Senior Computer Vision Engineer

Inventure; San Francisco (CA); Hybrid; USD 220,000 - 250,000; Full time

Benefits offered by this job

Equity grant

Hybrid work pattern

Job summary

Inventure, a San Francisco Bay Area hardware company, is seeking a Senior Computer Vision Engineer to own the computer vision for a commercial product that reduces food waste. The role is hybrid in the SF Bay Area and focuses on training, evaluation, and deploying edge-to-cloud computer vision systems.

You will build MLOps pipelines, curate datasets per customer, and collaborate with MLOps and edge engineers to meet production targets.

Qualifications

• Experience shipping CV models into production.
• Ability to design evaluation metrics and harnesses.
• Experience with edge/embedded systems.
• Strong communication and documentation.

Responsibilities

• Train and evaluate segmentation, classification and mass-estimation models for the camera pipeline.
• Optimise edge models for production and operationalise the ML pipeline end to end, with model lineage tracking throughout.
• Create and curate purpose-built datasets per customer and vertical to hold accuracy across food types, kitchen environments and deployment configurations.
• Analyse failure cases systematically, from unfamiliar food classes to novel kitchens and difficult lighting and clutter, and drive the data and modelling decisions that close the gaps.
• Build annotation tooling and ground-truth workflows, including foundation-model-assisted labelling, to keep pace with model iteration.
• Partner with the team's MLOps and edge engineers on training practices, versioning and deployment tradeoffs as they come up.

Skills

Computer vision

Deep learning

Python

PyTorch

OpenCV

Edge deployment

MLOps concepts

Tools

Weights & Biases

MLflow

SageMaker

ClearML

Cloud platforms

Job description

Senior Computer Vision Engineer | San Francisco Bay Area (Hybrid) | $220K--$250K base

We've partnered with a Bay Area hardware company on a mission to prevent waste, starting with food. They build smart systems and infrastructure for homes, businesses and municipalities that turn food scraps into a valuable resource instead of sending them to landfill. Tens of thousands of their home recyclers are already in kitchens, diverting large volumes of food waste every year, and they are now launching a first-of-its-kind commercial product: an end-to-end system for managing, understanding and preventing food waste in settings like grocery, restaurants and food service. This is a mission-driven product with a real one behind it, and the computer vision work sits right at the centre of the next chapter.

This is a rare opportunity to own the computer vision behind that commercial product. The system puts a camera inside a high-capacity food recycler: models identify and quantify what passes through, and the pipeline turns that signal into procurement and operational guidance for large food-service operators. You'll join a small, capable team and own the modelling and training infrastructure that powers it, designing the cloud-side evaluation harness that decides whether edge models meet production targets and building the ground-truth workflows underneath. It is a hands-on IC role for someone who brings deep computer vision fundamentals to fine-tuning models, building MLOps pipelines, and staying methodical as the system grows in complexity.

The Role

As a Computer Vision Engineer, you will:

• Train and evaluate segmentation, classification and mass-estimation models for the camera pipeline, from prompting foundation models to fine-tuning ConvNets and VLMs.
• Optimise edge models for production and operationalise the ML pipeline end to end, with model lineage tracking throughout.
• Create and curate purpose-built datasets per customer and vertical to hold accuracy across food types, kitchen environments and deployment configurations.
• Analyse failure cases systematically, from unfamiliar food classes to novel kitchens and difficult lighting and clutter, and drive the data and modelling decisions that close the gaps.
• Build annotation tooling and ground-truth workflows, including foundation-model-assisted labelling, to keep pace with model iteration.
• Partner with the team's MLOps and edge engineers on training practices, versioning and deployment tradeoffs as they come up.

About You

• You have strong fundamentals in computer vision and deep learning, across segmentation, detection, classification and tracking, deep enough to make informed architecture calls.
• You are fluent with modern approaches such as VLMs, LLMs, foundation models and agentic systems alongside classical deep learning, and you know when to fine-tune a ConvNet, when to prompt a VLM and when to wire up an agent, including the practical realities of putting any of them into a product.
• You evaluate models rigorously: designing metrics, building eval harnesses, and using the results to drive product decisions rather than to publish a number.
• You have taken a model to production and dealt with what comes after, from drift and edge cases to latency budgets, not just to a benchmark.
• You have a bias for action, and you can make build-versus-buy or tooling decisions backed by data or a clear rubric, and show your working.
• You communicate clearly, explain tradeoffs to non-technical stakeholders, push back honestly when you disagree, and write docs others can follow.
• You are fluent in Python, PyTorch and OpenCV, with experience using LLM and agent frameworks, and you care about applying AI to food-waste reduction.
• One of these backgrounds fits you: a computer vision engineer who has shipped models into a real product, an ML engineer working across classical CV and modern foundation models, or an applied scientist who owns evaluation and MLOps as much as modelling.
• Nice to have: video understanding such as temporal consistency, tracking and video segmentation; MLOps tooling such as Weights & Biases, MLflow, SageMaker or ClearML; and hardware or IoT product experience, particularly computer vision and cameras on embedded systems.
• You are based in the San Francisco Bay Area and comfortable with a hybrid working pattern.

The role pays a competitive base plus a potential equity grant. The team is moving quickly with this hire. If you're interested in owning the computer vision that turns a stream of food waste into operational intelligence, on a product built to keep food out of landfill,