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machine learning engineer

Valence

<div>
 Valence has built the only AI native coaching platform for enterprise, offering personalized, expert, and human-like guidance and support to any leader or employee. We&rsquo;re not just talking about the future of work &mdash; we&rsquo;re building it now, with the most innovative Fortune 500 companies across healthcare, financial services, manufacturing, and technology.
 
 
 Our focus is on the problems that actually decide whether AI changes how organizations operate &mdash; the ones with no playbook, no obvious answers, and no guarantee of success. If you want to be part of the small group that defines how AI transforms the future at a global scale, this is your chance.
 
 
 And this isn&rsquo;t for everyone. We&rsquo;re not looking for people who want predictability or incremental progress. We only want those who are restless at the edge of what&rsquo;s possible, who get bored when things feel &ldquo;done,&rdquo; and who are driven to redefine what AI can mean for leaders, companies, and the world. Because at Valence, the work worth doing is the kind that redefines work itself.
 
 
 
 The Role
 
 
 
 This role is a Machine Learning Engineer role for our conversational AI coaching product designed for Fortune 500 enterprises, reporting into our Head of AI. In this role you will implement, and optimize machine learning models that power our coaching insights and recommendations. This role is focused on the development and optimization of machine learning models and algorithms, optimizing the underlying ML infrastructure and model development. This role will directly impact our product's core and shape the future of AI-driven leadership coaching for Fortune 500 enterprises.
 
 
 
 About Valence
 
 
 
 We're the only company pioneering leadership coaching for large enterprises in an AI-first way. Our mission is to transform how the world's biggest companies approach learning and development, helping teams work better together through AI-powered personalization that adapts to individual goals and organizational culture using the latest advances in machine learning and natural language processing.
 
 
 We've been featured in Harvard Business Review, TIME, World Economic Forum, Financial Times, Forbes and an Inc. 5000 fastest-growing private companies in America. Our clients represent the most diverse and sophisticated enterprise AI implementations globally, including Coca-Cola, Delta, Nestl&eacute;, General Mills, Schneider Electric, Deutsche Telekom, AstraZeneca, Prudential, CVS and Bristol Myers Squibb.
 
 
 Working at Valence means you'll work directly with Fortune 500 technology leaders, building expertise through the most complex enterprise deployments while gaining insight into diverse organizational approaches to AI transformation. These aren't just any enterprise clients - they're the companies defining what AI-first business transformation looks like across every major industry.
 
 
 
 What You'll Do
 
 
 
 <ul>
 <li>
 You will develop scalable data pipelines, optimize models for performance and accuracy, and evaluate them to ensure they are production-ready.
 </li>
 <li>
 Develop, design and implement improvements in user experience in conversational interactions leveraging LLMs in new ways to advance product goals.
 </li>
 <li>
 Work with the product team to analyze user behavior and prioritize evolving requirements.
 </li>
 <li>
 Experiment at a high velocity, conducting statistical analyses, to optimize the end user experience
 </li>
 <li>
 Research and development on new Conversational AI approaches leveraging cutting edge LLM/NLP advancements.
 </li>
 <li>
 Documentation of models, prompts, and processes to increase replicability and drive quality improvements.
 </li>
 <li>
 Stay current with the latest leading research advancements in ML, LLMs, and Conversational AI
 </li>
 <li>
 Support other coding and feature development where required
 
 
 </li>
 </ul>
 
 What We're Looking For
 
 
 
