Top Entry Level Machine Learning Jobs in Boston, MA

Develop, optimize, and deploy audio and multimodal machine learning models on embedded and edge devices. Build real-time inference pipelines, convert models to efficient C/C++ implementations, and optimize latency, memory, SRAM, and power across MCUs, DSPs, NPUs, and accelerators. Collaborate with researchers, DSP experts, firmware engineers, and hardware teams to deliver production-ready embedded AI systems using model development, quantization, profiling, and on-device runtimes.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

The AI/Machine Learning Specialist designs and implements AI solutions, collaborates with cross-functional teams, and continuously improves ML processes.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

Develop, train, evaluate, and deploy machine learning models and AI-powered applications. Responsibilities include data preprocessing, feature engineering, deep learning development, cloud-based model deployment, and collaboration with data engineers, MLOps teams, and business stakeholders. The role also requires applying strong analytical and research skills while staying current with advances in artificial intelligence.

Six-month remote AI/ML trainee program focused on developing Python and database skills, understanding complex code, and exploring LLM, vector database, knowledge graph, and open-source technologies. Trainees participate in global team meetings through Zoom and collaborate using Slack, Jira, and WhatsApp. The role includes a two-week in-person sprint at an offsite location. A B.Tech or MCA degree and strong self-learning ability are required.

Build and deploy deep-learning models for financial market data, owning the full workflow from raw data and experiments through daily production inference. Responsibilities include managing small-scale GPU infrastructure, conducting leakage-proof and regime-aware evaluations, reproducing research, reading recent AI literature, and communicating findings. The role requires strong ML fundamentals, Python and PyTorch expertise, hands-on infrastructure skills, rigorous experimentation, and self-directed remote work. Trading experience is not required.

Paid 3-month remote contract for students or recent grads to build and test LLM-based solutions using the Token Factory inference platform. Assist senior SAs with prompt engineering, model selection, benchmarking, and inference optimization. Run performance and quality experiments and contribute to internal tooling and automation. Mentorship provided; strong performers may be considered for full-time roles.