Top Entry Level Machine Learning Jobs

Build and deliver cloud-based AI and machine learning solutions for ServiceNow’s enterprise search platform. Develop high-quality product features using efficient algorithms, collaborate with developers, product managers, and quality engineers, and own software through design, implementation, testing, and deployment. Ensure solutions are reliable, scalable, performant, modular, and well tested while improving intelligent search and enterprise workflow experiences.

Designs, develops, evaluates, and deploys cloud-based machine learning and AI solutions for enterprise services. Responsibilities include training and iterating on supervised, unsupervised, deep learning, NLP, and LLM models; converting research into production systems; partnering with product teams; and owning software from design through monitoring. The role may also involve high-scale ML systems, LLM fine-tuning, retrieval-augmented generation, agentic AI, and MLOps pipelines.

Develop and optimize deep learning algorithms for resource-constrained embedded audio devices. Build software prototypes, deploy models, research efficient techniques such as quantization and pruning, evaluate models, and assess neural-network hardware accelerators. The role focuses on audio ML for headphones, earbuds, speakers, soundbars, and wearables, requiring embedded C/C++, Python, deep learning frameworks, and collaboration across teams.

Build production AI and machine learning software for large-scale computing and AI infrastructure. Responsibilities include developing anomaly detection, forecasting, root cause analysis, LLM workflows, agentic systems, and operational analytics using Go, Python, and React. The role spans data engineering, model development, evaluation, deployment, monitoring, and continuous improvement while collaborating with engineering, architecture, product, and customer teams.

Develop and productize machine learning and digital signal processing algorithms for speech and audio applications, including noise suppression, echo cancellation, beamforming, spatial audio, and enhancement. Research modern deep learning approaches, prototype with Python or MATLAB, and deploy optimized real-time solutions on consumer hardware. Collaborate across research, systems engineering, and product development to deliver robust audio technologies at scale.

Develop and evaluate real-time machine learning algorithms for audio processing applications, including voice pickup and hearing augmentation. Prototype solutions, integrate them into existing platforms, curate internal resources, present findings, and contribute to customer products, patents, and research publications. The role involves collaboration across machine learning, digital signal processing, software engineering, and psychoacoustics.

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.

Develop and deploy machine learning and hybrid models for industrial process optimization. Apply process engineering, advanced control, and real-time optimization expertise to build scalable solutions integrated with industrial data systems, cloud platforms, and enterprise software. Lead model evaluation, governance, monitoring, and continuous improvement while collaborating with product, software, process engineering, UX, data science, and customer-facing teams.

Own machine learning models from experimentation through production. Train, evaluate, deploy, and integrate models for unstructured data, while building data flows and benchmarking tools. Partner with founders and customers on technical and product direction, and manage the full model lifecycle. The role emphasizes OCR, document understanding, vision-language models, computer vision, and large language models in a customer-facing, high-ownership software environment.

Design, build, optimize, and scale machine learning models and systems for quantitative investment research and production. Collaborate with quantitative researchers and engineers on model architecture, distributed training, inference optimization, research tooling, and internal ML libraries. Improve training speed, scalability, reliability, performance, and cost efficiency across high-performance computing environments while translating research needs into robust technical solutions.

Develop and productionize machine learning classifiers and evaluation systems for assessing model behavior. Responsibilities include supervised fine-tuning, distillation, rigorous evaluation, inference optimization, deployment, observability, reliability, and ongoing improvement. The role partners with researchers and software engineers to translate open-ended research needs into measurable ML objectives and scalable production systems, balancing model quality, latency, compute utilization, and cost.

Develop and productionize AI/ML systems, train and integrate models, build ETL pipelines and databases, support operational users, and research emerging AI technologies. The role applies machine learning, NLP, predictive modeling, statistical analysis, and secure software architecture to intelligence-community mission requirements. Candidates collaborate with customers, derive insights from data, automate workflows, and require an active TS/SCI with polygraph clearance and U.S. citizenship.