 <ul>
 <li>
 Bachelor's degree in Computer Science, Engineering, Mathematics, related field, or equivalent experience
 </li>
 <li>
 3+ years of professional experience (or equivalent) in software engineering, AI/ML development (ideally including a Master's or Ph.D. in Computer Science, ML, Data Science, or a related field)
 </li>
 <li>
 Practical experience and theoretical knowledge of language technologies such as: dialogue/conversational systems, NLP, and Information Retrieval
 </li>
 <li>
 Strong foundation in data structures, algorithms, and software engineering principles.
 </li>
 <li>
 Proficiency in Python and relevant deep learning frameworks; training (e.g. PyTorch, Tensorflow, JAX, Hugging Face Transformers/Adapters), serving (e.g., Hugging Face TGI//outlines, vLLM)
 </li>
 <li>
 Experience with LLM model development and deployment ideally including experience with model distillation, supervised fine-tuning using RLHF/DPO, and automatic prompt tuning (e.g. DSPy, TextGrad)
 </li>
 <li>
 Experience with cloud deployment of ML systems (e.g., AWS, GCP, Azure) including and open systems (e.g. Docker and Kubernetes) and associated ML services.
 </li>
 <li>
 Strong analytical and problem-solving skills
 </li>
 <li>
 Experience structuring and running data-backed experiments
 </li>
 <li>
 Strong written and verbal communication skills
 </li>
 <li>
 Exposure to early-stage startups, preferably B2B SaaS
 
 
 </li>
 </ul>
 
 What You'll Get
 
 
 
 <ul>
 <li>
 Ownership &amp; Rapid Growth
 </li>
 <li>
 Outsized missions from day one, with direct responsibility for company-defining projects
 </li>
 <li>
 Work alongside the executive team with transparency into strategy and decision-making
 </li>
 <li>
 Influence on direction through real-time customer feedback and market insights
 </li>
 <li>
 AI-First Operator
 </li>
 <li>
 Work directly with cutting-edge AI models and next-generation platforms
 </li>
 <li>
 Build expertise in enterprise AI implementation across Fortune 500 companies and multiple industries
 </li>
 <li>
 Establish yourself as a recognized leader among peers in shaping how AI transforms work at a global scale
 </li>
 <li>
 Compensation
 </li>
 <li>
 Competitive salary including base + bonuses
 </li>
 <li>
 Comprehensive health coverage (medical, dental, vision) from day one
 </li>
 <li>
 Generous PTO, company-wide R&amp;R shutdowns, and paid parental leave
 </li>
 <li>
 Retirement plan support for US and global employees
 </li>
 <li>
 Equity
 </li>
 <li>
 Meaningful ownership in a venture-backed company at a growth inflection point
 </li>
 <li>
 Financial upside that comes from scaling fast
 </li>
 <li>
 Top-up grants as we scale and you deliver exceptional performance &mdash; your compensation grows alongside your impact
 </li>
 <li>
 Top-Performing Culture
 </li>
 <li>
 A culture built for top talent: intensity to win, growth without limits, and a team that solves hard problems and celebrates big wins together
 
 
 </li>
 </ul>
 
 Learn more about us and meet our team
 
 
 here
 
 
 
 
 Location and Work Environment
 
 
 
 If candidates are based in NYC or Toronto they can work hybrid in our offices, otherwise this role can be remote. Candidates must be comfortable working with colleagues in different time zones (UK), and have valid travel documents without work authorization restrictions in the US.
 
 
 
 Diversity and Inclusion
 
 
 
 We are dedicated to creating a diverse and inclusive environment where everyone feels valued and supported. We encourage applications from candidates of all backgrounds and offer accommodations upon request throughout the hiring process. If you have any questions, please reach out to Allison Langille, Head of People, at jobs@valence.co.
 
 
 
 Employment Verification &amp; Commitment
 
 
 
 We use third-party services to verify employment history, education, and other information relevant to your candidacy. Employment is contingent upon the successful completion of these verification checks. This is a full-time role that requires a high level of focus, availability, and commitment. Employees may not hold concurrent full-time employment with another organization while employed at Valence. Any outside consulting, advisory, freelance, or other professional work must be disclosed and approved in advance and must not interfere with job responsibilities, availability, performance, or create a conflict of interest - including risks related to confidentiality, intellectual property, or competition.
 
 
</div>

Machine Learning Engineer

FanDuel

<div>
 
 THE POSITION
 
 
 
 Our roster has an opening with your name on it
 
 
 At FanDuel, data is the heartbeat of our organization. As a Machine Learning Engineer at FanDuel, you will help us unlock the full potential of our vast amounts of real-time and relational data. You will be asked to provide our business with insight and our customers with world-class personalized experiences. Every click our users make, every bet, every touchdown, every fumble, and every play is fair game for us to turn into a stream of knowledge. Your expertise will be used here to make better and faster decisions &ndash; outpacing our competition.
 
