team technical projects; write design docs; review code and models; mentor senior and mid-level engineers.
• Track the research frontier, decide what is worth adopting, and turn it into prototypes and product.

Qualifications

• 5+ years building and shipping ML systems, with focus on computer vision; BS/MS/PhD in CS, EE, or a related field, or equivalent experience.
• Deep hands-on experience with object detection and segmentation (YOLO family, DETR-style models), including training, tuning, and production deployment.
• Hands-on experience with VLMs (for example LLaVA-, Qwen-VL-, or PaliGemma-style models): fine-tuning, prompting, grounding, and evaluation.
• Experience with VLAs or embodied and robot-learning models (for example OpenVLA-, RT-2-, or pi0-style policies), or a clear record in adjacent multimodal work.
• Strong PyTorch skills and fluency with distributed training, mixed precision, and GPU profiling.
• Track record of taking models from research to production: model optimization, inference serving, monitoring, and iteration on real-world failures.
• Evidence of staff-level impact: setting direction across teams, making sound build-versus-adopt calls, and raising the bar for others.
• Clear written and verbal communication with engineers, researchers, and product leaders.

Nice to have

• Publications or open-source contributions in vision or multimodal learning.
• Experience with video understanding, 3D perception, or multi-sensor fusion.
• Experience with simulation, sim-to-real transfer, or reinforcement and imitation learning.
• Edge and embedded deployment experience (TensorRT, CUDA, quantization-aware training).
• Experience with LLM tooling such as vLLM, or with RLHF-style post-training for multimodal models.

Benefits

• Salary based on location and experience - market rate.
• Equity participation in a high-growth AI startup.
• Comprehensive health, dental, and vision coverage.
• Flexible PTO and professional development opportunities.
• Remote-first culture with modern collaboration tools.