Develop, train, productionize, and support AI/ML systems for intelligence-community mission applications. Build models, ETL pipelines, databases, human-review processes, and secure scalable software integrations. Research emerging AI technologies, analyze data, support operational users, and collaborate with customers to define solutions. Apply machine learning, NLP, predictive modeling, statistics, and software architecture to automate workflows and address business requirements.

Own machine learning projects from scoping through production, developing reliable multi-agent AI systems. Design agent workflows, safety limits, memory and context management, model routing, evaluation pipelines, benchmarks, metrics, error monitoring, prompt structures, search and indexing systems, and document-processing validation layers. Conduct rigorous ML experiments, test systems end to end, write technical specifications, and communicate findings. The role is full-time and on-site five days per week in New York City.

Build and deploy production machine learning systems for quoting, material selection, planning, and factory decisions. Responsibilities include developing datasets, features, models, evaluation systems, and pipelines; monitoring quality, drift, latency, and business impact; designing safeguards and fallbacks; improving data quality and explainability; and collaborating with software engineers, operators, and domain experts.

Build and deploy scalable AI/ML systems for retrieval, ranking, categorization, triage, and generative AI across healthcare data. Own solutions end-to-end from problem framing and model selection through production deployment and optimization. Collaborate with software engineers, product managers, clinicians, and operations teams to translate complex business problems into explainable, maintainable systems with measurable outcomes.

Participate in the Gradient Development Program, complete technical training, pursue Google Cloud certifications, and collaborate with technical and business leads to translate business requirements into effective AI and machine learning solutions.

Grade midterms, tests, and assignments for the Mathematical Machine Learning course. The role requires a student with at least 30 credits in mathematics and statistics and a strong understanding of the subject. Priority is given to graduate students in the Mathematics and Statistics Department. Work is performed in person during the Winter 2027 semester, with 50 total hours allocated entirely to grading.

Develop and deploy applied AI and machine learning systems for customer-facing agents. Responsibilities include model fine-tuning, reinforcement learning, evaluation design, large-scale text and audio data work, post-training, voice AI, long-horizon optimization, and production deployment. The role translates customer and product challenges into ML problems and carries solutions from experimentation through measurable customer impact.

Design and deploy production machine learning models and conversational AI agents that transform advertiser briefs into optimized media plans. Build evaluation systems to improve model and agent quality, collaborate across science and engineering teams, investigate ambiguous problems, and iterate using performance data and seller feedback. Communicate effectively with technical and non-technical stakeholders while contributing broadly across machine learning domains.

Own the machine-learning signals and decisions governing web crawling and indexing at massive scale. Develop classifiers, rankers, and calibrated models to assess page quality, credibility, freshness, parsing success, duplicate content, and indexing value. Define ground truth for ambiguous problems, validate measurable search improvements, and collaborate across crawling, indexing, and retrieval teams to improve the quality and coverage of search results.

Build data preprocessing and generation pipelines, run model training and fine-tuning workflows, design benchmarking and evaluation suites, and operationalize research models into reproducible production workflows. The role collaborates with researchers, communicates metrics and results, and owns measurable machine learning outcomes from data preparation through delivery.

Develop mathematical models for music preferences, similarity, engagement, personalization, and discovery. Build and deploy scalable real-time recommendation and ranking systems, translate product objectives into metrics, run large-scale experiments and causal analyses, and collaborate with product and research leaders on personalization strategy.

Develop deep learning models using imitation and reinforcement learning to generate and evaluate autonomous vehicle trajectories. Build production machine learning pipelines, metrics, and error-analysis tools, leveraging large-scale infrastructure. Collaborate with Planning, Perception, Simulation, and Systems Engineering teams to improve autonomous driving behavior in complex urban environments.

Build shared machine learning capabilities for AI-native platform products, including document extraction, foundation models, model routing, evaluation, agentic systems, and continuous improvement. Translate urgent product-team needs into reusable platform abstractions, own capabilities through production, optimize accuracy, latency, cost, and reliability, and develop feedback and retraining loops that improve deployed systems across regulated institutional workflows.