 
 Collaboration is at the core of your role. You&rsquo;ll be the linchpin between engineering teams working downstream to build out our online application and upstream to land necessary data for feature engineering. You&rsquo;ll also be working with Data Scientists and Analysts to productionize, analyze, and validate AI powered insights. You will be asked to help organize, model, and present our data as a coherent product and offer it to our stakeholders, providing a common information framework that allows FanDuel to intelligently react to what is happening on the field and in the marketplace.
 
 
 We are looking for Senior Machine Learning Engineer who may be looking to make the move to a big data environment. If this describes you, read on &ndash; we want to hear from you!
 
 
 
 THE GAME PLAN
 
 
 
 Everyone on our team has a part to play
 
 
 <ul>
 <li>
 Designing and implementing intelligent search system incorporating typeahead search, vector search and ML personalization model signals to optimize relevance and user experience
 </li>
 <li>
 Contributing to the design and development of scalable serving systems for ML and GenAI/LLM models
 </li>
 <li>
 Developing platform features and capabilities (e.g. CLI, SDK, Infra Automation, Platform Applications) for streamlining ML Model and GenAI/LLM Application development and deployment lifecycle
 </li>
 <li>
 Business intelligence tools (e.g., Tableau, Knime, Looker)
 </li>
 <li>
 Data security and privacy (e.g. GDPR, CPP)
 </li>
 <li>
 Data governance and data testing frameworks
 </li>
 <li>
 Continuous integration and delivery of production data products
 </li>
 <li>
 An inclusive culture that expects excellence and priorities your growth as an engineer and your well-being as a person
 </li>
 <li>
 Advance your career within well-defined, skill-based tracks, either as an individual contributor or as a manager &ndash; both providing equal opportunities for compensation and advancement
 </li>
 <li>
 Collaborating with peers and sharing best practices in system reliability, automation, and data quality
 
 
 
 </li>
 </ul>
 ML engineering is a rapidly changing field &ndash; most of all, we&rsquo;re looking for someone who enjoys experimenting, keeping their finger on the pulse of current data engineering tools, and always thinking about how to do something better.
 
 
 
 THE STATS
 
 
 
 What we're looking for in our next teammate
 
 
 <ul>
 <li>
 5-7+ years of relevant experience developing code in one or more core programming languages (Python, Java, etc.)
 </li>
 <li>
 1+ Years of Experience implementing vector search, semantic search, or embedding-based retrieval systems for production ML or AI applications
 </li>
 <li>
 1+ Years of Experience working with typeahead / autocomplete systems and integrating ML signals into query understanding or ranking workflows
 </li>
 <li>
 1+ Years of Experience combining outputs from multiple retrieval systems (e.g., vector search + typeahead + personalization models) to improve relevance
 </li>
 <li>
 1+ Years of experience in deploying ML and GenAI/LLM models under the constraints of scalability, correctness, and maintainability.
 </li>
 <li>
 Hands on experience with ML frameworks and libraries (Scikit-learn, Pytorch, Tensorflow, LightGBM, Keras, MLFlow etc.) and familiarity with LLM-specific frameworks (e.g., LangChain, Hugging Face Transformers, etc).
 </li>
 <li>
 Hands on experience with one or more ML and GenAI/LLM cloud services (Amazon SageMaker, Amazon Bedrock, Databricks Mosaic AI, Seldon, Arize, etc)
 </li>
 <li>
 4+ Years of experience designing and building various software architecture, with some emphasis on scalable architectures supporting both traditional ML and advanced LLM workflows.
 </li>
 <li>
 Deep understanding and knowledge of data structures, distributed computing, and software engineering principles
 </li>
 <li>
 3+ Years of experience demonstrating technical leadership working with teams, owning projects, defining, and setting technical direction for projects.
 </li>
 <li>
 Experience with one or more relevant tools (Flink, Spark, Sqoop, Flume, Kafka, Amazon Kinesis, Terraform, Airflow)
 </li>
 <li>
 Ability to share findings in easy to consume formats, whether that is through dashboards or data modeling.
 </li>
 <li>
 Conduct regular design process reviews and ensure development standards within the team.
 </li>
 <li>
 Working with leadership to drive adoption of ML and GenAI/LLM solutions to product engineering teams.
 </li>
 <li>
 Experience working in a cloud environment such as AWS, GCP, Azure.
 </li>
 <li>
 Experience with Databricks is a plus, their unity catalog, another plus.
 </li>
 <li>
 Designing and building data pipelines for production level ML and GenAI/LLM infrastructure.
 </li>
 <li>
 Experience with vector databases to efficiently manage and retrieve embeddings for LLM applications, enabling high-performance similarity search and retrieval-augmented generation (RAG) workflows is a plus
 </li>
 <li>
 Motivate junior engineers on best practices and latest industry design patterns.
 
