Join Specter as an ML Infrastructure engineer to build and scale machine learning systems for real-time perception across edge-cloud platforms.
Posted by employer 1 year ago
First seen on Joblaze 54 minutes ago
Last verified on the company career page 54 minutes ago
Skills & Technologies
What you'll build
- Design and implement scalable ML training and inference pipelines
- Develop continuous training and evaluation systems
- Design large-scale multi-modal data pipelines
- Create data pipelines for managing multi-modal sensor datasets
- Implement model monitoring and performance analytics
Must have
- Strong software engineering fundamentals in Python
- Working proficiency in a systems language (Rust, Go, C++)
- Experience with ML frameworks (PyTorch, TensorFlow)
- Deep experience building model inference systems at scale
- Hands-on experience with distributed compute frameworks for ML
Nice to have
- Familiarity with LLM/VLM serving frameworks
- Experience with vector databases for similarity search
- Familiarity with video processing or sensor fusion
Not disclosed in this posting: compensation, years of experience, work arrangement, visa sponsorship.
Joblaze summary
In this role, the ML Infrastructure Engineer at Specter focuses on building and scaling machine learning systems that facilitate real-time perception and inference for edge-cloud applications. Key skills include proficiency in Python and a systems language like Rust or Go, along with experience in ML frameworks such as PyTorch or TensorFlow. This position is well-suited for engineers with a strong background in distributed systems and model inference at scale, particularly those who have worked with video processing or multi-modal data. Specter's small, fast-growing team is driven by a mission to automate the physical world through advanced AI technologies.
Joblaze insights
Quick facts
- What's the tech stack?
- Joblaze extracted these technologies from the posting: C++, Go, LanceDB, PyTorch, Python, Qdrant.
- What seniority level is this role?
- Specter targets mid-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Software Engineer - ML Infrastructure role at Specter.
From the original posting
The Role
Specter is hiring an ML Infrastructure engineer to build and scale the machine learning systems that power real-time perception and inference across our edge-cloud platform. This role owns the data, training, deployment, and serving infrastructure for the computer vision and VLM systems that enable autonomous monitoring and orchestration across our customers' physical assets.
Responsibilities:
Designing and implementing scalable ML training and inference pipelines for perception models (object detection, tracking, classification, segmentation) and VLMs.
Developing continuous training and evaluation systems to improve model performance from production data feedback loops.
Designing large-scale multi-modal data pipelines for ingesting, processing, and indexing video and sensor data spanning both batch and streaming workloads.
Creating data pipelines for ingesting, labeling, versioning, and managing massive multi-modal sensor datasets (video, radar, lidar, thermal).
Implementing model monitoring, A/B testing frameworks, and performance analytics for deployed perception systems.
Collaborating with perception researchers to transition models from research to production at scale across thousands of edge nodes.
Building tools and infrastructure for distributed training, hyperparameter optimization, and experiment tracking.
Qualifications:
Strong software engineering fundamentals in Python, with working proficiency in a systems language (Rust, Go, C++) for performance-sensitive data and inference paths.
Working proficiency with ML frameworks (PyTorch, TensorFlow) and model optimization tooling.
Deep experience building and operating model inference systems at scale, request routing, batching, autoscaling, caching, and latency/throughput tuning under real production load.
Hands-on experience with distributed compute frameworks for ML and data workloads (Ray, Spark, or equivalent), including GPU cluster management and orchestration.
Strong understanding of distributed systems fundamentals: partitioning, replication, backpressure, exactly-once semantics.
Experience with vector databases (QDrant, LanceDB, or equivalent) for similarity search and retrieval workloads.
Familiarity with LLM/VLM serving frameworks (VLLM, SGLang, TensorRT-LLM) in production is a strong plus.
Familiarity with video processing, sensor fusion, or multi-modal perception systems is a plus.
Standard company text repeated across Specter's postings is omitted here.