Top Entry Level Machine Learning Jobs

Develop and deploy machine learning algorithms and models for Atlassian products. Design model and system architectures, run experiments and evaluations, solve product problems with AI/ML, and contribute to Atlassian Intelligence features. Collaborate with product, engineering, and analytics teams while building scalable, production-quality solutions using large datasets, deep learning, and LLM-related applications.

Six-month research co-op developing and evaluating multimodal sensor fusion, DSP, and machine learning algorithms for room environmental sensing. Responsibilities include collecting microphone, IMU, camera, and light-based sensor data; developing models, training datasets, and evaluation infrastructure; and implementing real-time prototypes. The role requires Python, MATLAB, TensorFlow or PyTorch, signal processing, deep learning, acoustic modeling, and real-time processing knowledge.

Develops end-to-end AI features for Atlassian’s developer AI products, including LLM integration, prompt engineering, machine learning algorithms, data preprocessing, feature engineering, experimentation, evaluation, pipeline optimization, and monitoring. Collaborates with data scientists, software engineers, and partner teams to solve complex problems and apply advances in AI and machine learning.

Build production AI/ML software for supercomputers and AI infrastructure, including anomaly detection, forecasting, root cause analysis, automation, and operational intelligence. Develop services with Go, Python, and React; integrate machine learning models, LLM workflows, agentic systems, and analytics. Own the lifecycle from data engineering and model development through deployment, monitoring, and improvement while collaborating with engineering, architecture, product, and customers.

The job description content is not provided. Based on the title, this role would manage machine learning engineering teams and initiatives, including technical direction, project execution, and team development.

Architect and ship large-scale ML and evaluation systems for Atlassian Rovo Chat. Define technical strategy for measuring helpfulness, correctness, reliability, and agentic capabilities; develop datasets, simulations, automated judges, experimentation platforms, and production quality signals. Identify failure modes, implement interventions, and measure customer impact. Lead cross-functional initiatives, establish reusable AI quality standards, and mentor engineers while balancing quality, safety, latency, reliability, and cost.

Research Engineer responsible for identifying weaknesses in voice AI models, defining improvement targets, designing evaluations, curating and generating data, fine-tuning deep learning models, and rapidly shipping production systems. The role emphasizes end-to-end ownership of customer problems, measurable production improvements, practical judgment, and fast experimentation. Candidates should have strong Python and PyTorch skills, production ML experience, and ideally experience with speech technologies, distributed training, publications, open source, or public benchmarks.

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.

Develop data-centric machine learning systems for autonomous driving. Design dataset mixtures, curation, mining, sampling, evaluation, and scaling-law experiments; train foundation models using real and synthetic driving data; investigate failures and improve data quality. Apply self-supervised, imitation, and reinforcement learning to perception and trajectory generation. Collaborate on large-scale multi-GPU training, production deployment, and integration into onboard driving systems.

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.

Architect and co-design machine learning models for custom hardware, accounting for latency, resource, and numerical constraints. Translate model requirements into hardware architecture decisions, collaborate with hardware engineers to implement and verify inference solutions, and deploy systems from proof of concept through production. Evaluate research in neural architecture search, ML systems, quantization, and compiler infrastructure for measurable performance improvements.

Develops post-training recipes for domain-specific AI models, including datasets, training stages, hyperparameters, evaluations, and alignment methods. Designs meaningful evaluations, investigates model behavior, studies scaling with data and compute, and advances techniques such as RLHF, RLAIF, preference modeling, and reward learning. The role requires strong statistical and machine learning foundations, original contributions, and the ability to understand sales and go-to-market user needs.

Build reproducible data and AI workflows using Python, SQL, notebooks, and version control. Prepare and validate structured and unstructured data, support analytics and machine learning, prototype generative AI applications, evaluate models, and contribute to applied R&D. Communicate assumptions, limitations, and findings to technical and consulting stakeholders while developing reusable tools, documentation, and training materials in a collaborative consulting environment.