 
 
 </li>
 </ul>
 
 About Fanduel
 
 
 
 FanDuel Group is the premier mobile gaming company in the United States and Canada. FanDuel Group consists of a portfolio of leading brands across mobile wagering including: America&rsquo;s #1 Sportsbook, FanDuel Sportsbook; its leading iGaming platform, FanDuel Casino; the industry&rsquo;s unquestioned leader in horse racing and advance-deposit wagering, FanDuel Racing; and its daily fantasy sports product.
 
 
 In addition, FanDuel Group operates FanDuel TV, its broadly distributed linear cable television network and FanDuel TV+, its leading direct-to-consumer OTT platform. FanDuel Group has a presence across all 50 states, Canada, and Puerto Rico.
 
 
 The company is based in New York with US offices in Los Angeles, Atlanta, and Jersey City, as well as global offices in Canada and Scotland. The company&rsquo;s affiliates have offices worldwide, including in Ireland, Portugal, Romania, and Australia.
 
 
 FanDuel Group is a subsidiary of Flutter Entertainment, the world's largest sports betting and gaming operator with a portfolio of globally recognized brands and traded on the New York Stock Exchange (NYSE: FLUT).
 
 
 
 Player Benefits
 
 
 
 We treat our team right
 
 
 We offer amazing benefits above and beyond the basics. We have an array of health plans to choose from (some as low as $0 per paycheck) that include programs for fertility and family planning, mental health support, and fitness benefits. We offer generous paid time off (PTO &amp; sick leave), annual bonus and long-term incentive opportunities (based on performance), 401k with up to a 5% match, commuter benefits , pet insurance, and more - check out all our benefits here:
 
 FanDuel Total Rewards
 
 . *Benefits differ across location, role, and level.
 
 
 FanDuel is an equal opportunities employer and we believe, as one of our principles states, &ldquo;We are One Team!&rdquo;. As such, we are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, creed, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, veteran status, or any other characteristic protected by state, local or federal law. We believe FanDuel is strongest and best able to compete if all employees feel valued, respected, and included.
 
 
 
 
 The applicable salary range for this position is $138,000 - $173,000 USD, which is dependent on a variety of factors including relevant experience, location, business needs and market demand. This role may offer the following benefits: medical, vision, and dental insurance; life insurance; disability insurance; a 401(k) matching program; among other employee benefits. This role may also be eligible for short-term or long-term incentive compensation, including, but not limited to, cash bonuses and stock program participation. This role includes paid personal time off and 14 paid company holidays. FanDuel offers paid sick time in accordance with all applicable state and federal laws.
 
 
 
 
 
 
 FanDuel is committed to providing reasonable accommodations for qualified individuals with disabilities. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please email Benefits@fanduel.com.
 
