Senior ML Production Pipelines Engineer

Mindspan Institute; Cambridge (MA); On-site; USD 180,000 - 235,000; Full time

Benefits offered by this job

Health benefits

Dental benefits

Vision benefits

Family benefits

Retirement contribution matching

Job summary

Mindspan Institute in Cambridge, MA is seeking an experienced software engineer to productionize ML models and run them at scale. You will own end-to-end pipelines, from data movement to monitoring and automated failure feedback, collaborating with microscopy, optics and chemistry teams.

The role emphasizes building reliable, cost-efficient systems, with strong documentation and a bias toward practical, scalable solutions. This is an in-person, on-site position with flexible hours at times.

Qualifications

• Five years building and operating production software and ML models.
• Strong Python and software engineering practices (code review, testing, CI/CD).
• Hands-on experience with distributed batch or inference systems (cluster or cloud).
• Experience with containers, workflow orchestration, and infrastructure as code.
• Experience deploying models for GPU inference and speed improvements.

Responsibilities

• Turn trained research models into versioned, tested, deployable inference services and batch jobs.
• Build and run distributed inference over very large, chunked volumes.
• Own the automated training loop.
• Build evaluation and release processes ensuring every model version is scored on the same benchmarks.
• Instrument the pipeline for throughput, GPU use, cost per unit volume, and data lineage from raw image to final output.
• Make the system usable by scientists.
• Documentation and training: runbooks and design records for others to execute.

Skills

Five years building production systems

Strong Python and software engineering

Distributed batch/inference systems

Containers and workflow orchestration

GPU inference deployment

Tools

SLURM

Kubernetes

Ray

Dask

Spark

Docker

Job description

Mindspan Institute in Cambridge, MA is seeking an experienced software engineer to productionize ML models and run them at scale. You will own end-to-end pipelines, from data movement to monitoring and automated failure feedback, collaborating with microscopy, optics and chemistry teams.

The role emphasizes building reliable, cost-efficient systems, with strong documentation and a bias toward practical, scalable solutions. This is an in-person, on-site position with flexible hours at times.