[All remote jobs](https://jobicy.com/jobs.md)[![Tala logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2022/02/a788b3a65c0fc717303f0b5b049cc7e6.jpeg)](https://jobicy.com/company/tala.md)[Tala](https://jobicy.com/company/tala.md) # Manager, Machine Learning Engineering Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/tala.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 170k–210k / yrDepartment[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentFull TimeExperienceSeniorPublished21 Oct 2026Apply before20 Nov 2026Listing views30Application actions2Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Tala is seeking a player-coach Manager of Machine Learning Engineering to lead its ML Platform team and manage 4–6 engineers. The role owns delivery, technical direction, hiring, performance development, operational health, and roadmap prioritization for production ML infrastructure. Key technical scope includes real-time inference, streaming feature systems, batch model workflows, observability, reliability, and infrastructure automation. The manager will partner closely with data science, data engineering, product, credit, and business teams to deliver scalable machine-learning capabilities. Candidates need substantial backend engineering depth, hands-on Python experience, production ML exposure, and proven engineering people-management experience. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a senior leadership role combining people management with accountability for highly available, production-grade ML platforms. It requires balancing architecture, reliability, hiring, delivery priorities, and cross-functional dependencies in an evolving fintech environment. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$190,000US market range (per year)$180k–$240k0$264k AI insightThe employer discloses an annual base-compensation range of $170,000–$210,000 USD, with a midpoint of $190,000. Estimated US market compensation for a machine learning engineering manager leading ML platform and reliability work is approximately $180,000–$240,000 USD annually; this is an AI estimate, not verified salary research. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning Engineering](https://jobicy.com/jobs?search_keywords=Machine%20Learning%20Engineering.md)[Engineering Management](https://jobicy.com/jobs?search_keywords=Engineering%20Management.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[ML Platform Architecture](https://jobicy.com/jobs?search_keywords=ML%20Platform%20Architecture.md)[Real-Time Inference](https://jobicy.com/jobs?search_keywords=Real-Time%20Inference.md)[Streaming Data Systems](https://jobicy.com/jobs?search_keywords=Streaming%20Data%20Systems.md)[Kafka](https://jobicy.com/jobs?search_keywords=Kafka.md)[Kubernetes](https://jobicy.com/jobs?search_keywords=Kubernetes.md)[AWS](https://jobicy.com/jobs?search_keywords=AWS.md)[MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md)[SLO Management](https://jobicy.com/jobs?search_keywords=SLO%20Management.md)[Incident Response](https://jobicy.com/jobs?search_keywords=Incident%20Response.md)[Data Engineering](https://jobicy.com/jobs?search_keywords=Data%20Engineering.md)[Technical Leadership](https://jobicy.com/jobs?search_keywords=Technical%20Leadership.md) Sample interview questionsHow would you prioritize platform investments between new ML capabilities, technical debt, and production reliability?I would make reliability and security constraints explicit, assess each item by customer and business impact, and reserve capacity for operational work and debt reduction. I would then align quarterly priorities with stakeholders, publish trade-offs, and revisit plans as incident data, delivery risk, or product needs change. Describe how you would design a reliable real-time model-inference platform. I would define latency, availability, throughput, and model-quality SLOs first. The platform would use versioned models and features, resilient APIs, streaming feature pipelines, automated deployment controls, observability, rollback mechanisms, and clear ownership for on-call response. How do you develop mid-level engineers into senior technical leaders? I set clear competency expectations, provide timely feedback, and assign progressively broader ownership with appropriate support. I pair engineers with meaningful design, reliability, and cross-functional leadership opportunities, then use regular career conversations to measure progress and remove blockers. What metrics would you use to assess the health of an ML platform team? I would track service SLO attainment, incident volume and recovery time, on-call burden, deployment lead time, model deployment success rates, platform adoption, engineering throughput, and technical-debt trends. Metrics should be reviewed alongside qualitative feedback so optimization does not create unhealthy incentives. How would you manage disagreement between Data Science and Engineering on a model deployment approach? I would clarify the shared outcome, surface assumptions with measurable requirements, and evaluate options using reliability, latency, security, maintainability, and experimentation needs. If a trade-off remains, I would document the decision, assign accountable owners, and create a path to revisit it using production evidence. Opportunity details ## About this role. About Tala Tala is the AI-native credit infrastructure that connects global capital to the global majority. We combine proprietary risk intelligence with an expanding network of capital and distribution partners to power credit access at scale. To date, Tala has distributed more than $9 billion in capital to more than 14 million customers across Africa, Latin America, and Asia, building the definitive contextual dataset on thin-file borrowers in emerging markets. Tala is now converting