Senior Machine Learning Engineer, Infrastructure Intelligence
About The Position
Cyvl is a Boston-based tech startup revolutionizing how governments map and manage transportation infrastructure. Our enterprise-grade hardware and software solutions leverage 3D mapping sensors to capture LiDAR, imagery, and GPS data, retrofitted to our customers' vehicles. This data is processed through our AI-powered cloud pipelines to generate actionable geospatial insights that save cities time, money, and resources. At Cyvl, our mission is to empower governments to build and maintain public infrastructure they're proud of by accelerating decision-making through our sensors and Infrastructure Intelligence Platform. This role is the engine behind our intelligence extraction: the person who figures out how to turn millions of street-level images and dense LiDAR point clouds into structured, queryable knowledge about the physical world. We're a fast-moving, high-growth team that believes in solving real-world problems with authenticity, simplicity, and boldness. Every member of our team is empowered to take ownership, deliver results, and leave a lasting impact on the communities we serve. About You You're an ML engineer who has spent your career figuring out how to extract more from data with less. You've built systems that scale detection and measurement across massive sensor datasets without needing a massive labeling budget. You've worked with 3D data, not just 2D images, and you understand how to fuse what a camera sees with what a LiDAR measures. You want to be the first ML hire at a company where the data, the compute, and the customers already exist, and where the learning system is yours to design from scratch.
Requirements
- 4-8 years of professional ML experience, with at least 2 years working on large-scale sensor data (imagery, LiDAR, or both).
- Production experience with data-efficient learning: you have built or significantly contributed to an active learning, auto-labeling, or semi-supervised training system that shipped.
- Hands-on experience with LiDAR-camera fusion or multi-modal perception (e.g., projecting image detections into 3D, extracting geometric attributes from point clouds, BEV representations, or cross-modal embeddings).
- Experience training and evaluating models at dataset scale (millions of images), not just benchmarks.
- Built representation learning or self-supervised pre-training systems that were reused across multiple downstream tasks.
- Strong Python. Writes production code, not just notebooks. Comfortable with experiment tracking, dataset versioning, and model deployment infrastructure.
Nice To Haves
- Experience at an autonomous vehicle company (perception, mapping, or data engine teams).
- Familiarity with point cloud architectures (PointNet family, voxel-based methods, or projected multi-view approaches).
- Geospatial domain knowledge (PostGIS, coordinate systems, spatial indexing).
- Experience with vision foundation models (SAM, CLIP, DINOv2) and adapter/decoder-head patterns.
- Background in mobile mapping, SLAM, or survey-grade sensor systems.
- Experience hiring and mentoring ML engineers.
Responsibilities
- Design and own the ML strategy for extracting intelligence from Cyvl's imagery and LiDAR data, including choosing the right approach for data-efficient learning (active learning, semi-supervised methods, foundation model adaptation, or whatever works).
- Build the pipeline that detects, classifies, and measures infrastructure assets across millions of images and co-registered point clouds.
- Develop cross-modal representations that encode both visual and geometric information so that 3D compliance and condition questions can be answered from stored embeddings rather than re-processing raw data.
- Ship models to production, working alongside a strong infrastructure/software engineering team. Build confidence calibration, automated evaluation, and model promotion systems.
- Over 6-12 months, hire and lead a small ML team.
Benefits
- Competitive salary and equity package ($250K-$350K total comp, equity-heavy for a founding role)
- Comprehensive health, dental, and vision coverage
- 401(k) with immediate vesting
- Generous PTO, paid parental leave, and flexible work options
- Annual professional development stipend
- Team events, referral bonuses, and a collaborative, growth-focused culture
- Full technical latitude to set ML strategy from the ground up
- Real sensor data, real domain problems, real revenue -- not a research project
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