 
 
 
 
 
 It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
 
 
</div>

Senior Machine Learning Engineer

Capital One

<div>Sr. Lead, Machine Learning Engineer (Enterprise Platforms Technology)<div><div><div><div><div><div><div><div><div>
As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You&rsquo;ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You&rsquo;ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.
Enterprise Platforms Technology (EPTech) comprises many of Capital One&rsquo;s most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices.
What you&rsquo;ll do in the role:<ul><li>
The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:</li><li>
Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams.</li><li>
Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation).</li><li>
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.</li><li>
Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.</li><li>
Retrain, maintain, and monitor models in production.</li><li>
Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.</li><li>
Construct optimized data pipelines to feed ML models.</li><li>
Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.</li><li>
Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.</li><li>
Use programming languages like Python, Scala, or Java.</li></ul>
Basic Qualifications:<ul><li>
Bachelor&rsquo;s degree</li><li>
At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)</li><li>
At least 4 years of experience programming with Python, Scala, or Java</li><li>
At least 3 years of experience building, scaling, and optimizing ML systems</li><li>
At least 2 years of experience leading teams developing ML solutions</li></ul>
Preferred Qualifications:<ul><li>
Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field</li><li>
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform</li><li>
4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow</li><li>
3+ years of experience developing performant, resilient, and maintainable code</li><li>
3+ years of experience with data gathering and preparation for ML models</li><li>
3+ years of people management experience</li><li>
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents</li><li>
3+ years of experience building production-ready data pipelines that feed ML models</li><li>
Ability to communicate complex technical concepts clearly to a variety of audiences</li></ul>
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
</div></div></div></div></div></div></div></div></div>The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer 

New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer 
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate&rsquo;s offer letter.
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
This role is expected to accept applications for a minimum of 5 business days.
No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City&rsquo;s Fair Chance Act; Philadelphia&rsquo;s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.
If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com
Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.
Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).</div>

Sr. Lead, Machine Learning Engineer (Enterprise Platforms Technology)

Andiamo

<div>
 Senior Data Engineer
 
 
 
 About The Role
 
 
 
 Design and scale data platforms that power analytics, ML, and product features. You&rsquo;ll build streaming and batch pipelines, enforce data contracts, and ensure trust in critical datasets.
 
 
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Develop ELT/ETL and near-real-time pipelines (Kafka/Flink/Spark) with strong SLAs.
 </li>
 <li>
 Model data (Dimensional/Data Vault), implement governance, lineage, and quality checks.
 </li>
 <li>
 Operate cloud data warehouses (Snowflake/BigQuery/Redshift) and lakehouse patterns (Iceberg/Delta/Hudi).
 </li>
 <li>
 Enable self-service with dbt, catalogs, and metrics layers; partner with DS/BI stakeholders.
 
 
 </li>
 </ul>
 
 Requirements
 
 
 
 <ul>
 <li>
 5+ years in data engineering; strong SQL and Python/Scala.
 </li>
 <li>
 Experience with orchestration (Airflow/Dagster) and CI/CD for data.
 </li>
 <li>
 Focus on reliability, cost efficiency, and documentation.
 
 
 </li>
 </ul>
 
 About Andiamo
 
 
 
 Talent Partners for the AI Revolution. As a globally recognized staffing and consulting firm, we specialize in placing the top 2% of technology and go-to-market professionals with the world&rsquo;s largest and most well-known companies.
 
 
 For over 20 years, we've maintained the status of tier-one vendor for firms such as Palantir, Amazon, Fluidstack, Bloomberg, Relativity Space, Firefly, MasterCard, Visa, Two Sigma, Citadel, as well as other major financial services firms, elite hedge funds, Google-backed tech start-ups, and major software firms.
 
 
 Our talent solutions include Permanent Placement, Contract Staffing, Executive Search, and Dedicated Recruiting Services (RPO). Find out more at www.andiamogo.com
</div>

Machine Learning Engineer, Robot Learning - Robotics Software Firm in High Growth

Fusemachines

<div>
 
 About Fusemachines
 
 
 
 Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients&rsquo; AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government.
 
 
 Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.
 
 
 
 Salary Range:
 
 US$ 140,000-190,000/year
 
 
 
 Role Overview
 
 
 
 We&rsquo;re hiring a mid-to-senior Machine Learning Engineer / Data Scientist to build and deploy machine learning solutions that drive measurable business impact. You&rsquo;ll work across the ML lifecycle&mdash;from problem framing and data exploration to model development, evaluation, deployment, and monitoring&mdash;often in partnership with client stakeholders and internal delivery teams.
 