Develops and deploys machine learning models and AI features for medical simulators, including speech and image recognition. Responsibilities include designing, training, testing, productizing, and integrating models; developing simulator software; defining platform requirements; collaborating with engineering, product management, and other teams; and continuously improving customer-facing products.

Build agentic AI systems that improve autonomous-driving software through automated experimentation, recursive self-improvement, failure mining, data generation, LLM training, and policy optimization. Develop reusable skill libraries, shape onboard versus offline architectures, and collaborate with prediction, planning, simulation, and vehicle teams to move research prototypes into safe production workflows.

Own machine learning problems end to end, from framing ambiguous requirements through production deployment. Build NLP and LLM systems, data pipelines, model-serving infrastructure, evaluation and monitoring systems. Select data, models, metrics and system designs while critically reviewing AI coding agents. The role combines research and engineering, requiring strong ML fundamentals, expert Python, testing discipline and independent ownership of complex problems.

Build and evaluate machine learning systems for an interactive consumer education product. Own AI features end to end, including goal setting, evaluation tooling, production deployment, orchestration, user modeling, personalization, feedback loops, experimentation, and quality, safety, latency, and engagement measurement. Partner with learning specialists and product engineers to deliver high-volume, low-latency language-model functionality.

Develops and validates predictive, machine-learning, and deep-learning models; performs exploratory statistical analysis; processes large datasets using GPU, distributed, and cloud computing; creates visualizations and recommendations; and collaborates cross-functionally to improve products and processes. The role requires Python, TensorFlow, PyTorch, large-dataset management, and eligibility for a Secret clearance as a U.S. citizen.

Develops and validates predictive and deep-learning models through exploratory research and statistical analysis. Designs scalable GPU, distributed, and cloud computing solutions for large datasets; creates visualizations and actionable recommendations; and collaborates cross-functionally to improve products and processes. The role uses machine learning, optimization, simulation, and potentially NLP or Generative AI to deliver business value.

Develops and validates predictive, deep-learning, and optimization models; performs exploratory statistical analysis; processes large datasets using GPU, distributed, and cloud computing; creates visualizations and actionable recommendations; and collaborates cross-functionally to improve products and processes.

Develops and validates predictive and deep-learning models through exploratory research, statistical analysis, and large-scale data processing. Designs GPU, distributed, and cloud computing solutions; identifies patterns in complex datasets; creates visualizations and actionable recommendations; and collaborates cross-functionally to improve products and processes. The role requires Python, TensorFlow, PyTorch, large-dataset experience, U.S. citizenship, and eligibility to obtain a Secret clearance.

Develops, integrates, tests, and deploys AI, machine learning, and autonomy algorithms for mission systems. Translates requirements into software, supports perception, planning, controls, and human-machine interfaces, and troubleshoots deployed capabilities. Leads technical teams and guides AI/ML efforts from research or prototype stages through operational delivery, including technical strategy, integration, testing, risk management, and mentoring. Work may involve time-series, telemetry, sensor, anomaly detection, forecasting, and predictive modeling applications in aerospace or defense systems.

Develop and deliver AI/ML-enabled enterprise, cloud, and application software for national security space programs. Support software design, development, integration, testing, mission management, command and control, and data analytics. Collaborate with engineering teams on mission-focused capabilities using object-oriented programming, AI prompting, model training, evaluation, data preparation, Agile practices, unit testing, and configuration management.

Develop and deliver AI/ML, enterprise, cloud, and application-based software for national security space programs. Support software design, development, integration, testing, data analytics, mission management, and command-and-control capabilities. Collaborate with engineering teams, apply coding standards, perform unit testing, and use configuration management and Agile practices. The role requires familiarity with object-oriented programming, AI/ML concepts, Linux or UNIX, and software development lifecycle activities.

Develop, evaluate, and integrate machine learning models for physical robots. Build data and evaluation workflows, run research experiments, analyze failures, and communicate tradeoffs. Integrate models into robot software, test them on real hardware, and optimize inference for practical latency and compute constraints. Collaborate with robotics and software engineers to turn research into reliable product capabilities.