Machine Learning Jobs in Cambridge, MA

Lead full-stack development for an AWS-based product: design and implement backend, data, and web UI; integrate external APIs and AI/ML features; collaborate cross-functionally; improve architecture and engineering practices; mentor teammates and deliver high-quality, production-ready code.

Design and implement pixel-perfect, high-performance web applications from wireframes to production. Collaborate with designers, PMs, and engineers, iterate on architecture and best practices, explore AI/ML integrations, and contribute to a collaborative engineering culture.

Lead engineering at a pre-seed GenAI startup: define architecture and tech stack, build the first B2B enterprise SaaS product, set up cloud infrastructure, DevOps and CI/CD, ensure code quality, hire and manage 1-2 engineers, and apply LLM/ML expertise to drive product and business strategy.

Design and optimize computer-vision and robotics ML models for constrained edge devices, build large-scale training and fine-tuning pipelines, compress and deploy models to embedded accelerators, profile on-device inference for latency and accuracy, and write maintainable Python/C++ code while staying current on state-of-the-art techniques.

Lead training, adaptation, evaluation, and productionization of Lila's AI models for scientific customer workflows. Build evaluation loops, design experiments, debug model failures, integrate model improvements into product workflows, and create reusable tooling for model adaptation, evaluation, and deployment while collaborating across research and software teams.

Lead the scientific direction and machine learning development for climate modeling. Manage model training, evaluation, and collaboration across teams to develop predictive systems based on hydrology and climate data.

Design and optimize on-vehicle perception inference: allocate CPU/GPU resources, compress and quantize large multimodal models, build TensorRT model conversion pipelines, and deliver production-quality low-latency C++/CUDA inference for power- and thermal-constrained vehicle SoCs.

Develop and lead research and product applications for LLMs, multimodal models, generative AI, and computer vision across cloud, devices, sensors, and robotics. Responsibilities include designing scalable and efficient models, optimizing them for resource-constrained hardware, evaluating real-world performance, mentoring scientists, collaborating with engineering teams, and contributing to patents, publications, and technical documentation.

Build and scale machine learning infrastructure, training systems, inference workflows, and agentic AI tools for protein design research. Improve distributed training performance, GPU utilization, reliability, and scalability while partnering with AI scientists and protein engineers. Translate research prototypes into reusable production systems, contribute to technical direction, evaluate emerging ML tools, and support scientific communication.

Lead development, training, validation, and application of generative chemistry and molecular models. Curate datasets and benchmarks, optimize workflows, collaborate with wet lab scientists, and translate experimental data into insights to shape the company's scientific direction.

Develop, fine-tune, and deploy visual and language foundation models (e.g., SAM, DINO) to robots in the field; build cloud-based training and inference infrastructure; manage and optimize robot data flows and databases; contribute across the robotics stack including web UI and full-stack deployment.

The MLOps Engineer focuses on developing tools and processes for ML workflows, empowering engineers, and ensuring high-quality model deployment. Key responsibilities include managing distributed systems, improving observability, and advocating best practices across teams.

Summer data science internship working on ML projects (anomaly detection, numerical optimization, PII/PHI NLP). Interns will develop models, write high-quality Python, use Linux and development tools, work with git, Docker, CI/CD and Kubernetes, participate in code reviews and agile rituals, and present results to technical and non-technical audiences.

Develop and productionize AI features from PoC to scalable deployments. Write production-ready Python code, implement testing and debugging, refine user stories, participate in ethical and security reviews, and collaborate with stakeholders to align AI solutions with business needs.

Own and build end-to-end matching and ranking systems for out-of-home advertising: design candidate generation, re-ranking, geospatial scoring, data warehouse and feature pipelines, evaluation infrastructure, low-latency ranking APIs, and model monitoring with drift detection and retraining triggers.

Develop end-to-end foundation models that read entire CT studies and generate radiology-style reports. Build vision-language and 3D models, run large-scale distributed training and rigorous experiments, and deliver models that progress through FDA clearance to hospital deployment. Collaborate with clinicians and engineers to ensure safety, reproducibility, and clinical impact.

Support portfolio managers with quantitative analysis, portfolio monitoring, and risk evaluation. Build and maintain analytical tools, analyze large datasets for investment insights, assist with systematic fixed income strategies, performance attribution, and reporting in collaboration with research and technology teams.

Own end-to-end matching and ranking systems for out-of-home advertising: design candidate generation and re-ranking models, build and manage data warehouse and pipelines, implement evaluation and monitoring, publish low-latency ranking APIs, and maintain model observability and retraining workflows.

Lead and build Suno's ML data operations function: source and manage external labeling vendors, partner with researchers to deliver required training data, improve in-product data collection, and establish teams, systems, and processes to scale reliable data sourcing for model development.

Design, build, and scale retrieval-augmented generation (RAG) systems: chunking, embeddings, vector search, LLM orchestration, and production ML pipelines. Work cross-functionally to deploy robust retrieval, indexing, and evaluation solutions over large proprietary datasets, collaborating with MLOps, Product, and Design to productionize and maintain systems.

Lead on-device ML inference deployment and performance optimization for outdoor camera/doorbell workloads. Optimize latency, throughput, memory, power and stability across CPU/DSP/NPU/GPU. Perform kernel/operator-level tuning, runtime integration in C/C++, quantization validation, and build profiling/benchmarking tooling. Drive cross-functional work with ML, firmware, and hardware teams and provide staff-level technical leadership and mentoring.

Lead strategy, development, and production deployment of scalable AI/GenAI and ML solutions. Drive technical direction, mentor data scientists, own end-to-end initiatives, ensure MLOps and model governance, collaborate cross-functionally, and deliver measurable business impact using advanced models (LLMs, transformers, diffusion) and cloud platforms.

The Senior Machine Learning Engineer will develop, deploy, and optimize large language models on healthcare data, collaborate with teams, and ensure compliance.

Work with the machine learning team and portfolio managers to translate investment questions into ML problems, develop and evaluate data-driven models (including simulation and optimization), maintain production models and tools, document methods and results, and stay current with ML/AI techniques relevant to finance. Gradually take ownership of features, components, and end-to-end projects.

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, evaluation, and inference architecture. Collaborate with data and infra teams, drive reproducible model development, and provide technical leadership and mentorship from research through production.