 
 You should be strong in core data science and applied machine learning, comfortable working with real-world data, and capable of turning modeling work into production-ready systems.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Problem Framing &amp; Stakeholder Partnership
 </li>
 <ul>
 <li>
 Translate business questions into ML problem statements (classification, regression, time series forecasting, clustering, anomaly detection, recommendation, etc.)
 </li>
 <li>
 Collaborate with stakeholders to define success metrics, evaluation plans, and practical constraints (latency, interpretability, cost, data availability)
 </li>
 </ul>
 <li>
 Data Analysis &amp; Feature Engineering
 </li>
 <ul>
 <li>
 Use SQL and Python to extract, join, and analyze data from relational databases and data warehouses
 </li>
 <li>
 Perform data profiling, missingness analysis, leakage checks, and exploratory analysis to guide modeling choices
 </li>
 <li>
 Build robust feature pipelines (aggregation, encoding, scaling, embeddings where appropriate) and document assumptions
 </li>
 </ul>
 <li>
 Model Development (Core ML)
 </li>
 <ul>
 <li>
 Train and tune supervised learning models for tabular data (e.g., logistic/linear models, tree-based methods, gradient boosting such as XGBoost/LightGBM/CatBoost, and neural nets for structured data)
 </li>
 <li>
 Apply strong tabular modeling practices: handling missing data, categorical encoding, leakage prevention, class imbalance strategies, calibration, and robust cross-validation
 </li>
 <li>
 Build time series models (statistical and ML/DL approaches) and validate with proper backtesting
 </li>
 <li>
 Apply clustering and segmentation techniques (k-means, hierarchical, DBSCAN, Gaussian mixtures) and evaluate stability and usefulness
 </li>
 <li>
 Apply statistics in practice (hypothesis testing, confidence intervals, sampling, experiment design) to support inference and decision-making
 </li>
 </ul>
 <li>
 Deep Learning
 </li>
 <ul>
 <li>
 Build and train deep learning models using PyTorch or TensorFlow/Keras
 </li>
 <li>
 Use best practices for training (regularization, calibration, class imbalance handling, reproducibility, sound train/val/test design)
 </li>
 </ul>
 <li>
 Evaluation, Explainability, and Iteration
 </li>
 <ul>
 <li>
 Choose appropriate metrics (AUC/F1/PR, RMSE/MAE/MAPE, calibration, lift, and business KPIs) and create evaluation reports
 </li>
 <li>
 Perform error analysis and interpretation (feature importance/SHAP, cohort slicing) and iterate based on evidence
 </li>
 </ul>
 <li>
 Productionization &amp; MLOps (Project-Dependent)
 </li>
 <ul>
 <li>
 Package models for deployment (batch scoring pipelines or real-time APIs) and collaborate with engineers on integration
 </li>
 <li>
 Implement practical MLOps: versioning, reproducible training, automated evaluation, monitoring for drift/performance, and retraining plans
 </li>
 </ul>
 <li>
 Documentation &amp; Communication
 </li>
 <ul>
 <li>
 Communicate tradeoffs and recommendations clearly to technical and non-technical stakeholders
 </li>
 <li>
 Create documentation and lightweight demos that make results actionable
 
 </li>
 </ul>
 </ul>
 Success in This Role Looks Like
 
 
 <ul>
 <li>
 You deliver models that perform well and move business metrics (revenue lift, cost reduction, risk reduction, improved forecast accuracy, operational efficiency)
 </li>
 <li>
 Your work is reproducible and production-aware: clear data lineage, robust evaluation, and a credible path to deployment/monitoring
 </li>
 <li>
 Stakeholders trust your judgment in selecting methods and communicating uncertainty honestly
 
 
 </li>
 </ul>
 
 Required Qualifications
 
 
 
