Machine Learning Engineer
About The Position
The Machine Learning Engineer will own the full machine learning pipeline, from raw sensor data to production inference, dataset curation to deployment monitoring, and cloud to edge. This role is for an engineer who is motivated to put models into production and see them on the road. Blissway processes 11 million images daily, running various analyses like detection, segmentation, classification, embeddings, and re-identification. The engineer will work with real hardware in the field, handle vision at a large scale, and utilize a range of tools from classical computer vision to custom state-of-the-art models. The role involves both cloud and edge computing, requiring models that are fast, small, and power-efficient for roadside hardware.
Requirements
- 2 to 6 years of software engineering experience with a focus on machine learning and/or computer vision.
- True end-to-end experience: taken models from raw data to production and owned what happens after they ship.
- Strong software engineering fundamentals and hands-on ML experience.
- Ability to write production-quality code and train/debug models.
- Experience owning models beyond the notebook: trained, deployed, monitored, and iterated in production.
- Real computer vision depth, including experience with modern computer vision and the judgment to know when a classical technique beats a heavy model.
- Proficiency in Python and TypeScript, or the ability to learn fast.
Responsibilities
- Own the full machine learning pipeline: collection, dataset curation, training, deployment, monitoring, and iteration.
- Decide which problems are worth solving and take them from raw sensor data to production.
- Work with real hardware in the field and test ideas on real roads.
- Process and analyze 11 million images daily, running detection, segmentation, classification, embeddings, and re-identification.
- Utilize classical computer vision algorithms and custom-trained state-of-the-art models.
- Train models when off-the-shelf solutions are insufficient.
- Work on both cloud and edge computing, optimizing models for speed, size, and power efficiency on roadside hardware.
Benefits
- Relocation bonus
- Personalized health coverage (ICHRA)
- 401(k) matching up to 4%
- Company-sponsored life & disability insurance
- Competitive equity package with annual tender process
- 4 weeks of untracked PTO
- 12 weeks of paid parental leave
- 12 weeks of fully paid sabbatical every 5 years
- Daily team lunches
- Fully stocked kitchen with snacks
- Annual 4-day team getaway (BlissTrip)
- Monthly team events (game nights, escape rooms, dinners)
- Tuition reimbursement for courses, programs, and conferences
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
