Junior AI engineer

3 days ago

Boston, Massachusetts, United States Daice Labs Full time $80,000 - $120,000 per year

Company Description

Daice Labs is building hybrid AI frameworks that integrate today's models into systems that learn continuously. Founded by MIT CSAIL scientists, we focus on building new architectures by combining LLMs/DL with symbolic reasoning and bio-inspired system design. Operating on two tracks, our Product Lab develops industry-specific solutions for collaborative human teams + AI co-building and co-owning vertical applications, while our Research Lab explores how principles of natural intelligence can guide systems design of new hybrid AI architectures.

Join us in taking the next leap in productivity through collaborative innovation.

Role Description

This is a full-time remote role for a Junior applied AI/ML Engineer. The engineer will be responsible for developing, testing, and optimizing AI/ML models with a focus on hybrid AI architectures. Day-to-day tasks include implementing innovative algorithms, conducting experiments to validate models, collaborating with cross-functional teams, and contributing to research on new AI paradigms. The engineer will also be involved in translating research findings into practical applications and building scalable AI solutions.

Qualifications

  • Strong understanding of Pattern Recognition, Neural Networks, LLMs, agentic architectures
  • Proficiency in Computer Science and ML Algorithms (proficiency in python and ML stack languages)
  • Experience with Statistics
  • Good problem-solving skills and the ability to work collaboratively
  • Excellent written and verbal communication skills
  • Bachelor's degree in AI, Machine Learning, Computer Science, or related field
  • Experience in bio-inspired system design and hybrid AI architectures is a plus
  • Ability to work independently and remotely
  • 1-2 years building ML platforms/dev‑tools; you've shipped SDKs, sandboxes, or orchestration systems
  • Strong in TypeScript + Python (Go a plus); API design (gRPC/REST), JSON Schema, signing/SBOMs
  • Containers/WASM, quotas, least‑privilege patterns
  • Observability (OTel), Postgres/pgvector, Redis; message queues (SQS/NATS/Kafka)
  • Evaluation mindset; you've built golden sets, regressions, drift detection, and CI gates for ML/agents
  • Security & privacy chops (network allow‑lists, PII controls, auditability)

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