 <ul>
 <li>
 3&ndash;8 years of experience in data science, machine learning engineering, or applied ML (mid-to-senior)
 </li>
 <li>
 Strong Python skills for data analysis and modeling (pandas/numpy/scikit-learn or equivalent)
 </li>
 <li>
 Strong SQL skills (joins, window functions, aggregation, performance awareness)
 </li>
 <li>
 Solid foundation in statistics (hypothesis testing, uncertainty, bias/variance, sampling) and practical experimentation mindset
 </li>
 <li>
 Hands-on experience across multiple model types, including:
 </li>
 <ul>
 <li>
 Classification &amp; regression
 </li>
 <li>
 Time series forecasting
 </li>
 <li>
 Clustering/segmentation
 </li>
 </ul>
 <li>
 Experience with deep learning in PyTorch or TensorFlow/Keras
 </li>
 <li>
 Strong problem-solving skills: ability to work with ambiguous goals and messy data
 </li>
 <li>
 Clear communication skills and ability to translate analysis into decisions
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Experience with Databricks for applied ML (e.g., Spark, Delta Lake, MLflow, Databricks Jobs/Workflows)
 </li>
 <li>
 Experience deploying models to production (APIs, batch pipelines) and maintaining them over time (monitoring, retraining)
 </li>
 <li>
 Experience with orchestration tools (Airflow, Prefect, Dagster) and modern data stacks (Snowflake/BigQuery/Redshift/Databricks)
 </li>
 <li>
 Experience with cloud platforms (AWS/GCP/Azure/IBM) and containerization (Docker)
 </li>
 <li>
 Experience with responsible AI and governance best practices (privacy/PII handling, auditability, access controls)
 </li>
 <li>
 Consulting or client-facing delivery experience
 
 
 </li>
 </ul>
 
 Certifications (Strong Plus)
 
 
 
 Candidates with at least one relevant certification are especially encouraged to apply:
 
 
 <ul>
 <li>
 Cloud certifications: AWS, Google Cloud, Microsoft Azure, or IBM (data/AI/ML tracks)
 </li>
 <li>
 Databricks certifications (Data Scientist, Data Engineer, or related)
 
 
 </li>
 </ul>
 
 Nice-to-Have
 
 
 
 <ul>
 <li>
 Causal inference experience (e.g., quasi-experimental methods, propensity scores, uplift/heterogeneous treatment effects, experimentation beyond A/B tests)
 </li>
 <li>
 Agentic development experience: designing and evaluating agentic workflows (tool use, planning, memory/state, guardrails) and integrating them into products
 </li>
 <li>
 Deep familiarity with agentic coding tools and workflows for accelerated product development (e.g., AI-assisted IDEs, code agents, automated testing/refactoring, repo-aware assistants), including strong judgment on quality, security, and maintainability
 
 
 </li>
 </ul>
 
 
 Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws.
 
 
 
 
 Important: Immigration Sponsorship Policy
 
 
 Fusemachines is unable to proceed with candidates who require any form of work authorization or immigration support from the company. This restriction applies to all types of support, including:
 
 
 <ul>
 <li>
 Direct Company Sponsorship: Such as H-1B, J-1, or TN visas
 </li>
 <li>
 Employer of Record: Listing Fusemachines as the immigration employer on any government documentation
 </li>
 <li>
 Written Documentation: Providing letters or other support for any work authorization (e.g., OPT, STEM OPT, CPT)
 
 
 </li>
 </ul>
 Powered by JazzHR
 
 
 I0GDC3XJwj
</div>

Data Scientist

Research Engineer, FAIR AI & Society

Bank of America

<div>Job Description:
At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day. 
 
Being a Great Place to Work is core to how we drive Responsible Growth. This includes our commitment to being an inclusive workplace, attracting and developing exceptional talent, supporting our teammates&rsquo; physical, emotional, and financial wellness, recognizing and rewarding performance, and how we make an impact in the communities we serve. 
 
Bank of America is committed to an in-office culture with specific requirements for office-based attendance and which allows for an appropriate level of flexibility for our teammates and businesses based on role-specific considerations. 
 