that foundation into shared infrastructure that partners, capital providers, and ecosystems can build on. The company has been named to the Fortune Impact 20 list, CNBC’s World’s Top Fintech Companies twice, CNBC Disruptor 50 for five years, and Forbes’ Fintech 50 list for ten years running. Given the global nature of our team, we operate on a remote-first approach with office hubs in Santa Monica, CA (HQ); Nairobi, Kenya; Mexico City, Mexico; Manila, the Philippines; and Bangalore, India. Most Talazens join us because they connect with our mission. If you are energized by the impact you can make at Tala, we’d love to hear from you! ### The Role We’re looking for a Manager, Machine Learning Engineering to lead Tala’s ML Platform team. This person will manage a team of Machine Learning Engineers responsible for building the platforms, frameworks, and infrastructure that enable our Data Science teams to securely train, deploy, monitor, and operate machine learning models at scale. This is a player-coach management role. You’ll be responsible for developing and growing the team while also providing enough technical leadership to guide architecture, engineering practices, reliability, and production systems. The role has a particular focus on real-time machine learning inference and streaming data systems, as well as the platforms that support batch model development and deployment. ### What You’ll Do Lead & Grow the Team * Manage and develop a team of 4–6 Machine Learning Engineers across mid-to-senior levels. * Hire, source, interview, and close strong MLE talent. * Establish clear expectations, provide regular feedback, and create development plans for direct reports. * Coach engineers toward growth and promotion while addressing performance gaps directly and thoughtfully. * Create opportunities for engineers to take on challenging projects and grow their technical leadership. Own Engineering Delivery * Set quarterly goals and ensure the team consistently delivers against them. * Own prioritization across product roadmap work, run-the-business activities, and operational excellence. * Balance team capacity across new development, maintenance, technical debt, and production support. * Improve team productivity by reducing context switching and delegating effectively. * Partner with engineers and technical leads to estimate and scope complex work. Provide Technical Leadership * Guide the development of platforms and frameworks that allow Data Scientists and Analysts to explore data, develop features, and train, test, deploy, and monitor ML models. * Provide technical leadership across model infrastructure, real-time inference, streaming feature extraction, batch processing, and production ML systems. * Drive strong engineering practices around testing, automation, observability, fault tolerance, infrastructure-as-code, and deployment. * Own and improve SLOs, on-call health, capacity planning, reliability, and incident response. * Review technical designs and help drive architectural standards and technical debt reduction. Partner Across the Organization * Work closely with Data Science, Data Engineering, Data Platform, Product, Credit, and Business Development teams. * Translate business and technical needs into scalable ML platform solutions. * Coordinate dependencies and delivery across multiple engineering and data teams. * Help create structure and clarity in an environment where priorities and requirements can evolve. ### What You’ll Need Management Experience * 2+ years of directly managing engineers, including hiring, performance management, coaching, and career development. * Experience managing a team through at least one full performance cycle. * Demonstrated ability to coach engineers toward promotion and address underperformance effectively. * Experience owning team goals, prioritization, estimation, and delivery. * Experience with production on-call, incident response, and capacity planning. * Willingness to be actively involved in sourcing, interviewing, and closing engineering talent. Technical Experience * 6+ years of backend software engineering experience in consumer-scale applications. * At least 3 years of hands-on Python experience. * Experience building and operating machine learning or causal inference systems in production. * Earlier-career experience personally building and deploying ML models or ML infrastructure. * Ability to participate in technical architecture and system-design discussions and provide technical direction without needing to be the primary coder. * Strong understanding of software quality, security, reliability, testing, and production operations. Technical Skills We’re particularly interested in candidates with experience across: * Languages: Python, SQL * Machine Learning: Jupyter, Pandas, Scikit-Learn, XGBoost, TensorFlow, PyTorch, Hugging Face * Cloud & Infrastructure: AWS, GCP, Azure, Kubernetes, Docker * Streaming: Kafka, Kinesis, Beam, Flink, Spark Streaming * Batch Processing: Airflow, Metaflow * Databases: MySQL, PostgreSQL, Cassandra, Snowflake, Druid, and/or similar technologies * APIs: REST, GraphQL, gRPC, Protocol Buffers * Production Engineering: DevOps, SLOs, monitoring/observability, on-call, capacity planning, root-cause analysis * ML/Analytics: Machine learning, causal inference, scalable algorithms ### Compensation ### Additional Information Our vision is to build a new financial ecosystem where everyone can participate on equal footing and access the tools they need to be financially healthy. We strongly believe that inclusion fosters innovation and we’re proud to have a diverse global team that represents a multitude of backgrounds, cultures, and experience. We hire talented people regardless of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. 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