At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us! 
Job Description: 
This job is responsible for defining and leading the engineering approach for solutions at the program or portfolio level, to deliver significant business outcomes. Key responsibilities include continuously improving the design, quality, and reuse of the solution and delivering technology enablers that improve development efficiencies for the solution. Job expectations include familiarity with at least one area of engineering, acting as a &ldquo;go to&rdquo; reference across the organization, and applying knowledge to improve technical competencies through recruitment and development activities.
Job Description Summary:
Global Markets GenAI Technology is a fast-growing global team with locations in New York City, Charlotte, London, and India. Generative AI presents an exciting opportunity to derive valuable insights from data and drive revenue growth, efficiencies, and improved business processes. Global Markets GenAI Technology will collaborate with Global Markets Sales &amp; Trading, Quantitative Strategies &amp; Data Group (QSDG) &amp; Platform teams to the design and buildout global generative AI platform.
The platform will cater to a rapidly growing number of use cases that harness the power of Generative AI. Both proprietary and open-source Large Language Models, and large structured and un-structured data sets will be leveraged to produce insights for Global Markets and its clients.
We are looking for a Principal Engineer to continue to scale the platform. The Principal Engineer will be a detail-oriented, highly technical, hands-on senior software developer, and a key member of the team developing the Global Markets Generative AI platform. These solutions will be engineered for scalability, performance, reliability, and supportability.
The platform in production with multiple use cases continues to scale with rapidly growing opportunities using Generative AI, built on proprietary and open-source Large Language Models, working with on-premises and Cloud infrastructure, and using structured and unstructured data sets to produce insights for Global Markets and its clients.
Responsibilities:<ul><li>
Develops the engineering approach for the entire program/portfolio solution and works with Architecture, to develop/analyze/deliver the implementation of technical enablers</li><li>
Leads the planning, definition, and design of the complex features which span multiple teams and explore solution alternatives</li><li>
Creates ideas on designing complex technology and solution development approaches</li><li>
Leads the technical oversight for teams in solution development including design reviews and code within own domain</li><li>
Defines the technology tool stack for the solution within ranged of internally approved and supported technologies</li><li>
Explores state-of-the-art technologies to improve development efficiencies, quality of test/QA coverage, and release management</li><li>
Leads and is responsible for the end-to-end test strategy/creation/adherence, and the integration between teams for a program/portfolio solution</li><li>
Global Markets GenAI Technology works across Sales &amp; Trading and the Quantitative teams to buildout our Generative AI platform.</li><li>
Identify, design, and implement integrating applications with scalability and availability requirements, especially with Agents.</li><li>
Develops architectural approaches for Generative AI software and hardware integration with emphasis on designing scale with Agents.</li><li>
Work with stakeholders from both the business and technology organizations to optimize product design.</li></ul>
Required Qualifications:<ul><li>
Strong experience in Machine Learning, Data Analytics, AI.</li><li>
Experience implementing Generative AI solutions highly desirable.</li><li>
Knowledge of Generative AI and machine learning frameworks, products, and tools for agentic solutions.</li><li>
Extensive experience in designing &amp; implementing multi-tier (including microservices) application platforms.</li><li>
Advanced proficiency in Python, including object-oriented techniques, and REST API development.</li><li>
In-depth knowledge of databases, large structured &amp; unstructured dataset processing, and messaging is important.</li><li>
Proven ability to partner effectively with business stakeholders to drive technology strategy and align with organizational goals.</li><li>
A self-starter attitude quickly adapts to new technologies and industry trends.</li><li>
Strong interpersonal and communication skills to work independently and in a collaborative environment.</li></ul>
Desired Qualifications:<ul><li>
Higher education in computer science or relevant previous work experience.</li><li>
10+ years of relevant experience with a degree in engineering.</li></ul>
Skills:<ul><li>
Automation</li><li>
Influence</li><li>
Result Orientation</li><li>
Stakeholder Management</li><li>
Technical Strategy Development</li><li>
Application Development</li><li>
Architecture</li><li>
Business Acumen</li><li>
Risk Management</li><li>
Solution Design</li><li>
Agile Practices</li><li>
Analytical Thinking</li><li>
Collaboration</li><li>
Data Management</li><li>
Solution Delivery Process</li></ul>
Minimum Education Requirements: Bachelor's Degree or Equivalent Professional Experience
Shift:
1st shift (United States of America)
Hours Per Week:
40</div>

Principal Engineer - GenAI Team

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